add yolo v10 and modify pipeline
This commit is contained in:
王庆刚
@ -1,34 +1,62 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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import contextlib
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import re
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import os
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import shutil
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import subprocess
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import sys
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from pathlib import Path
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from types import SimpleNamespace
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from typing import Dict, List, Union
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import re
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from ultralytics.utils import (ASSETS, DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_PATH, LOGGER, RANK, SETTINGS,
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SETTINGS_YAML, IterableSimpleNamespace, __version__, checks, colorstr, deprecation_warn,
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yaml_load, yaml_print)
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from ultralytics.utils import (
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ASSETS,
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DEFAULT_CFG,
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DEFAULT_CFG_DICT,
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DEFAULT_CFG_PATH,
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LOGGER,
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RANK,
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ROOT,
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RUNS_DIR,
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SETTINGS,
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SETTINGS_YAML,
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TESTS_RUNNING,
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IterableSimpleNamespace,
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__version__,
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checks,
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colorstr,
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deprecation_warn,
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yaml_load,
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yaml_print,
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)
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# Define valid tasks and modes
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MODES = 'train', 'val', 'predict', 'export', 'track', 'benchmark'
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TASKS = 'detect', 'segment', 'classify', 'pose'
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TASK2DATA = {'detect': 'coco8.yaml', 'segment': 'coco8-seg.yaml', 'classify': 'imagenet10', 'pose': 'coco8-pose.yaml'}
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MODES = {"train", "val", "predict", "export", "track", "benchmark"}
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TASKS = {"detect", "segment", "classify", "pose", "obb"}
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TASK2DATA = {
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"detect": "coco8.yaml",
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"segment": "coco8-seg.yaml",
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"classify": "imagenet10",
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"pose": "coco8-pose.yaml",
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"obb": "dota8.yaml",
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}
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TASK2MODEL = {
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'detect': 'yolov8n.pt',
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'segment': 'yolov8n-seg.pt',
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'classify': 'yolov8n-cls.pt',
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'pose': 'yolov8n-pose.pt'}
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"detect": "yolov8n.pt",
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"segment": "yolov8n-seg.pt",
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"classify": "yolov8n-cls.pt",
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"pose": "yolov8n-pose.pt",
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"obb": "yolov8n-obb.pt",
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}
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TASK2METRIC = {
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'detect': 'metrics/mAP50-95(B)',
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'segment': 'metrics/mAP50-95(M)',
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'classify': 'metrics/accuracy_top1',
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'pose': 'metrics/mAP50-95(P)'}
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"detect": "metrics/mAP50-95(B)",
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"segment": "metrics/mAP50-95(M)",
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"classify": "metrics/accuracy_top1",
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"pose": "metrics/mAP50-95(P)",
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"obb": "metrics/mAP50-95(B)",
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}
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CLI_HELP_MSG = \
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f"""
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CLI_HELP_MSG = f"""
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Arguments received: {str(['yolo'] + sys.argv[1:])}. Ultralytics 'yolo' commands use the following syntax:
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yolo TASK MODE ARGS
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@ -42,7 +70,7 @@ CLI_HELP_MSG = \
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yolo train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
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2. Predict a YouTube video using a pretrained segmentation model at image size 320:
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yolo predict model=yolov8n-seg.pt source='https://youtu.be/Zgi9g1ksQHc' imgsz=320
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yolo predict model=yolov8n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320
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3. Val a pretrained detection model at batch-size 1 and image size 640:
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yolo val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=640
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@ -50,6 +78,9 @@ CLI_HELP_MSG = \
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4. Export a YOLOv8n classification model to ONNX format at image size 224 by 128 (no TASK required)
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yolo export model=yolov8n-cls.pt format=onnx imgsz=224,128
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6. Explore your datasets using semantic search and SQL with a simple GUI powered by Ultralytics Explorer API
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yolo explorer
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5. Run special commands:
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yolo help
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yolo checks
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@ -64,16 +95,84 @@ CLI_HELP_MSG = \
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"""
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# Define keys for arg type checks
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CFG_FLOAT_KEYS = 'warmup_epochs', 'box', 'cls', 'dfl', 'degrees', 'shear'
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CFG_FRACTION_KEYS = ('dropout', 'iou', 'lr0', 'lrf', 'momentum', 'weight_decay', 'warmup_momentum', 'warmup_bias_lr',
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'label_smoothing', 'hsv_h', 'hsv_s', 'hsv_v', 'translate', 'scale', 'perspective', 'flipud',
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'fliplr', 'mosaic', 'mixup', 'copy_paste', 'conf', 'iou', 'fraction') # fraction floats 0.0 - 1.0
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CFG_INT_KEYS = ('epochs', 'patience', 'batch', 'workers', 'seed', 'close_mosaic', 'mask_ratio', 'max_det', 'vid_stride',
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'line_width', 'workspace', 'nbs', 'save_period')
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CFG_BOOL_KEYS = ('save', 'exist_ok', 'verbose', 'deterministic', 'single_cls', 'rect', 'cos_lr', 'overlap_mask', 'val',
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'save_json', 'save_hybrid', 'half', 'dnn', 'plots', 'show', 'save_txt', 'save_conf', 'save_crop',
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'show_labels', 'show_conf', 'visualize', 'augment', 'agnostic_nms', 'retina_masks', 'boxes', 'keras',
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'optimize', 'int8', 'dynamic', 'simplify', 'nms', 'profile')
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CFG_FLOAT_KEYS = {"warmup_epochs", "box", "cls", "dfl", "degrees", "shear", "time"}
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CFG_FRACTION_KEYS = {
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"dropout",
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"iou",
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"lr0",
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"lrf",
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"momentum",
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"weight_decay",
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"warmup_momentum",
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"warmup_bias_lr",
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"label_smoothing",
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"hsv_h",
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"hsv_s",
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"hsv_v",
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"translate",
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"scale",
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"perspective",
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"flipud",
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"fliplr",
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"bgr",
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"mosaic",
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"mixup",
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"copy_paste",
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"conf",
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"iou",
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"fraction",
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} # fraction floats 0.0 - 1.0
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CFG_INT_KEYS = {
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"epochs",
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"patience",
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"batch",
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"workers",
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"seed",
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"close_mosaic",
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"mask_ratio",
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"max_det",
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"vid_stride",
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"line_width",
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"workspace",
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"nbs",
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"save_period",
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}
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CFG_BOOL_KEYS = {
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"save",
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"exist_ok",
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"verbose",
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"deterministic",
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"single_cls",
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"rect",
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"cos_lr",
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"overlap_mask",
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"val",
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"save_json",
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"save_hybrid",
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"half",
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"dnn",
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"plots",
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"show",
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"save_txt",
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"save_conf",
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"save_crop",
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"save_frames",
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"show_labels",
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"show_conf",
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"visualize",
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"augment",
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"agnostic_nms",
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"retina_masks",
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"show_boxes",
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"keras",
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"optimize",
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"int8",
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"dynamic",
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"simplify",
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"nms",
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"profile",
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"multi_scale",
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}
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def cfg2dict(cfg):
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@ -109,53 +208,72 @@ def get_cfg(cfg: Union[str, Path, Dict, SimpleNamespace] = DEFAULT_CFG_DICT, ove
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# Merge overrides
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if overrides:
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overrides = cfg2dict(overrides)
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if 'save_dir' not in cfg:
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overrides.pop('save_dir', None) # special override keys to ignore
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if "save_dir" not in cfg:
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overrides.pop("save_dir", None) # special override keys to ignore
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check_dict_alignment(cfg, overrides)
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cfg = {**cfg, **overrides} # merge cfg and overrides dicts (prefer overrides)
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# Special handling for numeric project/name
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for k in 'project', 'name':
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for k in "project", "name":
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if k in cfg and isinstance(cfg[k], (int, float)):
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cfg[k] = str(cfg[k])
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if cfg.get('name') == 'model': # assign model to 'name' arg
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cfg['name'] = cfg.get('model', '').split('.')[0]
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if cfg.get("name") == "model": # assign model to 'name' arg
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cfg["name"] = cfg.get("model", "").split(".")[0]
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LOGGER.warning(f"WARNING ⚠️ 'name=model' automatically updated to 'name={cfg['name']}'.")
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# Type and Value checks
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for k, v in cfg.items():
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if v is not None: # None values may be from optional args
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if k in CFG_FLOAT_KEYS and not isinstance(v, (int, float)):
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raise TypeError(f"'{k}={v}' is of invalid type {type(v).__name__}. "
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f"Valid '{k}' types are int (i.e. '{k}=0') or float (i.e. '{k}=0.5')")
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elif k in CFG_FRACTION_KEYS:
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if not isinstance(v, (int, float)):
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raise TypeError(f"'{k}={v}' is of invalid type {type(v).__name__}. "
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f"Valid '{k}' types are int (i.e. '{k}=0') or float (i.e. '{k}=0.5')")
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if not (0.0 <= v <= 1.0):
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raise ValueError(f"'{k}={v}' is an invalid value. "
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f"Valid '{k}' values are between 0.0 and 1.0.")
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elif k in CFG_INT_KEYS and not isinstance(v, int):
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raise TypeError(f"'{k}={v}' is of invalid type {type(v).__name__}. "
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f"'{k}' must be an int (i.e. '{k}=8')")
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elif k in CFG_BOOL_KEYS and not isinstance(v, bool):
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raise TypeError(f"'{k}={v}' is of invalid type {type(v).__name__}. "
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f"'{k}' must be a bool (i.e. '{k}=True' or '{k}=False')")
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check_cfg(cfg)
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# Return instance
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return IterableSimpleNamespace(**cfg)
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def check_cfg(cfg, hard=True):
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"""Check Ultralytics configuration argument types and values."""
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for k, v in cfg.items():
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if v is not None: # None values may be from optional args
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if k in CFG_FLOAT_KEYS and not isinstance(v, (int, float)):
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if hard:
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raise TypeError(
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f"'{k}={v}' is of invalid type {type(v).__name__}. "
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f"Valid '{k}' types are int (i.e. '{k}=0') or float (i.e. '{k}=0.5')"
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)
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cfg[k] = float(v)
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elif k in CFG_FRACTION_KEYS:
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if not isinstance(v, (int, float)):
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if hard:
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raise TypeError(
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f"'{k}={v}' is of invalid type {type(v).__name__}. "
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f"Valid '{k}' types are int (i.e. '{k}=0') or float (i.e. '{k}=0.5')"
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)
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cfg[k] = v = float(v)
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if not (0.0 <= v <= 1.0):
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raise ValueError(f"'{k}={v}' is an invalid value. " f"Valid '{k}' values are between 0.0 and 1.0.")
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elif k in CFG_INT_KEYS and not isinstance(v, int):
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if hard:
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raise TypeError(
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f"'{k}={v}' is of invalid type {type(v).__name__}. " f"'{k}' must be an int (i.e. '{k}=8')"
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)
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cfg[k] = int(v)
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elif k in CFG_BOOL_KEYS and not isinstance(v, bool):
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if hard:
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raise TypeError(
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f"'{k}={v}' is of invalid type {type(v).__name__}. "
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f"'{k}' must be a bool (i.e. '{k}=True' or '{k}=False')"
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)
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cfg[k] = bool(v)
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def get_save_dir(args, name=None):
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"""Return save_dir as created from train/val/predict arguments."""
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if getattr(args, 'save_dir', None):
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if getattr(args, "save_dir", None):
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save_dir = args.save_dir
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else:
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from ultralytics.utils.files import increment_path
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project = args.project or Path(SETTINGS['runs_dir']) / args.task
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name = name or args.name or f'{args.mode}'
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project = args.project or (ROOT.parent / "tests/tmp/runs" if TESTS_RUNNING else RUNS_DIR) / args.task
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name = name or args.name or f"{args.mode}"
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save_dir = increment_path(Path(project) / name, exist_ok=args.exist_ok if RANK in (-1, 0) else True)
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return Path(save_dir)
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@ -165,23 +283,26 @@ def _handle_deprecation(custom):
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"""Hardcoded function to handle deprecated config keys."""
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for key in custom.copy().keys():
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if key == 'hide_labels':
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deprecation_warn(key, 'show_labels')
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custom['show_labels'] = custom.pop('hide_labels') == 'False'
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if key == 'hide_conf':
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deprecation_warn(key, 'show_conf')
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custom['show_conf'] = custom.pop('hide_conf') == 'False'
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if key == 'line_thickness':
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deprecation_warn(key, 'line_width')
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custom['line_width'] = custom.pop('line_thickness')
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if key == "boxes":
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deprecation_warn(key, "show_boxes")
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custom["show_boxes"] = custom.pop("boxes")
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if key == "hide_labels":
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deprecation_warn(key, "show_labels")
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custom["show_labels"] = custom.pop("hide_labels") == "False"
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if key == "hide_conf":
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deprecation_warn(key, "show_conf")
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custom["show_conf"] = custom.pop("hide_conf") == "False"
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if key == "line_thickness":
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deprecation_warn(key, "line_width")
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custom["line_width"] = custom.pop("line_thickness")
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return custom
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def check_dict_alignment(base: Dict, custom: Dict, e=None):
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"""
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This function checks for any mismatched keys between a custom configuration list and a base configuration list.
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If any mismatched keys are found, the function prints out similar keys from the base list and exits the program.
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This function checks for any mismatched keys between a custom configuration list and a base configuration list. If
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any mismatched keys are found, the function prints out similar keys from the base list and exits the program.
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Args:
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custom (dict): a dictionary of custom configuration options
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@ -194,36 +315,35 @@ def check_dict_alignment(base: Dict, custom: Dict, e=None):
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if mismatched:
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from difflib import get_close_matches
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string = ''
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string = ""
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for x in mismatched:
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matches = get_close_matches(x, base_keys) # key list
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matches = [f'{k}={base[k]}' if base.get(k) is not None else k for k in matches]
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match_str = f'Similar arguments are i.e. {matches}.' if matches else ''
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matches = [f"{k}={base[k]}" if base.get(k) is not None else k for k in matches]
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match_str = f"Similar arguments are i.e. {matches}." if matches else ""
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string += f"'{colorstr('red', 'bold', x)}' is not a valid YOLO argument. {match_str}\n"
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raise SyntaxError(string + CLI_HELP_MSG) from e
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def merge_equals_args(args: List[str]) -> List[str]:
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"""
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Merges arguments around isolated '=' args in a list of strings.
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The function considers cases where the first argument ends with '=' or the second starts with '=',
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as well as when the middle one is an equals sign.
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Merges arguments around isolated '=' args in a list of strings. The function considers cases where the first
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argument ends with '=' or the second starts with '=', as well as when the middle one is an equals sign.
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Args:
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args (List[str]): A list of strings where each element is an argument.
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Returns:
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List[str]: A list of strings where the arguments around isolated '=' are merged.
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(List[str]): A list of strings where the arguments around isolated '=' are merged.
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"""
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new_args = []
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for i, arg in enumerate(args):
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if arg == '=' and 0 < i < len(args) - 1: # merge ['arg', '=', 'val']
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new_args[-1] += f'={args[i + 1]}'
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if arg == "=" and 0 < i < len(args) - 1: # merge ['arg', '=', 'val']
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new_args[-1] += f"={args[i + 1]}"
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del args[i + 1]
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elif arg.endswith('=') and i < len(args) - 1 and '=' not in args[i + 1]: # merge ['arg=', 'val']
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new_args.append(f'{arg}{args[i + 1]}')
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elif arg.endswith("=") and i < len(args) - 1 and "=" not in args[i + 1]: # merge ['arg=', 'val']
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new_args.append(f"{arg}{args[i + 1]}")
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del args[i + 1]
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elif arg.startswith('=') and i > 0: # merge ['arg', '=val']
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elif arg.startswith("=") and i > 0: # merge ['arg', '=val']
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new_args[-1] += arg
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else:
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new_args.append(arg)
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@ -247,11 +367,11 @@ def handle_yolo_hub(args: List[str]) -> None:
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"""
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from ultralytics import hub
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if args[0] == 'login':
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key = args[1] if len(args) > 1 else ''
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if args[0] == "login":
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key = args[1] if len(args) > 1 else ""
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# Log in to Ultralytics HUB using the provided API key
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hub.login(key)
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elif args[0] == 'logout':
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elif args[0] == "logout":
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# Log out from Ultralytics HUB
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hub.logout()
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@ -271,39 +391,47 @@ def handle_yolo_settings(args: List[str]) -> None:
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python my_script.py yolo settings reset
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```
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"""
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url = 'https://docs.ultralytics.com/quickstart/#ultralytics-settings' # help URL
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url = "https://docs.ultralytics.com/quickstart/#ultralytics-settings" # help URL
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try:
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if any(args):
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if args[0] == 'reset':
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if args[0] == "reset":
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SETTINGS_YAML.unlink() # delete the settings file
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||||
SETTINGS.reset() # create new settings
|
||||
LOGGER.info('Settings reset successfully') # inform the user that settings have been reset
|
||||
LOGGER.info("Settings reset successfully") # inform the user that settings have been reset
|
||||
else: # save a new setting
|
||||
new = dict(parse_key_value_pair(a) for a in args)
|
||||
check_dict_alignment(SETTINGS, new)
|
||||
SETTINGS.update(new)
|
||||
|
||||
LOGGER.info(f'💡 Learn about settings at {url}')
|
||||
LOGGER.info(f"💡 Learn about settings at {url}")
|
||||
yaml_print(SETTINGS_YAML) # print the current settings
|
||||
except Exception as e:
|
||||
LOGGER.warning(f"WARNING ⚠️ settings error: '{e}'. Please see {url} for help.")
|
||||
|
||||
|
||||
def handle_explorer():
|
||||
"""Open the Ultralytics Explorer GUI."""
|
||||
checks.check_requirements("streamlit")
|
||||
LOGGER.info("💡 Loading Explorer dashboard...")
|
||||
subprocess.run(["streamlit", "run", ROOT / "data/explorer/gui/dash.py", "--server.maxMessageSize", "2048"])
|
||||
|
||||
|
||||
def parse_key_value_pair(pair):
|
||||
"""Parse one 'key=value' pair and return key and value."""
|
||||
re.sub(r' *= *', '=', pair) # remove spaces around equals sign
|
||||
k, v = pair.split('=', 1) # split on first '=' sign
|
||||
k, v = pair.split("=", 1) # split on first '=' sign
|
||||
k, v = k.strip(), v.strip() # remove spaces
|
||||
assert v, f"missing '{k}' value"
|
||||
return k, smart_value(v)
|
||||
|
||||
|
||||
def smart_value(v):
|
||||
"""Convert a string to an underlying type such as int, float, bool, etc."""
|
||||
if v.lower() == 'none':
|
||||
v_lower = v.lower()
|
||||
if v_lower == "none":
|
||||
return None
|
||||
elif v.lower() == 'true':
|
||||
elif v_lower == "true":
|
||||
return True
|
||||
elif v.lower() == 'false':
|
||||
elif v_lower == "false":
|
||||
return False
|
||||
else:
|
||||
with contextlib.suppress(Exception):
|
||||
@ -311,7 +439,7 @@ def smart_value(v):
|
||||
return v
|
||||
|
||||
|
||||
def entrypoint(debug=''):
|
||||
def entrypoint(debug=""):
|
||||
"""
|
||||
This function is the ultralytics package entrypoint, it's responsible for parsing the command line arguments passed
|
||||
to the package.
|
||||
@ -326,135 +454,160 @@ def entrypoint(debug=''):
|
||||
It uses the package's default cfg and initializes it using the passed overrides.
|
||||
Then it calls the CLI function with the composed cfg
|
||||
"""
|
||||
args = (debug.split(' ') if debug else sys.argv)[1:]
|
||||
args = (debug.split(" ") if debug else sys.argv)[1:]
|
||||
if not args: # no arguments passed
|
||||
LOGGER.info(CLI_HELP_MSG)
|
||||
return
|
||||
|
||||
special = {
|
||||
'help': lambda: LOGGER.info(CLI_HELP_MSG),
|
||||
'checks': checks.check_yolo,
|
||||
'version': lambda: LOGGER.info(__version__),
|
||||
'settings': lambda: handle_yolo_settings(args[1:]),
|
||||
'cfg': lambda: yaml_print(DEFAULT_CFG_PATH),
|
||||
'hub': lambda: handle_yolo_hub(args[1:]),
|
||||
'login': lambda: handle_yolo_hub(args),
|
||||
'copy-cfg': copy_default_cfg}
|
||||
"help": lambda: LOGGER.info(CLI_HELP_MSG),
|
||||
"checks": checks.collect_system_info,
|
||||
"version": lambda: LOGGER.info(__version__),
|
||||
"settings": lambda: handle_yolo_settings(args[1:]),
|
||||
"cfg": lambda: yaml_print(DEFAULT_CFG_PATH),
|
||||
"hub": lambda: handle_yolo_hub(args[1:]),
|
||||
"login": lambda: handle_yolo_hub(args),
|
||||
"copy-cfg": copy_default_cfg,
|
||||
"explorer": lambda: handle_explorer(),
|
||||
}
|
||||
full_args_dict = {**DEFAULT_CFG_DICT, **{k: None for k in TASKS}, **{k: None for k in MODES}, **special}
|
||||
|
||||
# Define common mis-uses of special commands, i.e. -h, -help, --help
|
||||
# Define common misuses of special commands, i.e. -h, -help, --help
|
||||
special.update({k[0]: v for k, v in special.items()}) # singular
|
||||
special.update({k[:-1]: v for k, v in special.items() if len(k) > 1 and k.endswith('s')}) # singular
|
||||
special = {**special, **{f'-{k}': v for k, v in special.items()}, **{f'--{k}': v for k, v in special.items()}}
|
||||
special.update({k[:-1]: v for k, v in special.items() if len(k) > 1 and k.endswith("s")}) # singular
|
||||
special = {**special, **{f"-{k}": v for k, v in special.items()}, **{f"--{k}": v for k, v in special.items()}}
|
||||
|
||||
overrides = {} # basic overrides, i.e. imgsz=320
|
||||
for a in merge_equals_args(args): # merge spaces around '=' sign
|
||||
if a.startswith('--'):
|
||||
LOGGER.warning(f"WARNING ⚠️ '{a}' does not require leading dashes '--', updating to '{a[2:]}'.")
|
||||
if a.startswith("--"):
|
||||
LOGGER.warning(f"WARNING ⚠️ argument '{a}' does not require leading dashes '--', updating to '{a[2:]}'.")
|
||||
a = a[2:]
|
||||
if a.endswith(','):
|
||||
LOGGER.warning(f"WARNING ⚠️ '{a}' does not require trailing comma ',', updating to '{a[:-1]}'.")
|
||||
if a.endswith(","):
|
||||
LOGGER.warning(f"WARNING ⚠️ argument '{a}' does not require trailing comma ',', updating to '{a[:-1]}'.")
|
||||
a = a[:-1]
|
||||
if '=' in a:
|
||||
if "=" in a:
|
||||
try:
|
||||
k, v = parse_key_value_pair(a)
|
||||
if k == 'cfg': # custom.yaml passed
|
||||
LOGGER.info(f'Overriding {DEFAULT_CFG_PATH} with {v}')
|
||||
overrides = {k: val for k, val in yaml_load(checks.check_yaml(v)).items() if k != 'cfg'}
|
||||
if k == "cfg" and v is not None: # custom.yaml passed
|
||||
LOGGER.info(f"Overriding {DEFAULT_CFG_PATH} with {v}")
|
||||
overrides = {k: val for k, val in yaml_load(checks.check_yaml(v)).items() if k != "cfg"}
|
||||
else:
|
||||
overrides[k] = v
|
||||
except (NameError, SyntaxError, ValueError, AssertionError) as e:
|
||||
check_dict_alignment(full_args_dict, {a: ''}, e)
|
||||
check_dict_alignment(full_args_dict, {a: ""}, e)
|
||||
|
||||
elif a in TASKS:
|
||||
overrides['task'] = a
|
||||
overrides["task"] = a
|
||||
elif a in MODES:
|
||||
overrides['mode'] = a
|
||||
overrides["mode"] = a
|
||||
elif a.lower() in special:
|
||||
special[a.lower()]()
|
||||
return
|
||||
elif a in DEFAULT_CFG_DICT and isinstance(DEFAULT_CFG_DICT[a], bool):
|
||||
overrides[a] = True # auto-True for default bool args, i.e. 'yolo show' sets show=True
|
||||
elif a in DEFAULT_CFG_DICT:
|
||||
raise SyntaxError(f"'{colorstr('red', 'bold', a)}' is a valid YOLO argument but is missing an '=' sign "
|
||||
f"to set its value, i.e. try '{a}={DEFAULT_CFG_DICT[a]}'\n{CLI_HELP_MSG}")
|
||||
raise SyntaxError(
|
||||
f"'{colorstr('red', 'bold', a)}' is a valid YOLO argument but is missing an '=' sign "
|
||||
f"to set its value, i.e. try '{a}={DEFAULT_CFG_DICT[a]}'\n{CLI_HELP_MSG}"
|
||||
)
|
||||
else:
|
||||
check_dict_alignment(full_args_dict, {a: ''})
|
||||
check_dict_alignment(full_args_dict, {a: ""})
|
||||
|
||||
# Check keys
|
||||
check_dict_alignment(full_args_dict, overrides)
|
||||
|
||||
# Mode
|
||||
mode = overrides.get('mode')
|
||||
mode = overrides.get("mode")
|
||||
if mode is None:
|
||||
mode = DEFAULT_CFG.mode or 'predict'
|
||||
LOGGER.warning(f"WARNING ⚠️ 'mode' is missing. Valid modes are {MODES}. Using default 'mode={mode}'.")
|
||||
mode = DEFAULT_CFG.mode or "predict"
|
||||
LOGGER.warning(f"WARNING ⚠️ 'mode' argument is missing. Valid modes are {MODES}. Using default 'mode={mode}'.")
|
||||
elif mode not in MODES:
|
||||
raise ValueError(f"Invalid 'mode={mode}'. Valid modes are {MODES}.\n{CLI_HELP_MSG}")
|
||||
|
||||
# Task
|
||||
task = overrides.pop('task', None)
|
||||
task = overrides.pop("task", None)
|
||||
if task:
|
||||
if task not in TASKS:
|
||||
raise ValueError(f"Invalid 'task={task}'. Valid tasks are {TASKS}.\n{CLI_HELP_MSG}")
|
||||
if 'model' not in overrides:
|
||||
overrides['model'] = TASK2MODEL[task]
|
||||
if "model" not in overrides:
|
||||
overrides["model"] = TASK2MODEL[task]
|
||||
|
||||
# Model
|
||||
model = overrides.pop('model', DEFAULT_CFG.model)
|
||||
model = overrides.pop("model", DEFAULT_CFG.model)
|
||||
if model is None:
|
||||
model = 'yolov8n.pt'
|
||||
LOGGER.warning(f"WARNING ⚠️ 'model' is missing. Using default 'model={model}'.")
|
||||
overrides['model'] = model
|
||||
if 'rtdetr' in model.lower(): # guess architecture
|
||||
model = "yolov8n.pt"
|
||||
LOGGER.warning(f"WARNING ⚠️ 'model' argument is missing. Using default 'model={model}'.")
|
||||
overrides["model"] = model
|
||||
# stem = Path(model).stem.lower()
|
||||
stem = model.lower()
|
||||
if "rtdetr" in stem: # guess architecture
|
||||
from ultralytics import RTDETR
|
||||
|
||||
model = RTDETR(model) # no task argument
|
||||
elif 'fastsam' in model.lower():
|
||||
elif "fastsam" in stem:
|
||||
from ultralytics import FastSAM
|
||||
|
||||
model = FastSAM(model)
|
||||
elif 'sam' in model.lower():
|
||||
elif "sam" in stem:
|
||||
from ultralytics import SAM
|
||||
|
||||
model = SAM(model)
|
||||
else:
|
||||
elif re.search("v3|v5|v6|v8|v9", stem):
|
||||
from ultralytics import YOLO
|
||||
|
||||
model = YOLO(model, task=task)
|
||||
if isinstance(overrides.get('pretrained'), str):
|
||||
model.load(overrides['pretrained'])
|
||||
else:
|
||||
from ultralytics import YOLOv10
|
||||
|
||||
# Special case for the HuggingFace Hub
|
||||
split_path = model.split('/')
|
||||
if len(split_path) == 2 and (not os.path.exists(model)):
|
||||
model = YOLOv10.from_pretrained(model)
|
||||
else:
|
||||
model = YOLOv10(model)
|
||||
if isinstance(overrides.get("pretrained"), str):
|
||||
model.load(overrides["pretrained"])
|
||||
|
||||
# Task Update
|
||||
if task != model.task:
|
||||
if task:
|
||||
LOGGER.warning(f"WARNING ⚠️ conflicting 'task={task}' passed with 'task={model.task}' model. "
|
||||
f"Ignoring 'task={task}' and updating to 'task={model.task}' to match model.")
|
||||
LOGGER.warning(
|
||||
f"WARNING ⚠️ conflicting 'task={task}' passed with 'task={model.task}' model. "
|
||||
f"Ignoring 'task={task}' and updating to 'task={model.task}' to match model."
|
||||
)
|
||||
task = model.task
|
||||
|
||||
# Mode
|
||||
if mode in ('predict', 'track') and 'source' not in overrides:
|
||||
overrides['source'] = DEFAULT_CFG.source or ASSETS
|
||||
LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using default 'source={overrides['source']}'.")
|
||||
elif mode in ('train', 'val'):
|
||||
if 'data' not in overrides and 'resume' not in overrides:
|
||||
overrides['data'] = TASK2DATA.get(task or DEFAULT_CFG.task, DEFAULT_CFG.data)
|
||||
LOGGER.warning(f"WARNING ⚠️ 'data' is missing. Using default 'data={overrides['data']}'.")
|
||||
elif mode == 'export':
|
||||
if 'format' not in overrides:
|
||||
overrides['format'] = DEFAULT_CFG.format or 'torchscript'
|
||||
LOGGER.warning(f"WARNING ⚠️ 'format' is missing. Using default 'format={overrides['format']}'.")
|
||||
if mode in ("predict", "track") and "source" not in overrides:
|
||||
overrides["source"] = DEFAULT_CFG.source or ASSETS
|
||||
LOGGER.warning(f"WARNING ⚠️ 'source' argument is missing. Using default 'source={overrides['source']}'.")
|
||||
elif mode in ("train", "val"):
|
||||
if "data" not in overrides and "resume" not in overrides:
|
||||
overrides["data"] = DEFAULT_CFG.data or TASK2DATA.get(task or DEFAULT_CFG.task, DEFAULT_CFG.data)
|
||||
LOGGER.warning(f"WARNING ⚠️ 'data' argument is missing. Using default 'data={overrides['data']}'.")
|
||||
elif mode == "export":
|
||||
if "format" not in overrides:
|
||||
overrides["format"] = DEFAULT_CFG.format or "torchscript"
|
||||
LOGGER.warning(f"WARNING ⚠️ 'format' argument is missing. Using default 'format={overrides['format']}'.")
|
||||
|
||||
# Run command in python
|
||||
# getattr(model, mode)(**vars(get_cfg(overrides=overrides))) # default args using default.yaml
|
||||
getattr(model, mode)(**overrides) # default args from model
|
||||
|
||||
# Show help
|
||||
LOGGER.info(f"💡 Learn more at https://docs.ultralytics.com/modes/{mode}")
|
||||
|
||||
|
||||
# Special modes --------------------------------------------------------------------------------------------------------
|
||||
def copy_default_cfg():
|
||||
"""Copy and create a new default configuration file with '_copy' appended to its name."""
|
||||
new_file = Path.cwd() / DEFAULT_CFG_PATH.name.replace('.yaml', '_copy.yaml')
|
||||
new_file = Path.cwd() / DEFAULT_CFG_PATH.name.replace(".yaml", "_copy.yaml")
|
||||
shutil.copy2(DEFAULT_CFG_PATH, new_file)
|
||||
LOGGER.info(f'{DEFAULT_CFG_PATH} copied to {new_file}\n'
|
||||
f"Example YOLO command with this new custom cfg:\n yolo cfg='{new_file}' imgsz=320 batch=8")
|
||||
LOGGER.info(
|
||||
f"{DEFAULT_CFG_PATH} copied to {new_file}\n"
|
||||
f"Example YOLO command with this new custom cfg:\n yolo cfg='{new_file}' imgsz=320 batch=8"
|
||||
)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
# Example: entrypoint(debug='yolo predict model=yolov8n.pt')
|
||||
entrypoint(debug='')
|
||||
entrypoint(debug="")
|
||||
|
||||
Binary file not shown.
Binary file not shown.
@ -1,17 +1,17 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
|
||||
# Argoverse-HD dataset (ring-front-center camera) https://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
|
||||
# Documentation: https://docs.ultralytics.com/datasets/detect/argoverse/
|
||||
# Example usage: yolo train data=Argoverse.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── Argoverse ← downloads here (31.5 GB)
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/Argoverse # dataset root dir
|
||||
train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
|
||||
val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
|
||||
test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
|
||||
path: ../datasets/Argoverse # dataset root dir
|
||||
train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
|
||||
val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
|
||||
test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
|
||||
|
||||
# Classes
|
||||
names:
|
||||
@ -24,7 +24,6 @@ names:
|
||||
6: traffic_light
|
||||
7: stop_sign
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
import json
|
||||
@ -64,7 +63,9 @@ download: |
|
||||
# Download 'https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip' (deprecated S3 link)
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
urls = ['https://drive.google.com/file/d/1st9qW3BeIwQsnR0t8mRpvbsSWIo16ACi/view?usp=drive_link']
|
||||
download(urls, dir=dir)
|
||||
print("\n\nWARNING: Argoverse dataset MUST be downloaded manually, autodownload will NOT work.")
|
||||
print(f"WARNING: Manually download Argoverse dataset '{urls[0]}' to '{dir}' and re-run your command.\n\n")
|
||||
# download(urls, dir=dir)
|
||||
|
||||
# Convert
|
||||
annotations_dir = 'Argoverse-HD/annotations/'
|
||||
|
||||
@ -1,18 +1,19 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# DOTA 2.0 dataset https://captain-whu.github.io/DOTA/index.html for object detection in aerial images by Wuhan University
|
||||
# Example usage: yolo train model=yolov8n-obb.pt data=DOTAv2.yaml
|
||||
# DOTA 1.5 dataset https://captain-whu.github.io/DOTA/index.html for object detection in aerial images by Wuhan University
|
||||
# Documentation: https://docs.ultralytics.com/datasets/obb/dota-v2/
|
||||
# Example usage: yolo train model=yolov8n-obb.pt data=DOTAv1.5.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── dota2 ← downloads here (2GB)
|
||||
# └── dota1.5 ← downloads here (2GB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/DOTAv2 # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 1411 images
|
||||
val: images/val # val images (relative to 'path') 458 images
|
||||
test: images/test # test images (optional) 937 images
|
||||
path: ../datasets/DOTAv1.5 # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 1411 images
|
||||
val: images/val # val images (relative to 'path') 458 images
|
||||
test: images/test # test images (optional) 937 images
|
||||
|
||||
# Classes for DOTA 2.0
|
||||
# Classes for DOTA 1.5
|
||||
names:
|
||||
0: plane
|
||||
1: ship
|
||||
@ -30,8 +31,6 @@ names:
|
||||
13: soccer ball field
|
||||
14: swimming pool
|
||||
15: container crane
|
||||
16: airport
|
||||
17: helipad
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://github.com/ultralytics/yolov5/releases/download/v1.0/DOTAv2.zip
|
||||
download: https://github.com/ultralytics/yolov5/releases/download/v1.0/DOTAv1.5.zip
|
||||
35
ultralytics/cfg/datasets/DOTAv1.yaml
Normal file
35
ultralytics/cfg/datasets/DOTAv1.yaml
Normal file
@ -0,0 +1,35 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# DOTA 1.0 dataset https://captain-whu.github.io/DOTA/index.html for object detection in aerial images by Wuhan University
|
||||
# Documentation: https://docs.ultralytics.com/datasets/obb/dota-v2/
|
||||
# Example usage: yolo train model=yolov8n-obb.pt data=DOTAv1.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── dota1 ← downloads here (2GB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/DOTAv1 # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 1411 images
|
||||
val: images/val # val images (relative to 'path') 458 images
|
||||
test: images/test # test images (optional) 937 images
|
||||
|
||||
# Classes for DOTA 1.0
|
||||
names:
|
||||
0: plane
|
||||
1: ship
|
||||
2: storage tank
|
||||
3: baseball diamond
|
||||
4: tennis court
|
||||
5: basketball court
|
||||
6: ground track field
|
||||
7: harbor
|
||||
8: bridge
|
||||
9: large vehicle
|
||||
10: small vehicle
|
||||
11: helicopter
|
||||
12: roundabout
|
||||
13: soccer ball field
|
||||
14: swimming pool
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://github.com/ultralytics/yolov5/releases/download/v1.0/DOTAv1.zip
|
||||
@ -1,14 +1,14 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
|
||||
# Global Wheat 2020 dataset https://www.global-wheat.com/ by University of Saskatchewan
|
||||
# Documentation: https://docs.ultralytics.com/datasets/detect/globalwheat2020/
|
||||
# Example usage: yolo train data=GlobalWheat2020.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── GlobalWheat2020 ← downloads here (7.0 GB)
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/GlobalWheat2020 # dataset root dir
|
||||
path: ../datasets/GlobalWheat2020 # dataset root dir
|
||||
train: # train images (relative to 'path') 3422 images
|
||||
- images/arvalis_1
|
||||
- images/arvalis_2
|
||||
@ -29,7 +29,6 @@ test: # test images (optional) 1276 images
|
||||
names:
|
||||
0: wheat_head
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
from ultralytics.utils.downloads import download
|
||||
|
||||
@ -1,18 +1,18 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
|
||||
# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
|
||||
# Documentation: https://docs.ultralytics.com/datasets/classify/imagenet/
|
||||
# Example usage: yolo train task=classify data=imagenet
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── imagenet ← downloads here (144 GB)
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/imagenet # dataset root dir
|
||||
train: train # train images (relative to 'path') 1281167 images
|
||||
val: val # val images (relative to 'path') 50000 images
|
||||
test: # test images (optional)
|
||||
path: ../datasets/imagenet # dataset root dir
|
||||
train: train # train images (relative to 'path') 1281167 images
|
||||
val: val # val images (relative to 'path') 50000 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
names:
|
||||
@ -2020,6 +2020,5 @@ map:
|
||||
n13133613: ear
|
||||
n15075141: toilet_tissue
|
||||
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: yolo/data/scripts/get_imagenet.sh
|
||||
|
||||
@ -1,17 +1,17 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Objects365 dataset https://www.objects365.org/ by Megvii
|
||||
# Documentation: https://docs.ultralytics.com/datasets/detect/objects365/
|
||||
# Example usage: yolo train data=Objects365.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── Objects365 ← downloads here (712 GB = 367G data + 345G zips)
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/Objects365 # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 1742289 images
|
||||
path: ../datasets/Objects365 # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 1742289 images
|
||||
val: images/val # val images (relative to 'path') 80000 images
|
||||
test: # test images (optional)
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
names:
|
||||
@ -381,7 +381,6 @@ names:
|
||||
363: Curling
|
||||
364: Table Tennis
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
from tqdm import tqdm
|
||||
|
||||
@ -1,23 +1,22 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
|
||||
# Documentation: https://docs.ultralytics.com/datasets/detect/sku-110k/
|
||||
# Example usage: yolo train data=SKU-110K.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── SKU-110K ← downloads here (13.6 GB)
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/SKU-110K # dataset root dir
|
||||
train: train.txt # train images (relative to 'path') 8219 images
|
||||
val: val.txt # val images (relative to 'path') 588 images
|
||||
test: test.txt # test images (optional) 2936 images
|
||||
path: ../datasets/SKU-110K # dataset root dir
|
||||
train: train.txt # train images (relative to 'path') 8219 images
|
||||
val: val.txt # val images (relative to 'path') 588 images
|
||||
test: test.txt # test images (optional) 2936 images
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: object
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
import shutil
|
||||
|
||||
@ -1,12 +1,12 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
|
||||
# Documentation: # Documentation: https://docs.ultralytics.com/datasets/detect/voc/
|
||||
# Example usage: yolo train data=VOC.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── VOC ← downloads here (2.8 GB)
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/VOC
|
||||
train: # train images (relative to 'path') 16551 images
|
||||
@ -42,7 +42,6 @@ names:
|
||||
18: train
|
||||
19: tvmonitor
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
import xml.etree.ElementTree as ET
|
||||
@ -81,7 +80,7 @@ download: |
|
||||
urls = [f'{url}VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
|
||||
f'{url}VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
|
||||
f'{url}VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
|
||||
download(urls, dir=dir / 'images', curl=True, threads=3)
|
||||
download(urls, dir=dir / 'images', curl=True, threads=3, exist_ok=True) # download and unzip over existing paths (required)
|
||||
|
||||
# Convert
|
||||
path = dir / 'images/VOCdevkit'
|
||||
|
||||
@ -1,17 +1,17 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
|
||||
# Documentation: https://docs.ultralytics.com/datasets/detect/visdrone/
|
||||
# Example usage: yolo train data=VisDrone.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── VisDrone ← downloads here (2.3 GB)
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/VisDrone # dataset root dir
|
||||
train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images
|
||||
val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images
|
||||
test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images
|
||||
path: ../datasets/VisDrone # dataset root dir
|
||||
train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images
|
||||
val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images
|
||||
test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images
|
||||
|
||||
# Classes
|
||||
names:
|
||||
@ -26,7 +26,6 @@ names:
|
||||
8: bus
|
||||
9: motor
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
import os
|
||||
|
||||
24
ultralytics/cfg/datasets/african-wildlife.yaml
Normal file
24
ultralytics/cfg/datasets/african-wildlife.yaml
Normal file
@ -0,0 +1,24 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# African-wildlife dataset by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/detect/african-wildlife/
|
||||
# Example usage: yolo train data=african-wildlife.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── african-wildlife ← downloads here (100 MB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/african-wildlife # dataset root dir
|
||||
train: train/images # train images (relative to 'path') 1052 images
|
||||
val: valid/images # val images (relative to 'path') 225 images
|
||||
test: test/images # test images (relative to 'path') 227 images
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: buffalo
|
||||
1: elephant
|
||||
2: rhino
|
||||
3: zebra
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://ultralytics.com/assets/african-wildlife.zip
|
||||
22
ultralytics/cfg/datasets/brain-tumor.yaml
Normal file
22
ultralytics/cfg/datasets/brain-tumor.yaml
Normal file
@ -0,0 +1,22 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Brain-tumor dataset by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/detect/brain-tumor/
|
||||
# Example usage: yolo train data=brain-tumor.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── brain-tumor ← downloads here (4.05 MB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/brain-tumor # dataset root dir
|
||||
train: train/images # train images (relative to 'path') 893 images
|
||||
val: valid/images # val images (relative to 'path') 223 images
|
||||
test: # test images (relative to 'path')
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: negative
|
||||
1: positive
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://ultralytics.com/assets/brain-tumor.zip
|
||||
43
ultralytics/cfg/datasets/carparts-seg.yaml
Normal file
43
ultralytics/cfg/datasets/carparts-seg.yaml
Normal file
@ -0,0 +1,43 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Carparts-seg dataset by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/segment/carparts-seg/
|
||||
# Example usage: yolo train data=carparts-seg.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── carparts-seg ← downloads here (132 MB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/carparts-seg # dataset root dir
|
||||
train: train/images # train images (relative to 'path') 3516 images
|
||||
val: valid/images # val images (relative to 'path') 276 images
|
||||
test: test/images # test images (relative to 'path') 401 images
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: back_bumper
|
||||
1: back_door
|
||||
2: back_glass
|
||||
3: back_left_door
|
||||
4: back_left_light
|
||||
5: back_light
|
||||
6: back_right_door
|
||||
7: back_right_light
|
||||
8: front_bumper
|
||||
9: front_door
|
||||
10: front_glass
|
||||
11: front_left_door
|
||||
12: front_left_light
|
||||
13: front_light
|
||||
14: front_right_door
|
||||
15: front_right_light
|
||||
16: hood
|
||||
17: left_mirror
|
||||
18: object
|
||||
19: right_mirror
|
||||
20: tailgate
|
||||
21: trunk
|
||||
22: wheel
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://ultralytics.com/assets/carparts-seg.zip
|
||||
@ -1,20 +1,20 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# COCO 2017 dataset http://cocodataset.org by Microsoft
|
||||
# COCO 2017 dataset https://cocodataset.org by Microsoft
|
||||
# Documentation: https://docs.ultralytics.com/datasets/pose/coco/
|
||||
# Example usage: yolo train data=coco-pose.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── coco-pose ← downloads here (20.1 GB)
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/coco-pose # dataset root dir
|
||||
train: train2017.txt # train images (relative to 'path') 118287 images
|
||||
val: val2017.txt # val images (relative to 'path') 5000 images
|
||||
test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
|
||||
path: ../datasets/coco-pose # dataset root dir
|
||||
train: train2017.txt # train images (relative to 'path') 118287 images
|
||||
val: val2017.txt # val images (relative to 'path') 5000 images
|
||||
test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
|
||||
|
||||
# Keypoints
|
||||
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
||||
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
||||
flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
|
||||
|
||||
# Classes
|
||||
|
||||
@ -1,17 +1,17 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# COCO 2017 dataset http://cocodataset.org by Microsoft
|
||||
# COCO 2017 dataset https://cocodataset.org by Microsoft
|
||||
# Documentation: https://docs.ultralytics.com/datasets/detect/coco/
|
||||
# Example usage: yolo train data=coco.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── coco ← downloads here (20.1 GB)
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/coco # dataset root dir
|
||||
train: train2017.txt # train images (relative to 'path') 118287 images
|
||||
val: val2017.txt # val images (relative to 'path') 5000 images
|
||||
test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
|
||||
path: ../datasets/coco # dataset root dir
|
||||
train: train2017.txt # train images (relative to 'path') 118287 images
|
||||
val: val2017.txt # val images (relative to 'path') 5000 images
|
||||
test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
|
||||
|
||||
# Classes
|
||||
names:
|
||||
@ -96,7 +96,6 @@ names:
|
||||
78: hair drier
|
||||
79: toothbrush
|
||||
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: |
|
||||
from ultralytics.utils.downloads import download
|
||||
|
||||
@ -1,17 +1,17 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/segment/coco/
|
||||
# Example usage: yolo train data=coco128.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── coco128-seg ← downloads here (7 MB)
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/coco128-seg # dataset root dir
|
||||
train: images/train2017 # train images (relative to 'path') 128 images
|
||||
val: images/train2017 # val images (relative to 'path') 128 images
|
||||
test: # test images (optional)
|
||||
path: ../datasets/coco128-seg # dataset root dir
|
||||
train: images/train2017 # train images (relative to 'path') 128 images
|
||||
val: images/train2017 # val images (relative to 'path') 128 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
names:
|
||||
@ -96,6 +96,5 @@ names:
|
||||
78: hair drier
|
||||
79: toothbrush
|
||||
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://ultralytics.com/assets/coco128-seg.zip
|
||||
|
||||
@ -1,17 +1,17 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/detect/coco/
|
||||
# Example usage: yolo train data=coco128.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── coco128 ← downloads here (7 MB)
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/coco128 # dataset root dir
|
||||
train: images/train2017 # train images (relative to 'path') 128 images
|
||||
val: images/train2017 # val images (relative to 'path') 128 images
|
||||
test: # test images (optional)
|
||||
path: ../datasets/coco128 # dataset root dir
|
||||
train: images/train2017 # train images (relative to 'path') 128 images
|
||||
val: images/train2017 # val images (relative to 'path') 128 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
names:
|
||||
@ -96,6 +96,5 @@ names:
|
||||
78: hair drier
|
||||
79: toothbrush
|
||||
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://ultralytics.com/assets/coco128.zip
|
||||
|
||||
@ -1,20 +1,20 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# COCO8-pose dataset (first 8 images from COCO train2017) by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/pose/coco8-pose/
|
||||
# Example usage: yolo train data=coco8-pose.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── coco8-pose ← downloads here (1 MB)
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/coco8-pose # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 4 images
|
||||
val: images/val # val images (relative to 'path') 4 images
|
||||
test: # test images (optional)
|
||||
path: ../datasets/coco8-pose # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 4 images
|
||||
val: images/val # val images (relative to 'path') 4 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Keypoints
|
||||
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
||||
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
||||
flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
|
||||
|
||||
# Classes
|
||||
|
||||
@ -1,17 +1,17 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# COCO8-seg dataset (first 8 images from COCO train2017) by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/segment/coco8-seg/
|
||||
# Example usage: yolo train data=coco8-seg.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── coco8-seg ← downloads here (1 MB)
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/coco8-seg # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 4 images
|
||||
val: images/val # val images (relative to 'path') 4 images
|
||||
test: # test images (optional)
|
||||
path: ../datasets/coco8-seg # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 4 images
|
||||
val: images/val # val images (relative to 'path') 4 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
names:
|
||||
@ -96,6 +96,5 @@ names:
|
||||
78: hair drier
|
||||
79: toothbrush
|
||||
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://ultralytics.com/assets/coco8-seg.zip
|
||||
|
||||
@ -1,17 +1,17 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# COCO8 dataset (first 8 images from COCO train2017) by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/detect/coco8/
|
||||
# Example usage: yolo train data=coco8.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── coco8 ← downloads here (1 MB)
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/coco8 # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 4 images
|
||||
val: images/val # val images (relative to 'path') 4 images
|
||||
test: # test images (optional)
|
||||
path: ../datasets/coco8 # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 4 images
|
||||
val: images/val # val images (relative to 'path') 4 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
names:
|
||||
@ -96,6 +96,5 @@ names:
|
||||
78: hair drier
|
||||
79: toothbrush
|
||||
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://ultralytics.com/assets/coco8.zip
|
||||
|
||||
21
ultralytics/cfg/datasets/crack-seg.yaml
Normal file
21
ultralytics/cfg/datasets/crack-seg.yaml
Normal file
@ -0,0 +1,21 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Crack-seg dataset by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/segment/crack-seg/
|
||||
# Example usage: yolo train data=crack-seg.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── crack-seg ← downloads here (91.2 MB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/crack-seg # dataset root dir
|
||||
train: train/images # train images (relative to 'path') 3717 images
|
||||
val: valid/images # val images (relative to 'path') 112 images
|
||||
test: test/images # test images (relative to 'path') 200 images
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: crack
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://ultralytics.com/assets/crack-seg.zip
|
||||
34
ultralytics/cfg/datasets/dota8.yaml
Normal file
34
ultralytics/cfg/datasets/dota8.yaml
Normal file
@ -0,0 +1,34 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# DOTA8 dataset 8 images from split DOTAv1 dataset by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/obb/dota8/
|
||||
# Example usage: yolo train model=yolov8n-obb.pt data=dota8.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── dota8 ← downloads here (1MB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/dota8 # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 4 images
|
||||
val: images/val # val images (relative to 'path') 4 images
|
||||
|
||||
# Classes for DOTA 1.0
|
||||
names:
|
||||
0: plane
|
||||
1: ship
|
||||
2: storage tank
|
||||
3: baseball diamond
|
||||
4: tennis court
|
||||
5: basketball court
|
||||
6: ground track field
|
||||
7: harbor
|
||||
8: bridge
|
||||
9: large vehicle
|
||||
10: small vehicle
|
||||
11: helicopter
|
||||
12: roundabout
|
||||
13: soccer ball field
|
||||
14: swimming pool
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://github.com/ultralytics/yolov5/releases/download/v1.0/dota8.zip
|
||||
@ -1,17 +1,17 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Open Images v7 dataset https://storage.googleapis.com/openimages/web/index.html by Google
|
||||
# Documentation: https://docs.ultralytics.com/datasets/detect/open-images-v7/
|
||||
# Example usage: yolo train data=open-images-v7.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── open-images-v7 ← downloads here (561 GB)
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/open-images-v7 # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 1743042 images
|
||||
val: images/val # val images (relative to 'path') 41620 images
|
||||
test: # test images (optional)
|
||||
path: ../datasets/open-images-v7 # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 1743042 images
|
||||
val: images/val # val images (relative to 'path') 41620 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
names:
|
||||
@ -617,7 +617,6 @@ names:
|
||||
599: Zebra
|
||||
600: Zucchini
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
from ultralytics.utils import LOGGER, SETTINGS, Path, is_ubuntu, get_ubuntu_version
|
||||
|
||||
21
ultralytics/cfg/datasets/package-seg.yaml
Normal file
21
ultralytics/cfg/datasets/package-seg.yaml
Normal file
@ -0,0 +1,21 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Package-seg dataset by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/segment/package-seg/
|
||||
# Example usage: yolo train data=package-seg.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── package-seg ← downloads here (102 MB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/package-seg # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 1920 images
|
||||
val: images/val # val images (relative to 'path') 89 images
|
||||
test: test/images # test images (relative to 'path') 188 images
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: package
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://ultralytics.com/assets/package-seg.zip
|
||||
24
ultralytics/cfg/datasets/tiger-pose.yaml
Normal file
24
ultralytics/cfg/datasets/tiger-pose.yaml
Normal file
@ -0,0 +1,24 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Tiger Pose dataset by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/pose/tiger-pose/
|
||||
# Example usage: yolo train data=tiger-pose.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── tiger-pose ← downloads here (75.3 MB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/tiger-pose # dataset root dir
|
||||
train: train # train images (relative to 'path') 210 images
|
||||
val: val # val images (relative to 'path') 53 images
|
||||
|
||||
# Keypoints
|
||||
kpt_shape: [12, 2] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
||||
flip_idx: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: tiger
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://ultralytics.com/assets/tiger-pose.zip
|
||||
@ -1,17 +1,17 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)
|
||||
# -------- DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command! --------
|
||||
# Documentation: https://docs.ultralytics.com/datasets/detect/xview/
|
||||
# Example usage: yolo train data=xView.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── xView ← downloads here (20.7 GB)
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/xView # dataset root dir
|
||||
train: images/autosplit_train.txt # train images (relative to 'path') 90% of 847 train images
|
||||
val: images/autosplit_val.txt # train images (relative to 'path') 10% of 847 train images
|
||||
path: ../datasets/xView # dataset root dir
|
||||
train: images/autosplit_train.txt # train images (relative to 'path') 90% of 847 train images
|
||||
val: images/autosplit_val.txt # train images (relative to 'path') 10% of 847 train images
|
||||
|
||||
# Classes
|
||||
names:
|
||||
@ -76,7 +76,6 @@ names:
|
||||
58: Pylon
|
||||
59: Tower
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
import json
|
||||
|
||||
@ -1,116 +1,127 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Default training settings and hyperparameters for medium-augmentation COCO training
|
||||
|
||||
task: detect # (str) YOLO task, i.e. detect, segment, classify, pose
|
||||
mode: train # (str) YOLO mode, i.e. train, val, predict, export, track, benchmark
|
||||
task: detect # (str) YOLO task, i.e. detect, segment, classify, pose
|
||||
mode: train # (str) YOLO mode, i.e. train, val, predict, export, track, benchmark
|
||||
|
||||
# Train settings -------------------------------------------------------------------------------------------------------
|
||||
model: # (str, optional) path to model file, i.e. yolov8n.pt, yolov8n.yaml
|
||||
data: # (str, optional) path to data file, i.e. coco128.yaml
|
||||
epochs: 100 # (int) number of epochs to train for
|
||||
patience: 50 # (int) epochs to wait for no observable improvement for early stopping of training
|
||||
batch: 16 # (int) number of images per batch (-1 for AutoBatch)
|
||||
imgsz: 640 # (int | list) input images size as int for train and val modes, or list[w,h] for predict and export modes
|
||||
save: True # (bool) save train checkpoints and predict results
|
||||
model: # (str, optional) path to model file, i.e. yolov8n.pt, yolov8n.yaml
|
||||
data: # (str, optional) path to data file, i.e. coco128.yaml
|
||||
epochs: 100 # (int) number of epochs to train for
|
||||
time: # (float, optional) number of hours to train for, overrides epochs if supplied
|
||||
patience: 100 # (int) epochs to wait for no observable improvement for early stopping of training
|
||||
batch: 16 # (int) number of images per batch (-1 for AutoBatch)
|
||||
imgsz: 640 # (int | list) input images size as int for train and val modes, or list[w,h] for predict and export modes
|
||||
save: True # (bool) save train checkpoints and predict results
|
||||
save_period: -1 # (int) Save checkpoint every x epochs (disabled if < 1)
|
||||
cache: False # (bool) True/ram, disk or False. Use cache for data loading
|
||||
device: # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
|
||||
workers: 8 # (int) number of worker threads for data loading (per RANK if DDP)
|
||||
project: # (str, optional) project name
|
||||
name: # (str, optional) experiment name, results saved to 'project/name' directory
|
||||
exist_ok: False # (bool) whether to overwrite existing experiment
|
||||
pretrained: True # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)
|
||||
optimizer: auto # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
|
||||
verbose: True # (bool) whether to print verbose output
|
||||
seed: 0 # (int) random seed for reproducibility
|
||||
deterministic: True # (bool) whether to enable deterministic mode
|
||||
single_cls: False # (bool) train multi-class data as single-class
|
||||
rect: False # (bool) rectangular training if mode='train' or rectangular validation if mode='val'
|
||||
cos_lr: False # (bool) use cosine learning rate scheduler
|
||||
close_mosaic: 10 # (int) disable mosaic augmentation for final epochs (0 to disable)
|
||||
resume: False # (bool) resume training from last checkpoint
|
||||
amp: True # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
|
||||
fraction: 1.0 # (float) dataset fraction to train on (default is 1.0, all images in train set)
|
||||
profile: False # (bool) profile ONNX and TensorRT speeds during training for loggers
|
||||
freeze: None # (int | list, optional) freeze first n layers, or freeze list of layer indices during training
|
||||
val_period: 1 # (int) Validation every x epochs
|
||||
cache: False # (bool) True/ram, disk or False. Use cache for data loading
|
||||
device: # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
|
||||
workers: 8 # (int) number of worker threads for data loading (per RANK if DDP)
|
||||
project: # (str, optional) project name
|
||||
name: # (str, optional) experiment name, results saved to 'project/name' directory
|
||||
exist_ok: False # (bool) whether to overwrite existing experiment
|
||||
pretrained: True # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)
|
||||
optimizer: auto # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
|
||||
verbose: True # (bool) whether to print verbose output
|
||||
seed: 0 # (int) random seed for reproducibility
|
||||
deterministic: True # (bool) whether to enable deterministic mode
|
||||
single_cls: False # (bool) train multi-class data as single-class
|
||||
rect: False # (bool) rectangular training if mode='train' or rectangular validation if mode='val'
|
||||
cos_lr: False # (bool) use cosine learning rate scheduler
|
||||
close_mosaic: 10 # (int) disable mosaic augmentation for final epochs (0 to disable)
|
||||
resume: False # (bool) resume training from last checkpoint
|
||||
amp: True # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
|
||||
fraction: 1.0 # (float) dataset fraction to train on (default is 1.0, all images in train set)
|
||||
profile: False # (bool) profile ONNX and TensorRT speeds during training for loggers
|
||||
freeze: None # (int | list, optional) freeze first n layers, or freeze list of layer indices during training
|
||||
multi_scale: False # (bool) Whether to use multiscale during training
|
||||
# Segmentation
|
||||
overlap_mask: True # (bool) masks should overlap during training (segment train only)
|
||||
mask_ratio: 4 # (int) mask downsample ratio (segment train only)
|
||||
overlap_mask: True # (bool) masks should overlap during training (segment train only)
|
||||
mask_ratio: 4 # (int) mask downsample ratio (segment train only)
|
||||
# Classification
|
||||
dropout: 0.0 # (float) use dropout regularization (classify train only)
|
||||
dropout: 0.0 # (float) use dropout regularization (classify train only)
|
||||
|
||||
# Val/Test settings ----------------------------------------------------------------------------------------------------
|
||||
val: True # (bool) validate/test during training
|
||||
split: val # (str) dataset split to use for validation, i.e. 'val', 'test' or 'train'
|
||||
save_json: False # (bool) save results to JSON file
|
||||
save_hybrid: False # (bool) save hybrid version of labels (labels + additional predictions)
|
||||
conf: # (float, optional) object confidence threshold for detection (default 0.25 predict, 0.001 val)
|
||||
iou: 0.7 # (float) intersection over union (IoU) threshold for NMS
|
||||
max_det: 300 # (int) maximum number of detections per image
|
||||
half: False # (bool) use half precision (FP16)
|
||||
dnn: False # (bool) use OpenCV DNN for ONNX inference
|
||||
plots: True # (bool) save plots during train/val
|
||||
val: True # (bool) validate/test during training
|
||||
split: val # (str) dataset split to use for validation, i.e. 'val', 'test' or 'train'
|
||||
save_json: False # (bool) save results to JSON file
|
||||
save_hybrid: False # (bool) save hybrid version of labels (labels + additional predictions)
|
||||
conf: # (float, optional) object confidence threshold for detection (default 0.25 predict, 0.001 val)
|
||||
iou: 0.7 # (float) intersection over union (IoU) threshold for NMS
|
||||
max_det: 300 # (int) maximum number of detections per image
|
||||
half: False # (bool) use half precision (FP16)
|
||||
dnn: False # (bool) use OpenCV DNN for ONNX inference
|
||||
plots: True # (bool) save plots and images during train/val
|
||||
|
||||
# Prediction settings --------------------------------------------------------------------------------------------------
|
||||
source: # (str, optional) source directory for images or videos
|
||||
show: False # (bool) show results if possible
|
||||
save_txt: False # (bool) save results as .txt file
|
||||
save_conf: False # (bool) save results with confidence scores
|
||||
save_crop: False # (bool) save cropped images with results
|
||||
show_labels: True # (bool) show object labels in plots
|
||||
show_conf: True # (bool) show object confidence scores in plots
|
||||
vid_stride: 1 # (int) video frame-rate stride
|
||||
stream_buffer: False # (bool) buffer all streaming frames (True) or return the most recent frame (False)
|
||||
line_width: # (int, optional) line width of the bounding boxes, auto if missing
|
||||
visualize: False # (bool) visualize model features
|
||||
augment: False # (bool) apply image augmentation to prediction sources
|
||||
agnostic_nms: False # (bool) class-agnostic NMS
|
||||
classes: # (int | list[int], optional) filter results by class, i.e. classes=0, or classes=[0,2,3]
|
||||
retina_masks: False # (bool) use high-resolution segmentation masks
|
||||
boxes: True # (bool) Show boxes in segmentation predictions
|
||||
# Predict settings -----------------------------------------------------------------------------------------------------
|
||||
source: # (str, optional) source directory for images or videos
|
||||
vid_stride: 1 # (int) video frame-rate stride
|
||||
stream_buffer: False # (bool) buffer all streaming frames (True) or return the most recent frame (False)
|
||||
visualize: False # (bool) visualize model features
|
||||
augment: False # (bool) apply image augmentation to prediction sources
|
||||
agnostic_nms: False # (bool) class-agnostic NMS
|
||||
classes: # (int | list[int], optional) filter results by class, i.e. classes=0, or classes=[0,2,3]
|
||||
retina_masks: False # (bool) use high-resolution segmentation masks
|
||||
embed: # (list[int], optional) return feature vectors/embeddings from given layers
|
||||
|
||||
# Visualize settings ---------------------------------------------------------------------------------------------------
|
||||
show: False # (bool) show predicted images and videos if environment allows
|
||||
save_frames: False # (bool) save predicted individual video frames
|
||||
save_txt: False # (bool) save results as .txt file
|
||||
save_conf: False # (bool) save results with confidence scores
|
||||
save_crop: False # (bool) save cropped images with results
|
||||
show_labels: True # (bool) show prediction labels, i.e. 'person'
|
||||
show_conf: True # (bool) show prediction confidence, i.e. '0.99'
|
||||
show_boxes: True # (bool) show prediction boxes
|
||||
line_width: # (int, optional) line width of the bounding boxes. Scaled to image size if None.
|
||||
|
||||
# Export settings ------------------------------------------------------------------------------------------------------
|
||||
format: torchscript # (str) format to export to, choices at https://docs.ultralytics.com/modes/export/#export-formats
|
||||
keras: False # (bool) use Kera=s
|
||||
optimize: False # (bool) TorchScript: optimize for mobile
|
||||
int8: False # (bool) CoreML/TF INT8 quantization
|
||||
dynamic: False # (bool) ONNX/TF/TensorRT: dynamic axes
|
||||
simplify: False # (bool) ONNX: simplify model
|
||||
opset: # (int, optional) ONNX: opset version
|
||||
workspace: 4 # (int) TensorRT: workspace size (GB)
|
||||
nms: False # (bool) CoreML: add NMS
|
||||
format: torchscript # (str) format to export to, choices at https://docs.ultralytics.com/modes/export/#export-formats
|
||||
keras: False # (bool) use Kera=s
|
||||
optimize: False # (bool) TorchScript: optimize for mobile
|
||||
int8: False # (bool) CoreML/TF INT8 quantization
|
||||
dynamic: False # (bool) ONNX/TF/TensorRT: dynamic axes
|
||||
simplify: False # (bool) ONNX: simplify model using `onnxslim`
|
||||
opset: # (int, optional) ONNX: opset version
|
||||
workspace: 4 # (int) TensorRT: workspace size (GB)
|
||||
nms: False # (bool) CoreML: add NMS
|
||||
|
||||
# Hyperparameters ------------------------------------------------------------------------------------------------------
|
||||
lr0: 0.01 # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
|
||||
lrf: 0.01 # (float) final learning rate (lr0 * lrf)
|
||||
momentum: 0.937 # (float) SGD momentum/Adam beta1
|
||||
weight_decay: 0.0005 # (float) optimizer weight decay 5e-4
|
||||
warmup_epochs: 3.0 # (float) warmup epochs (fractions ok)
|
||||
warmup_momentum: 0.8 # (float) warmup initial momentum
|
||||
warmup_bias_lr: 0.1 # (float) warmup initial bias lr
|
||||
box: 7.5 # (float) box loss gain
|
||||
cls: 0.5 # (float) cls loss gain (scale with pixels)
|
||||
dfl: 1.5 # (float) dfl loss gain
|
||||
pose: 12.0 # (float) pose loss gain
|
||||
kobj: 1.0 # (float) keypoint obj loss gain
|
||||
label_smoothing: 0.0 # (float) label smoothing (fraction)
|
||||
nbs: 64 # (int) nominal batch size
|
||||
hsv_h: 0.015 # (float) image HSV-Hue augmentation (fraction)
|
||||
hsv_s: 0.7 # (float) image HSV-Saturation augmentation (fraction)
|
||||
hsv_v: 0.4 # (float) image HSV-Value augmentation (fraction)
|
||||
degrees: 0.0 # (float) image rotation (+/- deg)
|
||||
translate: 0.1 # (float) image translation (+/- fraction)
|
||||
scale: 0.5 # (float) image scale (+/- gain)
|
||||
shear: 0.0 # (float) image shear (+/- deg)
|
||||
perspective: 0.0 # (float) image perspective (+/- fraction), range 0-0.001
|
||||
flipud: 0.0 # (float) image flip up-down (probability)
|
||||
fliplr: 0.5 # (float) image flip left-right (probability)
|
||||
mosaic: 1.0 # (float) image mosaic (probability)
|
||||
mixup: 0.0 # (float) image mixup (probability)
|
||||
copy_paste: 0.0 # (float) segment copy-paste (probability)
|
||||
lr0: 0.01 # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
|
||||
lrf: 0.01 # (float) final learning rate (lr0 * lrf)
|
||||
momentum: 0.937 # (float) SGD momentum/Adam beta1
|
||||
weight_decay: 0.0005 # (float) optimizer weight decay 5e-4
|
||||
warmup_epochs: 3.0 # (float) warmup epochs (fractions ok)
|
||||
warmup_momentum: 0.8 # (float) warmup initial momentum
|
||||
warmup_bias_lr: 0.1 # (float) warmup initial bias lr
|
||||
box: 7.5 # (float) box loss gain
|
||||
cls: 0.5 # (float) cls loss gain (scale with pixels)
|
||||
dfl: 1.5 # (float) dfl loss gain
|
||||
pose: 12.0 # (float) pose loss gain
|
||||
kobj: 1.0 # (float) keypoint obj loss gain
|
||||
label_smoothing: 0.0 # (float) label smoothing (fraction)
|
||||
nbs: 64 # (int) nominal batch size
|
||||
hsv_h: 0.015 # (float) image HSV-Hue augmentation (fraction)
|
||||
hsv_s: 0.7 # (float) image HSV-Saturation augmentation (fraction)
|
||||
hsv_v: 0.4 # (float) image HSV-Value augmentation (fraction)
|
||||
degrees: 0.0 # (float) image rotation (+/- deg)
|
||||
translate: 0.1 # (float) image translation (+/- fraction)
|
||||
scale: 0.5 # (float) image scale (+/- gain)
|
||||
shear: 0.0 # (float) image shear (+/- deg)
|
||||
perspective: 0.0 # (float) image perspective (+/- fraction), range 0-0.001
|
||||
flipud: 0.0 # (float) image flip up-down (probability)
|
||||
fliplr: 0.5 # (float) image flip left-right (probability)
|
||||
bgr: 0.0 # (float) image channel BGR (probability)
|
||||
mosaic: 1.0 # (float) image mosaic (probability)
|
||||
mixup: 0.0 # (float) image mixup (probability)
|
||||
copy_paste: 0.0 # (float) segment copy-paste (probability)
|
||||
auto_augment: randaugment # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix)
|
||||
erasing: 0.4 # (float) probability of random erasing during classification training (0-1)
|
||||
crop_fraction: 1.0 # (float) image crop fraction for classification evaluation/inference (0-1)
|
||||
|
||||
# Custom config.yaml ---------------------------------------------------------------------------------------------------
|
||||
cfg: # (str, optional) for overriding defaults.yaml
|
||||
cfg: # (str, optional) for overriding defaults.yaml
|
||||
|
||||
# Tracker settings ------------------------------------------------------------------------------------------------------
|
||||
tracker: botsort.yaml # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml]
|
||||
tracker: botsort.yaml # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml]
|
||||
|
||||
@ -14,8 +14,7 @@ Model `*.yaml` files may be used directly in the Command Line Interface (CLI) wi
|
||||
yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=100
|
||||
```
|
||||
|
||||
They may also be used directly in a Python environment, and accepts the same
|
||||
[arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
|
||||
They may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
@ -2,49 +2,49 @@
|
||||
# RT-DETR-l object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/rtdetr
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
l: [1.00, 1.00, 1024]
|
||||
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, HGStem, [32, 48]] # 0-P2/4
|
||||
- [-1, 6, HGBlock, [48, 128, 3]] # stage 1
|
||||
- [-1, 1, HGStem, [32, 48]] # 0-P2/4
|
||||
- [-1, 6, HGBlock, [48, 128, 3]] # stage 1
|
||||
|
||||
- [-1, 1, DWConv, [128, 3, 2, 1, False]] # 2-P3/8
|
||||
- [-1, 6, HGBlock, [96, 512, 3]] # stage 2
|
||||
- [-1, 1, DWConv, [128, 3, 2, 1, False]] # 2-P3/8
|
||||
- [-1, 6, HGBlock, [96, 512, 3]] # stage 2
|
||||
|
||||
- [-1, 1, DWConv, [512, 3, 2, 1, False]] # 4-P3/16
|
||||
- [-1, 6, HGBlock, [192, 1024, 5, True, False]] # cm, c2, k, light, shortcut
|
||||
- [-1, 1, DWConv, [512, 3, 2, 1, False]] # 4-P3/16
|
||||
- [-1, 6, HGBlock, [192, 1024, 5, True, False]] # cm, c2, k, light, shortcut
|
||||
- [-1, 6, HGBlock, [192, 1024, 5, True, True]]
|
||||
- [-1, 6, HGBlock, [192, 1024, 5, True, True]] # stage 3
|
||||
- [-1, 6, HGBlock, [192, 1024, 5, True, True]] # stage 3
|
||||
|
||||
- [-1, 1, DWConv, [1024, 3, 2, 1, False]] # 8-P4/32
|
||||
- [-1, 6, HGBlock, [384, 2048, 5, True, False]] # stage 4
|
||||
- [-1, 1, DWConv, [1024, 3, 2, 1, False]] # 8-P4/32
|
||||
- [-1, 6, HGBlock, [384, 2048, 5, True, False]] # stage 4
|
||||
|
||||
head:
|
||||
- [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 10 input_proj.2
|
||||
- [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 10 input_proj.2
|
||||
- [-1, 1, AIFI, [1024, 8]]
|
||||
- [-1, 1, Conv, [256, 1, 1]] # 12, Y5, lateral_convs.0
|
||||
- [-1, 1, Conv, [256, 1, 1]] # 12, Y5, lateral_convs.0
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [7, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14 input_proj.1
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [7, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14 input_proj.1
|
||||
- [[-2, -1], 1, Concat, [1]]
|
||||
- [-1, 3, RepC3, [256]] # 16, fpn_blocks.0
|
||||
- [-1, 1, Conv, [256, 1, 1]] # 17, Y4, lateral_convs.1
|
||||
- [-1, 3, RepC3, [256]] # 16, fpn_blocks.0
|
||||
- [-1, 1, Conv, [256, 1, 1]] # 17, Y4, lateral_convs.1
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 19 input_proj.0
|
||||
- [[-2, -1], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, RepC3, [256]] # X3 (21), fpn_blocks.1
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 19 input_proj.0
|
||||
- [[-2, -1], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, RepC3, [256]] # X3 (21), fpn_blocks.1
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 22, downsample_convs.0
|
||||
- [[-1, 17], 1, Concat, [1]] # cat Y4
|
||||
- [-1, 3, RepC3, [256]] # F4 (24), pan_blocks.0
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 22, downsample_convs.0
|
||||
- [[-1, 17], 1, Concat, [1]] # cat Y4
|
||||
- [-1, 3, RepC3, [256]] # F4 (24), pan_blocks.0
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 25, downsample_convs.1
|
||||
- [[-1, 12], 1, Concat, [1]] # cat Y5
|
||||
- [-1, 3, RepC3, [256]] # F5 (27), pan_blocks.1
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 25, downsample_convs.1
|
||||
- [[-1, 12], 1, Concat, [1]] # cat Y5
|
||||
- [-1, 3, RepC3, [256]] # F5 (27), pan_blocks.1
|
||||
|
||||
- [[21, 24, 27], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
||||
- [[21, 24, 27], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
||||
|
||||
42
ultralytics/cfg/models/rt-detr/rtdetr-resnet101.yaml
Normal file
42
ultralytics/cfg/models/rt-detr/rtdetr-resnet101.yaml
Normal file
@ -0,0 +1,42 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# RT-DETR-ResNet101 object detection model with P3-P5 outputs.
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
l: [1.00, 1.00, 1024]
|
||||
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0
|
||||
- [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1
|
||||
- [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2
|
||||
- [-1, 1, ResNetLayer, [512, 256, 2, False, 23]] # 3
|
||||
- [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4
|
||||
|
||||
head:
|
||||
- [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 5
|
||||
- [-1, 1, AIFI, [1024, 8]]
|
||||
- [-1, 1, Conv, [256, 1, 1]] # 7
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 9
|
||||
- [[-2, -1], 1, Concat, [1]]
|
||||
- [-1, 3, RepC3, [256]] # 11
|
||||
- [-1, 1, Conv, [256, 1, 1]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [2, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14
|
||||
- [[-2, -1], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, RepC3, [256]] # X3 (16), fpn_blocks.1
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 17, downsample_convs.0
|
||||
- [[-1, 12], 1, Concat, [1]] # cat Y4
|
||||
- [-1, 3, RepC3, [256]] # F4 (19), pan_blocks.0
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 20, downsample_convs.1
|
||||
- [[-1, 7], 1, Concat, [1]] # cat Y5
|
||||
- [-1, 3, RepC3, [256]] # F5 (22), pan_blocks.1
|
||||
|
||||
- [[16, 19, 22], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
||||
42
ultralytics/cfg/models/rt-detr/rtdetr-resnet50.yaml
Normal file
42
ultralytics/cfg/models/rt-detr/rtdetr-resnet50.yaml
Normal file
@ -0,0 +1,42 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# RT-DETR-ResNet50 object detection model with P3-P5 outputs.
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
l: [1.00, 1.00, 1024]
|
||||
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0
|
||||
- [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1
|
||||
- [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2
|
||||
- [-1, 1, ResNetLayer, [512, 256, 2, False, 6]] # 3
|
||||
- [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4
|
||||
|
||||
head:
|
||||
- [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 5
|
||||
- [-1, 1, AIFI, [1024, 8]]
|
||||
- [-1, 1, Conv, [256, 1, 1]] # 7
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 9
|
||||
- [[-2, -1], 1, Concat, [1]]
|
||||
- [-1, 3, RepC3, [256]] # 11
|
||||
- [-1, 1, Conv, [256, 1, 1]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [2, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14
|
||||
- [[-2, -1], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, RepC3, [256]] # X3 (16), fpn_blocks.1
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 17, downsample_convs.0
|
||||
- [[-1, 12], 1, Concat, [1]] # cat Y4
|
||||
- [-1, 3, RepC3, [256]] # F4 (19), pan_blocks.0
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 20, downsample_convs.1
|
||||
- [[-1, 7], 1, Concat, [1]] # cat Y5
|
||||
- [-1, 3, RepC3, [256]] # F5 (22), pan_blocks.1
|
||||
|
||||
- [[16, 19, 22], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
||||
@ -2,53 +2,53 @@
|
||||
# RT-DETR-x object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/rtdetr
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
x: [1.00, 1.00, 2048]
|
||||
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, HGStem, [32, 64]] # 0-P2/4
|
||||
- [-1, 6, HGBlock, [64, 128, 3]] # stage 1
|
||||
- [-1, 1, HGStem, [32, 64]] # 0-P2/4
|
||||
- [-1, 6, HGBlock, [64, 128, 3]] # stage 1
|
||||
|
||||
- [-1, 1, DWConv, [128, 3, 2, 1, False]] # 2-P3/8
|
||||
- [-1, 1, DWConv, [128, 3, 2, 1, False]] # 2-P3/8
|
||||
- [-1, 6, HGBlock, [128, 512, 3]]
|
||||
- [-1, 6, HGBlock, [128, 512, 3, False, True]] # 4-stage 2
|
||||
- [-1, 6, HGBlock, [128, 512, 3, False, True]] # 4-stage 2
|
||||
|
||||
- [-1, 1, DWConv, [512, 3, 2, 1, False]] # 5-P3/16
|
||||
- [-1, 6, HGBlock, [256, 1024, 5, True, False]] # cm, c2, k, light, shortcut
|
||||
- [-1, 1, DWConv, [512, 3, 2, 1, False]] # 5-P3/16
|
||||
- [-1, 6, HGBlock, [256, 1024, 5, True, False]] # cm, c2, k, light, shortcut
|
||||
- [-1, 6, HGBlock, [256, 1024, 5, True, True]]
|
||||
- [-1, 6, HGBlock, [256, 1024, 5, True, True]]
|
||||
- [-1, 6, HGBlock, [256, 1024, 5, True, True]]
|
||||
- [-1, 6, HGBlock, [256, 1024, 5, True, True]] # 10-stage 3
|
||||
- [-1, 6, HGBlock, [256, 1024, 5, True, True]] # 10-stage 3
|
||||
|
||||
- [-1, 1, DWConv, [1024, 3, 2, 1, False]] # 11-P4/32
|
||||
- [-1, 1, DWConv, [1024, 3, 2, 1, False]] # 11-P4/32
|
||||
- [-1, 6, HGBlock, [512, 2048, 5, True, False]]
|
||||
- [-1, 6, HGBlock, [512, 2048, 5, True, True]] # 13-stage 4
|
||||
- [-1, 6, HGBlock, [512, 2048, 5, True, True]] # 13-stage 4
|
||||
|
||||
head:
|
||||
- [-1, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 14 input_proj.2
|
||||
- [-1, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 14 input_proj.2
|
||||
- [-1, 1, AIFI, [2048, 8]]
|
||||
- [-1, 1, Conv, [384, 1, 1]] # 16, Y5, lateral_convs.0
|
||||
- [-1, 1, Conv, [384, 1, 1]] # 16, Y5, lateral_convs.0
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [10, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 18 input_proj.1
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [10, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 18 input_proj.1
|
||||
- [[-2, -1], 1, Concat, [1]]
|
||||
- [-1, 3, RepC3, [384]] # 20, fpn_blocks.0
|
||||
- [-1, 1, Conv, [384, 1, 1]] # 21, Y4, lateral_convs.1
|
||||
- [-1, 3, RepC3, [384]] # 20, fpn_blocks.0
|
||||
- [-1, 1, Conv, [384, 1, 1]] # 21, Y4, lateral_convs.1
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [4, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 23 input_proj.0
|
||||
- [[-2, -1], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, RepC3, [384]] # X3 (25), fpn_blocks.1
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [4, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 23 input_proj.0
|
||||
- [[-2, -1], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, RepC3, [384]] # X3 (25), fpn_blocks.1
|
||||
|
||||
- [-1, 1, Conv, [384, 3, 2]] # 26, downsample_convs.0
|
||||
- [[-1, 21], 1, Concat, [1]] # cat Y4
|
||||
- [-1, 3, RepC3, [384]] # F4 (28), pan_blocks.0
|
||||
- [-1, 1, Conv, [384, 3, 2]] # 26, downsample_convs.0
|
||||
- [[-1, 21], 1, Concat, [1]] # cat Y4
|
||||
- [-1, 3, RepC3, [384]] # F4 (28), pan_blocks.0
|
||||
|
||||
- [-1, 1, Conv, [384, 3, 2]] # 29, downsample_convs.1
|
||||
- [[-1, 16], 1, Concat, [1]] # cat Y5
|
||||
- [-1, 3, RepC3, [384]] # F5 (31), pan_blocks.1
|
||||
- [-1, 1, Conv, [384, 3, 2]] # 29, downsample_convs.1
|
||||
- [[-1, 16], 1, Concat, [1]] # cat Y5
|
||||
- [-1, 3, RepC3, [384]] # F5 (31), pan_blocks.1
|
||||
|
||||
- [[25, 28, 31], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
||||
- [[25, 28, 31], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
||||
|
||||
40
ultralytics/cfg/models/v10/yolov10b.yaml
Normal file
40
ultralytics/cfg/models/v10/yolov10b.yaml
Normal file
@ -0,0 +1,40 @@
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
b: [0.67, 1.00, 512]
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2fCIB, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, PSA, [1024]] # 10
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2fCIB, [512, True]] # 13
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 13], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
|
||||
|
||||
- [-1, 1, SCDown, [512, 3, 2]]
|
||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
|
||||
|
||||
- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
|
||||
40
ultralytics/cfg/models/v10/yolov10l.yaml
Normal file
40
ultralytics/cfg/models/v10/yolov10l.yaml
Normal file
@ -0,0 +1,40 @@
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2fCIB, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, PSA, [1024]] # 10
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2fCIB, [512, True]] # 13
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 13], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
|
||||
|
||||
- [-1, 1, SCDown, [512, 3, 2]]
|
||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
|
||||
|
||||
- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
|
||||
43
ultralytics/cfg/models/v10/yolov10m.yaml
Normal file
43
ultralytics/cfg/models/v10/yolov10m.yaml
Normal file
@ -0,0 +1,43 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2fCIB, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, PSA, [1024]] # 10
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 13
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 13], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
|
||||
|
||||
- [-1, 1, SCDown, [512, 3, 2]]
|
||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
|
||||
|
||||
- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
|
||||
40
ultralytics/cfg/models/v10/yolov10n.yaml
Normal file
40
ultralytics/cfg/models/v10/yolov10n.yaml
Normal file
@ -0,0 +1,40 @@
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, PSA, [1024]] # 10
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 13
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 13], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 19 (P4/16-medium)
|
||||
|
||||
- [-1, 1, SCDown, [512, 3, 2]]
|
||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2fCIB, [1024, True, True]] # 22 (P5/32-large)
|
||||
|
||||
- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
|
||||
39
ultralytics/cfg/models/v10/yolov10s.yaml
Normal file
39
ultralytics/cfg/models/v10/yolov10s.yaml
Normal file
@ -0,0 +1,39 @@
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
s: [0.33, 0.50, 1024]
|
||||
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2fCIB, [1024, True, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, PSA, [1024]] # 10
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 13
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 13], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 19 (P4/16-medium)
|
||||
|
||||
- [-1, 1, SCDown, [512, 3, 2]]
|
||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2fCIB, [1024, True, True]] # 22 (P5/32-large)
|
||||
|
||||
- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
|
||||
40
ultralytics/cfg/models/v10/yolov10x.yaml
Normal file
40
ultralytics/cfg/models/v10/yolov10x.yaml
Normal file
@ -0,0 +1,40 @@
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
x: [1.00, 1.25, 512]
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2fCIB, [512, True]]
|
||||
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2fCIB, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, PSA, [1024]] # 10
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2fCIB, [512, True]] # 13
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 13], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
|
||||
|
||||
- [-1, 1, SCDown, [512, 3, 2]]
|
||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
|
||||
|
||||
- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
|
||||
@ -2,47 +2,45 @@
|
||||
# YOLOv3-SPP object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/yolov3
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
|
||||
# darknet53 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [32, 3, 1]], # 0
|
||||
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
|
||||
[-1, 1, Bottleneck, [64]],
|
||||
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
|
||||
[-1, 2, Bottleneck, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8
|
||||
[-1, 8, Bottleneck, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16
|
||||
[-1, 8, Bottleneck, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
|
||||
[-1, 4, Bottleneck, [1024]], # 10
|
||||
]
|
||||
- [-1, 1, Conv, [32, 3, 1]] # 0
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 1-P1/2
|
||||
- [-1, 1, Bottleneck, [64]]
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 3-P2/4
|
||||
- [-1, 2, Bottleneck, [128]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 5-P3/8
|
||||
- [-1, 8, Bottleneck, [256]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 7-P4/16
|
||||
- [-1, 8, Bottleneck, [512]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 9-P5/32
|
||||
- [-1, 4, Bottleneck, [1024]] # 10
|
||||
|
||||
# YOLOv3-SPP head
|
||||
head:
|
||||
[[-1, 1, Bottleneck, [1024, False]],
|
||||
[-1, 1, SPP, [512, [5, 9, 13]]],
|
||||
[-1, 1, Conv, [1024, 3, 1]],
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)
|
||||
- [-1, 1, Bottleneck, [1024, False]]
|
||||
- [-1, 1, SPP, [512, [5, 9, 13]]]
|
||||
- [-1, 1, Conv, [1024, 3, 1]]
|
||||
- [-1, 1, Conv, [512, 1, 1]]
|
||||
- [-1, 1, Conv, [1024, 3, 1]] # 15 (P5/32-large)
|
||||
|
||||
[-2, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 1, Bottleneck, [512, False]],
|
||||
[-1, 1, Bottleneck, [512, False]],
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)
|
||||
- [-2, 1, Conv, [256, 1, 1]]
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 1, Bottleneck, [512, False]]
|
||||
- [-1, 1, Bottleneck, [512, False]]
|
||||
- [-1, 1, Conv, [256, 1, 1]]
|
||||
- [-1, 1, Conv, [512, 3, 1]] # 22 (P4/16-medium)
|
||||
|
||||
[-2, 1, Conv, [128, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 1, Bottleneck, [256, False]],
|
||||
[-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)
|
||||
- [-2, 1, Conv, [128, 1, 1]]
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 1, Bottleneck, [256, False]]
|
||||
- [-1, 2, Bottleneck, [256, False]] # 27 (P3/8-small)
|
||||
|
||||
[[27, 22, 15], 1, Detect, [nc]], # Detect(P3, P4, P5)
|
||||
]
|
||||
- [[27, 22, 15], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
||||
|
||||
@ -2,38 +2,36 @@
|
||||
# YOLOv3-tiny object detection model with P4-P5 outputs. For details see https://docs.ultralytics.com/models/yolov3
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
|
||||
# YOLOv3-tiny backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [16, 3, 1]], # 0
|
||||
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2
|
||||
[-1, 1, Conv, [32, 3, 1]],
|
||||
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4
|
||||
[-1, 1, Conv, [64, 3, 1]],
|
||||
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8
|
||||
[-1, 1, Conv, [128, 3, 1]],
|
||||
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16
|
||||
[-1, 1, Conv, [256, 3, 1]],
|
||||
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32
|
||||
[-1, 1, Conv, [512, 3, 1]],
|
||||
[-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11
|
||||
[-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12
|
||||
]
|
||||
- [-1, 1, Conv, [16, 3, 1]] # 0
|
||||
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 1-P1/2
|
||||
- [-1, 1, Conv, [32, 3, 1]]
|
||||
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 3-P2/4
|
||||
- [-1, 1, Conv, [64, 3, 1]]
|
||||
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 5-P3/8
|
||||
- [-1, 1, Conv, [128, 3, 1]]
|
||||
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 7-P4/16
|
||||
- [-1, 1, Conv, [256, 3, 1]]
|
||||
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 9-P5/32
|
||||
- [-1, 1, Conv, [512, 3, 1]]
|
||||
- [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]] # 11
|
||||
- [-1, 1, nn.MaxPool2d, [2, 1, 0]] # 12
|
||||
|
||||
# YOLOv3-tiny head
|
||||
head:
|
||||
[[-1, 1, Conv, [1024, 3, 1]],
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large)
|
||||
- [-1, 1, Conv, [1024, 3, 1]]
|
||||
- [-1, 1, Conv, [256, 1, 1]]
|
||||
- [-1, 1, Conv, [512, 3, 1]] # 15 (P5/32-large)
|
||||
|
||||
[-2, 1, Conv, [128, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium)
|
||||
- [-2, 1, Conv, [128, 1, 1]]
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 1, Conv, [256, 3, 1]] # 19 (P4/16-medium)
|
||||
|
||||
[[19, 15], 1, Detect, [nc]], # Detect(P4, P5)
|
||||
]
|
||||
- [[19, 15], 1, Detect, [nc]] # Detect(P4, P5)
|
||||
|
||||
@ -2,47 +2,45 @@
|
||||
# YOLOv3 object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/yolov3
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
|
||||
# darknet53 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [32, 3, 1]], # 0
|
||||
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
|
||||
[-1, 1, Bottleneck, [64]],
|
||||
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
|
||||
[-1, 2, Bottleneck, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8
|
||||
[-1, 8, Bottleneck, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16
|
||||
[-1, 8, Bottleneck, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
|
||||
[-1, 4, Bottleneck, [1024]], # 10
|
||||
]
|
||||
- [-1, 1, Conv, [32, 3, 1]] # 0
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 1-P1/2
|
||||
- [-1, 1, Bottleneck, [64]]
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 3-P2/4
|
||||
- [-1, 2, Bottleneck, [128]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 5-P3/8
|
||||
- [-1, 8, Bottleneck, [256]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 7-P4/16
|
||||
- [-1, 8, Bottleneck, [512]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 9-P5/32
|
||||
- [-1, 4, Bottleneck, [1024]] # 10
|
||||
|
||||
# YOLOv3 head
|
||||
head:
|
||||
[[-1, 1, Bottleneck, [1024, False]],
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, Conv, [1024, 3, 1]],
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)
|
||||
- [-1, 1, Bottleneck, [1024, False]]
|
||||
- [-1, 1, Conv, [512, 1, 1]]
|
||||
- [-1, 1, Conv, [1024, 3, 1]]
|
||||
- [-1, 1, Conv, [512, 1, 1]]
|
||||
- [-1, 1, Conv, [1024, 3, 1]] # 15 (P5/32-large)
|
||||
|
||||
[-2, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 1, Bottleneck, [512, False]],
|
||||
[-1, 1, Bottleneck, [512, False]],
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)
|
||||
- [-2, 1, Conv, [256, 1, 1]]
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 1, Bottleneck, [512, False]]
|
||||
- [-1, 1, Bottleneck, [512, False]]
|
||||
- [-1, 1, Conv, [256, 1, 1]]
|
||||
- [-1, 1, Conv, [512, 3, 1]] # 22 (P4/16-medium)
|
||||
|
||||
[-2, 1, Conv, [128, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 1, Bottleneck, [256, False]],
|
||||
[-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)
|
||||
- [-2, 1, Conv, [128, 1, 1]]
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 1, Bottleneck, [256, False]]
|
||||
- [-1, 2, Bottleneck, [256, False]] # 27 (P3/8-small)
|
||||
|
||||
[[27, 22, 15], 1, Detect, [nc]], # Detect(P3, P4, P5)
|
||||
]
|
||||
- [[27, 22, 15], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
# YOLOv5 object detection model with P3-P6 outputs. For details see https://docs.ultralytics.com/models/yolov5
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov5n-p6.yaml' will call yolov5-p6.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
@ -14,48 +14,46 @@ scales: # model compound scaling constants, i.e. 'model=yolov5n-p6.yaml' will ca
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [768]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 11
|
||||
]
|
||||
- [-1, 1, Conv, [64, 6, 2, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C3, [128]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C3, [256]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 9, C3, [512]]
|
||||
- [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C3, [768]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
|
||||
- [-1, 3, C3, [1024]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 11
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head:
|
||||
[[-1, 1, Conv, [768, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P5
|
||||
[-1, 3, C3, [768, False]], # 15
|
||||
- [-1, 1, Conv, [768, 1, 1]]
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
||||
- [-1, 3, C3, [768, False]] # 15
|
||||
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 19
|
||||
- [-1, 1, Conv, [512, 1, 1]]
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C3, [512, False]] # 19
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 23 (P3/8-small)
|
||||
- [-1, 1, Conv, [256, 1, 1]]
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C3, [256, False]] # 23 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 20], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 26 (P4/16-medium)
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 20], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C3, [512, False]] # 26 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 16], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [768, False]], # 29 (P5/32-large)
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 16], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C3, [768, False]] # 29 (P5/32-large)
|
||||
|
||||
[-1, 1, Conv, [768, 3, 2]],
|
||||
[[-1, 12], 1, Concat, [1]], # cat head P6
|
||||
[-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
|
||||
- [-1, 1, Conv, [768, 3, 2]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P6
|
||||
- [-1, 3, C3, [1024, False]] # 32 (P6/64-xlarge)
|
||||
|
||||
[[23, 26, 29, 32], 1, Detect, [nc]], # Detect(P3, P4, P5, P6)
|
||||
]
|
||||
- [[23, 26, 29, 32], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
# YOLOv5 object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/yolov5
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov5n.yaml' will call yolov5.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
@ -14,37 +14,35 @@ scales: # model compound scaling constants, i.e. 'model=yolov5n.yaml' will call
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
- [-1, 1, Conv, [64, 6, 2, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C3, [128]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C3, [256]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 9, C3, [512]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C3, [1024]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head:
|
||||
[[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
- [-1, 1, Conv, [512, 1, 1]]
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C3, [512, False]] # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
- [-1, 1, Conv, [256, 1, 1]]
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C3, [256, False]] # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 14], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C3, [512, False]] # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C3, [1024, False]] # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc]], # Detect(P3, P4, P5)
|
||||
]
|
||||
- [[17, 20, 23], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
||||
|
||||
@ -2,8 +2,8 @@
|
||||
# YOLOv6 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/models/yolov6
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
activation: nn.ReLU() # (optional) model default activation function
|
||||
nc: 80 # number of classes
|
||||
activation: nn.ReLU() # (optional) model default activation function
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov6n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
@ -15,39 +15,39 @@ scales: # model compound scaling constants, i.e. 'model=yolov6n.yaml' will call
|
||||
# YOLOv6-3.0s backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 6, Conv, [128, 3, 1]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 12, Conv, [256, 3, 1]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 18, Conv, [512, 3, 1]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 6, Conv, [1024, 3, 1]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv6-3.0s head
|
||||
head:
|
||||
- [-1, 1, Conv, [256, 1, 1]]
|
||||
- [-1, 1, nn.ConvTranspose2d, [256, 2, 2, 0]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 1, Conv, [256, 3, 1]]
|
||||
- [-1, 9, Conv, [256, 3, 1]] # 14
|
||||
- [-1, 9, Conv, [256, 3, 1]] # 14
|
||||
|
||||
- [-1, 1, Conv, [128, 1, 1]]
|
||||
- [-1, 1, nn.ConvTranspose2d, [128, 2, 2, 0]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 1, Conv, [128, 3, 1]]
|
||||
- [-1, 9, Conv, [128, 3, 1]] # 19
|
||||
- [-1, 9, Conv, [128, 3, 1]] # 19
|
||||
|
||||
- [-1, 1, Conv, [128, 3, 2]]
|
||||
- [[-1, 15], 1, Concat, [1]] # cat head P4
|
||||
- [[-1, 15], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 1, Conv, [256, 3, 1]]
|
||||
- [-1, 9, Conv, [256, 3, 1]] # 23
|
||||
- [-1, 9, Conv, [256, 3, 1]] # 23
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 1, Conv, [512, 3, 1]]
|
||||
- [-1, 9, Conv, [512, 3, 1]] # 27
|
||||
- [-1, 9, Conv, [512, 3, 1]] # 27
|
||||
|
||||
- [[19, 23, 27], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
||||
- [[19, 23, 27], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
||||
|
||||
25
ultralytics/cfg/models/v8/yolov8-cls-resnet101.yaml
Normal file
25
ultralytics/cfg/models/v8/yolov8-cls-resnet101.yaml
Normal file
@ -0,0 +1,25 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8-cls image classification model. For Usage examples see https://docs.ultralytics.com/tasks/classify
|
||||
|
||||
# Parameters
|
||||
nc: 1000 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
s: [0.33, 0.50, 1024]
|
||||
m: [0.67, 0.75, 1024]
|
||||
l: [1.00, 1.00, 1024]
|
||||
x: [1.00, 1.25, 1024]
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0-P1/2
|
||||
- [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1-P2/4
|
||||
- [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2-P3/8
|
||||
- [-1, 1, ResNetLayer, [512, 256, 2, False, 23]] # 3-P4/16
|
||||
- [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4-P5/32
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, Classify, [nc]] # Classify
|
||||
25
ultralytics/cfg/models/v8/yolov8-cls-resnet50.yaml
Normal file
25
ultralytics/cfg/models/v8/yolov8-cls-resnet50.yaml
Normal file
@ -0,0 +1,25 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8-cls image classification model. For Usage examples see https://docs.ultralytics.com/tasks/classify
|
||||
|
||||
# Parameters
|
||||
nc: 1000 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
s: [0.33, 0.50, 1024]
|
||||
m: [0.67, 0.75, 1024]
|
||||
l: [1.00, 1.00, 1024]
|
||||
x: [1.00, 1.25, 1024]
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0-P1/2
|
||||
- [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1-P2/4
|
||||
- [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2-P3/8
|
||||
- [-1, 1, ResNetLayer, [512, 256, 2, False, 6]] # 3-P4/16
|
||||
- [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4-P5/32
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, Classify, [nc]] # Classify
|
||||
@ -2,7 +2,7 @@
|
||||
# YOLOv8-cls image classification model. For Usage examples see https://docs.ultralytics.com/tasks/classify
|
||||
|
||||
# Parameters
|
||||
nc: 1000 # number of classes
|
||||
nc: 1000 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
@ -14,16 +14,16 @@ scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will c
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, Classify, [nc]] # Classify
|
||||
- [-1, 1, Classify, [nc]] # Classify
|
||||
|
||||
54
ultralytics/cfg/models/v8/yolov8-ghost-p2.yaml
Normal file
54
ultralytics/cfg/models/v8/yolov8-ghost-p2.yaml
Normal file
@ -0,0 +1,54 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8 object detection model with P2-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024] # YOLOv8n-ghost-p2 summary: 491 layers, 2033944 parameters, 2033928 gradients, 13.8 GFLOPs
|
||||
s: [0.33, 0.50, 1024] # YOLOv8s-ghost-p2 summary: 491 layers, 5562080 parameters, 5562064 gradients, 25.1 GFLOPs
|
||||
m: [0.67, 0.75, 768] # YOLOv8m-ghost-p2 summary: 731 layers, 9031728 parameters, 9031712 gradients, 42.8 GFLOPs
|
||||
l: [1.00, 1.00, 512] # YOLOv8l-ghost-p2 summary: 971 layers, 12214448 parameters, 12214432 gradients, 69.1 GFLOPs
|
||||
x: [1.00, 1.25, 512] # YOLOv8x-ghost-p2 summary: 971 layers, 18664776 parameters, 18664760 gradients, 103.3 GFLOPs
|
||||
|
||||
# YOLOv8.0-ghost backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, GhostConv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C3Ghost, [128, True]]
|
||||
- [-1, 1, GhostConv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C3Ghost, [256, True]]
|
||||
- [-1, 1, GhostConv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C3Ghost, [512, True]]
|
||||
- [-1, 1, GhostConv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C3Ghost, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv8.0-ghost-p2 head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C3Ghost, [512]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C3Ghost, [256]] # 15 (P3/8-small)
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 2], 1, Concat, [1]] # cat backbone P2
|
||||
- [-1, 3, C3Ghost, [128]] # 18 (P2/4-xsmall)
|
||||
|
||||
- [-1, 1, GhostConv, [128, 3, 2]]
|
||||
- [[-1, 15], 1, Concat, [1]] # cat head P3
|
||||
- [-1, 3, C3Ghost, [256]] # 21 (P3/8-small)
|
||||
|
||||
- [-1, 1, GhostConv, [256, 3, 2]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C3Ghost, [512]] # 24 (P4/16-medium)
|
||||
|
||||
- [-1, 1, GhostConv, [512, 3, 2]]
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C3Ghost, [1024]] # 27 (P5/32-large)
|
||||
|
||||
- [[18, 21, 24, 27], 1, Detect, [nc]] # Detect(P2, P3, P4, P5)
|
||||
56
ultralytics/cfg/models/v8/yolov8-ghost-p6.yaml
Normal file
56
ultralytics/cfg/models/v8/yolov8-ghost-p6.yaml
Normal file
@ -0,0 +1,56 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8 object detection model with P3-P6 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024] # YOLOv8n-ghost-p6 summary: 529 layers, 2901100 parameters, 2901084 gradients, 5.8 GFLOPs
|
||||
s: [0.33, 0.50, 1024] # YOLOv8s-ghost-p6 summary: 529 layers, 9520008 parameters, 9519992 gradients, 16.4 GFLOPs
|
||||
m: [0.67, 0.75, 768] # YOLOv8m-ghost-p6 summary: 789 layers, 18002904 parameters, 18002888 gradients, 34.4 GFLOPs
|
||||
l: [1.00, 1.00, 512] # YOLOv8l-ghost-p6 summary: 1049 layers, 21227584 parameters, 21227568 gradients, 55.3 GFLOPs
|
||||
x: [1.00, 1.25, 512] # YOLOv8x-ghost-p6 summary: 1049 layers, 33057852 parameters, 33057836 gradients, 85.7 GFLOPs
|
||||
|
||||
# YOLOv8.0-ghost backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, GhostConv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C3Ghost, [128, True]]
|
||||
- [-1, 1, GhostConv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C3Ghost, [256, True]]
|
||||
- [-1, 1, GhostConv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C3Ghost, [512, True]]
|
||||
- [-1, 1, GhostConv, [768, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C3Ghost, [768, True]]
|
||||
- [-1, 1, GhostConv, [1024, 3, 2]] # 9-P6/64
|
||||
- [-1, 3, C3Ghost, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 11
|
||||
|
||||
# YOLOv8.0-ghost-p6 head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
||||
- [-1, 3, C3Ghost, [768]] # 14
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C3Ghost, [512]] # 17
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C3Ghost, [256]] # 20 (P3/8-small)
|
||||
|
||||
- [-1, 1, GhostConv, [256, 3, 2]]
|
||||
- [[-1, 17], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C3Ghost, [512]] # 23 (P4/16-medium)
|
||||
|
||||
- [-1, 1, GhostConv, [512, 3, 2]]
|
||||
- [[-1, 14], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C3Ghost, [768]] # 26 (P5/32-large)
|
||||
|
||||
- [-1, 1, GhostConv, [768, 3, 2]]
|
||||
- [[-1, 11], 1, Concat, [1]] # cat head P6
|
||||
- [-1, 3, C3Ghost, [1024]] # 29 (P6/64-xlarge)
|
||||
|
||||
- [[20, 23, 26, 29], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)
|
||||
47
ultralytics/cfg/models/v8/yolov8-ghost.yaml
Normal file
47
ultralytics/cfg/models/v8/yolov8-ghost.yaml
Normal file
@ -0,0 +1,47 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||
# Employs Ghost convolutions and modules proposed in Huawei's GhostNet in https://arxiv.org/abs/1911.11907v2
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024] # YOLOv8n-ghost summary: 403 layers, 1865316 parameters, 1865300 gradients, 5.8 GFLOPs
|
||||
s: [0.33, 0.50, 1024] # YOLOv8s-ghost summary: 403 layers, 5960072 parameters, 5960056 gradients, 16.4 GFLOPs
|
||||
m: [0.67, 0.75, 768] # YOLOv8m-ghost summary: 603 layers, 10336312 parameters, 10336296 gradients, 32.7 GFLOPs
|
||||
l: [1.00, 1.00, 512] # YOLOv8l-ghost summary: 803 layers, 14277872 parameters, 14277856 gradients, 53.7 GFLOPs
|
||||
x: [1.00, 1.25, 512] # YOLOv8x-ghost summary: 803 layers, 22229308 parameters, 22229292 gradients, 83.3 GFLOPs
|
||||
|
||||
# YOLOv8.0n-ghost backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, GhostConv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C3Ghost, [128, True]]
|
||||
- [-1, 1, GhostConv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C3Ghost, [256, True]]
|
||||
- [-1, 1, GhostConv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C3Ghost, [512, True]]
|
||||
- [-1, 1, GhostConv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C3Ghost, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C3Ghost, [512]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C3Ghost, [256]] # 15 (P3/8-small)
|
||||
|
||||
- [-1, 1, GhostConv, [256, 3, 2]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C3Ghost, [512]] # 18 (P4/16-medium)
|
||||
|
||||
- [-1, 1, GhostConv, [512, 3, 2]]
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C3Ghost, [1024]] # 21 (P5/32-large)
|
||||
|
||||
- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
||||
46
ultralytics/cfg/models/v8/yolov8-obb.yaml
Normal file
46
ultralytics/cfg/models/v8/yolov8-obb.yaml
Normal file
@ -0,0 +1,46 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8 Oriented Bounding Boxes (OBB) model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
|
||||
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
|
||||
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
|
||||
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
|
||||
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
||||
|
||||
- [[15, 18, 21], 1, OBB, [nc, 1]] # OBB(P3, P4, P5)
|
||||
@ -2,7 +2,7 @@
|
||||
# YOLOv8 object detection model with P2-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
@ -14,41 +14,41 @@ scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call
|
||||
# YOLOv8.0 backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv8.0-p2 head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 12
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 2], 1, Concat, [1]] # cat backbone P2
|
||||
- [-1, 3, C2f, [128]] # 18 (P2/4-xsmall)
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 2], 1, Concat, [1]] # cat backbone P2
|
||||
- [-1, 3, C2f, [128]] # 18 (P2/4-xsmall)
|
||||
|
||||
- [-1, 1, Conv, [128, 3, 2]]
|
||||
- [[-1, 15], 1, Concat, [1]] # cat head P3
|
||||
- [-1, 3, C2f, [256]] # 21 (P3/8-small)
|
||||
- [[-1, 15], 1, Concat, [1]] # cat head P3
|
||||
- [-1, 3, C2f, [256]] # 21 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 24 (P4/16-medium)
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 24 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2f, [1024]] # 27 (P5/32-large)
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2f, [1024]] # 27 (P5/32-large)
|
||||
|
||||
- [[18, 21, 24, 27], 1, Detect, [nc]] # Detect(P2, P3, P4, P5)
|
||||
- [[18, 21, 24, 27], 1, Detect, [nc]] # Detect(P2, P3, P4, P5)
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
# YOLOv8 object detection model with P3-P6 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
@ -14,43 +14,43 @@ scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will ca
|
||||
# YOLOv8.0x6 backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
|
||||
- [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [768, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 11
|
||||
- [-1, 1, SPPF, [1024, 5]] # 11
|
||||
|
||||
# YOLOv8.0x6 head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
||||
- [-1, 3, C2, [768, False]] # 14
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
||||
- [-1, 3, C2, [768, False]] # 14
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2, [512, False]] # 17
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2, [512, False]] # 17
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2, [256, False]] # 20 (P3/8-small)
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2, [256, False]] # 20 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 17], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
|
||||
- [[-1, 17], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 14], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2, [768, False]] # 26 (P5/32-large)
|
||||
- [[-1, 14], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2, [768, False]] # 26 (P5/32-large)
|
||||
|
||||
- [-1, 1, Conv, [768, 3, 2]]
|
||||
- [[-1, 11], 1, Concat, [1]] # cat head P6
|
||||
- [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
|
||||
- [[-1, 11], 1, Concat, [1]] # cat head P6
|
||||
- [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
|
||||
|
||||
- [[20, 23, 26, 29], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)
|
||||
- [[20, 23, 26, 29], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)
|
||||
|
||||
@ -2,8 +2,8 @@
|
||||
# YOLOv8-pose-p6 keypoints/pose estimation model. For Usage examples see https://docs.ultralytics.com/tasks/pose
|
||||
|
||||
# Parameters
|
||||
nc: 1 # number of classes
|
||||
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
||||
nc: 1 # number of classes
|
||||
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
@ -15,43 +15,43 @@ scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will ca
|
||||
# YOLOv8.0x6 backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
|
||||
- [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [768, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 11
|
||||
- [-1, 1, SPPF, [1024, 5]] # 11
|
||||
|
||||
# YOLOv8.0x6 head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
||||
- [-1, 3, C2, [768, False]] # 14
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
||||
- [-1, 3, C2, [768, False]] # 14
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2, [512, False]] # 17
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2, [512, False]] # 17
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2, [256, False]] # 20 (P3/8-small)
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2, [256, False]] # 20 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 17], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
|
||||
- [[-1, 17], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 14], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2, [768, False]] # 26 (P5/32-large)
|
||||
- [[-1, 14], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2, [768, False]] # 26 (P5/32-large)
|
||||
|
||||
- [-1, 1, Conv, [768, 3, 2]]
|
||||
- [[-1, 11], 1, Concat, [1]] # cat head P6
|
||||
- [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
|
||||
- [[-1, 11], 1, Concat, [1]] # cat head P6
|
||||
- [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
|
||||
|
||||
- [[20, 23, 26, 29], 1, Pose, [nc, kpt_shape]] # Pose(P3, P4, P5, P6)
|
||||
- [[20, 23, 26, 29], 1, Pose, [nc, kpt_shape]] # Pose(P3, P4, P5, P6)
|
||||
|
||||
@ -2,8 +2,8 @@
|
||||
# YOLOv8-pose keypoints/pose estimation model. For Usage examples see https://docs.ultralytics.com/tasks/pose
|
||||
|
||||
# Parameters
|
||||
nc: 1 # number of classes
|
||||
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
||||
nc: 1 # number of classes
|
||||
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-pose.yaml' will call yolov8-pose.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
@ -15,33 +15,33 @@ scales: # model compound scaling constants, i.e. 'model=yolov8n-pose.yaml' will
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 12
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
||||
|
||||
- [[15, 18, 21], 1, Pose, [nc, kpt_shape]] # Pose(P3, P4, P5)
|
||||
- [[15, 18, 21], 1, Pose, [nc, kpt_shape]] # Pose(P3, P4, P5)
|
||||
|
||||
@ -2,45 +2,45 @@
|
||||
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
|
||||
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
|
||||
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
|
||||
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
|
||||
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
|
||||
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
|
||||
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
|
||||
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
|
||||
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
|
||||
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 12
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
||||
|
||||
- [[15, 18, 21], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
||||
- [[15, 18, 21], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
# YOLOv8-seg-p6 instance segmentation model. For Usage examples see https://docs.ultralytics.com/tasks/segment
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-seg-p6.yaml' will call yolov8-seg-p6.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
@ -14,43 +14,43 @@ scales: # model compound scaling constants, i.e. 'model=yolov8n-seg-p6.yaml' wil
|
||||
# YOLOv8.0x6 backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
|
||||
- [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [768, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 11
|
||||
- [-1, 1, SPPF, [1024, 5]] # 11
|
||||
|
||||
# YOLOv8.0x6 head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
||||
- [-1, 3, C2, [768, False]] # 14
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
||||
- [-1, 3, C2, [768, False]] # 14
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2, [512, False]] # 17
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2, [512, False]] # 17
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2, [256, False]] # 20 (P3/8-small)
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2, [256, False]] # 20 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 17], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
|
||||
- [[-1, 17], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 14], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2, [768, False]] # 26 (P5/32-large)
|
||||
- [[-1, 14], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2, [768, False]] # 26 (P5/32-large)
|
||||
|
||||
- [-1, 1, Conv, [768, 3, 2]]
|
||||
- [[-1, 11], 1, Concat, [1]] # cat head P6
|
||||
- [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
|
||||
- [[-1, 11], 1, Concat, [1]] # cat head P6
|
||||
- [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
|
||||
|
||||
- [[20, 23, 26, 29], 1, Segment, [nc, 32, 256]] # Pose(P3, P4, P5, P6)
|
||||
- [[20, 23, 26, 29], 1, Segment, [nc, 32, 256]] # Pose(P3, P4, P5, P6)
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
# YOLOv8-seg instance segmentation model. For Usage examples see https://docs.ultralytics.com/tasks/segment
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-seg.yaml' will call yolov8-seg.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
@ -14,33 +14,33 @@ scales: # model compound scaling constants, i.e. 'model=yolov8n-seg.yaml' will c
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 12
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
||||
|
||||
- [[15, 18, 21], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5)
|
||||
- [[15, 18, 21], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5)
|
||||
|
||||
48
ultralytics/cfg/models/v8/yolov8-world.yaml
Normal file
48
ultralytics/cfg/models/v8/yolov8-world.yaml
Normal file
@ -0,0 +1,48 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8-World object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/tasks/detect
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
|
||||
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
|
||||
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
|
||||
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
|
||||
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2fAttn, [512, 256, 8]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2fAttn, [256, 128, 4]] # 15 (P3/8-small)
|
||||
|
||||
- [[15, 12, 9], 1, ImagePoolingAttn, [256]] # 16 (P3/8-small)
|
||||
|
||||
- [15, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2fAttn, [512, 256, 8]] # 19 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2fAttn, [1024, 512, 16]] # 22 (P5/32-large)
|
||||
|
||||
- [[15, 19, 22], 1, WorldDetect, [nc, 512, False]] # Detect(P3, P4, P5)
|
||||
46
ultralytics/cfg/models/v8/yolov8-worldv2.yaml
Normal file
46
ultralytics/cfg/models/v8/yolov8-worldv2.yaml
Normal file
@ -0,0 +1,46 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8-World-v2 object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/tasks/detect
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
|
||||
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
|
||||
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
|
||||
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
|
||||
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2fAttn, [512, 256, 8]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2fAttn, [256, 128, 4]] # 15 (P3/8-small)
|
||||
|
||||
- [15, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2fAttn, [512, 256, 8]] # 18 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2fAttn, [1024, 512, 16]] # 21 (P5/32-large)
|
||||
|
||||
- [[15, 18, 21], 1, WorldDetect, [nc, 512, True]] # Detect(P3, P4, P5)
|
||||
@ -2,45 +2,45 @@
|
||||
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
|
||||
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
|
||||
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
|
||||
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
|
||||
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
|
||||
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
|
||||
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
|
||||
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
|
||||
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
|
||||
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 12
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
||||
|
||||
- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
||||
- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
||||
|
||||
36
ultralytics/cfg/models/v9/yolov9c.yaml
Normal file
36
ultralytics/cfg/models/v9/yolov9c.yaml
Normal file
@ -0,0 +1,36 @@
|
||||
# YOLOv9
|
||||
|
||||
# parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
# gelan backbone
|
||||
backbone:
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]] # 2
|
||||
- [-1, 1, ADown, [256]] # 3-P3/8
|
||||
- [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]] # 4
|
||||
- [-1, 1, ADown, [512]] # 5-P4/16
|
||||
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 6
|
||||
- [-1, 1, ADown, [512]] # 7-P5/32
|
||||
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 8
|
||||
- [-1, 1, SPPELAN, [512, 256]] # 9
|
||||
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]] # 15 (P3/8-small)
|
||||
|
||||
- [-1, 1, ADown, [256]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 18 (P4/16-medium)
|
||||
|
||||
- [-1, 1, ADown, [512]]
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 21 (P5/32-large)
|
||||
|
||||
- [[15, 18, 21], 1, Detect, [nc]] # DDetect(P3, P4, P5)
|
||||
60
ultralytics/cfg/models/v9/yolov9e.yaml
Normal file
60
ultralytics/cfg/models/v9/yolov9e.yaml
Normal file
@ -0,0 +1,60 @@
|
||||
# YOLOv9
|
||||
|
||||
# parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
# gelan backbone
|
||||
backbone:
|
||||
- [-1, 1, Silence, []]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 1-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 2-P2/4
|
||||
- [-1, 1, RepNCSPELAN4, [256, 128, 64, 2]] # 3
|
||||
- [-1, 1, ADown, [256]] # 4-P3/8
|
||||
- [-1, 1, RepNCSPELAN4, [512, 256, 128, 2]] # 5
|
||||
- [-1, 1, ADown, [512]] # 6-P4/16
|
||||
- [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 7
|
||||
- [-1, 1, ADown, [1024]] # 8-P5/32
|
||||
- [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 9
|
||||
|
||||
- [1, 1, CBLinear, [[64]]] # 10
|
||||
- [3, 1, CBLinear, [[64, 128]]] # 11
|
||||
- [5, 1, CBLinear, [[64, 128, 256]]] # 12
|
||||
- [7, 1, CBLinear, [[64, 128, 256, 512]]] # 13
|
||||
- [9, 1, CBLinear, [[64, 128, 256, 512, 1024]]] # 14
|
||||
|
||||
- [0, 1, Conv, [64, 3, 2]] # 15-P1/2
|
||||
- [[10, 11, 12, 13, 14, -1], 1, CBFuse, [[0, 0, 0, 0, 0]]] # 16
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 17-P2/4
|
||||
- [[11, 12, 13, 14, -1], 1, CBFuse, [[1, 1, 1, 1]]] # 18
|
||||
- [-1, 1, RepNCSPELAN4, [256, 128, 64, 2]] # 19
|
||||
- [-1, 1, ADown, [256]] # 20-P3/8
|
||||
- [[12, 13, 14, -1], 1, CBFuse, [[2, 2, 2]]] # 21
|
||||
- [-1, 1, RepNCSPELAN4, [512, 256, 128, 2]] # 22
|
||||
- [-1, 1, ADown, [512]] # 23-P4/16
|
||||
- [[13, 14, -1], 1, CBFuse, [[3, 3]]] # 24
|
||||
- [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 25
|
||||
- [-1, 1, ADown, [1024]] # 26-P5/32
|
||||
- [[14, -1], 1, CBFuse, [[4]]] # 27
|
||||
- [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 28
|
||||
- [-1, 1, SPPELAN, [512, 256]] # 29
|
||||
|
||||
# gelan head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 25], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 2]] # 32
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 22], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 1, RepNCSPELAN4, [256, 256, 128, 2]] # 35 (P3/8-small)
|
||||
|
||||
- [-1, 1, ADown, [256]]
|
||||
- [[-1, 32], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 2]] # 38 (P4/16-medium)
|
||||
|
||||
- [-1, 1, ADown, [512]]
|
||||
- [[-1, 29], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 1, RepNCSPELAN4, [512, 1024, 512, 2]] # 41 (P5/32-large)
|
||||
|
||||
# detect
|
||||
- [[35, 38, 41], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
||||
@ -1,17 +1,17 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Default YOLO tracker settings for BoT-SORT tracker https://github.com/NirAharon/BoT-SORT
|
||||
|
||||
tracker_type: botsort # tracker type, ['botsort', 'bytetrack']
|
||||
track_high_thresh: 0.5 # threshold for the first association
|
||||
track_low_thresh: 0.1 # threshold for the second association
|
||||
new_track_thresh: 0.6 # threshold for init new track if the detection does not match any tracks
|
||||
track_buffer: 30 # buffer to calculate the time when to remove tracks
|
||||
match_thresh: 0.8 # threshold for matching tracks
|
||||
tracker_type: botsort # tracker type, ['botsort', 'bytetrack']
|
||||
track_high_thresh: 0.5 # threshold for the first association
|
||||
track_low_thresh: 0.1 # threshold for the second association
|
||||
new_track_thresh: 0.6 # threshold for init new track if the detection does not match any tracks
|
||||
track_buffer: 30 # buffer to calculate the time when to remove tracks
|
||||
match_thresh: 0.8 # threshold for matching tracks
|
||||
# min_box_area: 10 # threshold for min box areas(for tracker evaluation, not used for now)
|
||||
# mot20: False # for tracker evaluation(not used for now)
|
||||
|
||||
# BoT-SORT settings
|
||||
gmc_method: sparseOptFlow # method of global motion compensation
|
||||
gmc_method: sparseOptFlow # method of global motion compensation
|
||||
# ReID model related thresh (not supported yet)
|
||||
proximity_thresh: 0.5
|
||||
appearance_thresh: 0.25
|
||||
|
||||
@ -1,11 +1,11 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Default YOLO tracker settings for ByteTrack tracker https://github.com/ifzhang/ByteTrack
|
||||
|
||||
tracker_type: bytetrack # tracker type, ['botsort', 'bytetrack']
|
||||
track_high_thresh: 0.5 # threshold for the first association
|
||||
track_low_thresh: 0.1 # threshold for the second association
|
||||
new_track_thresh: 0.6 # threshold for init new track if the detection does not match any tracks
|
||||
track_buffer: 30 # buffer to calculate the time when to remove tracks
|
||||
match_thresh: 0.8 # threshold for matching tracks
|
||||
tracker_type: bytetrack # tracker type, ['botsort', 'bytetrack']
|
||||
track_high_thresh: 0.5 # threshold for the first association
|
||||
track_low_thresh: 0.1 # threshold for the second association
|
||||
new_track_thresh: 0.6 # threshold for init new track if the detection does not match any tracks
|
||||
track_buffer: 30 # buffer to calculate the time when to remove tracks
|
||||
match_thresh: 0.8 # threshold for matching tracks
|
||||
# min_box_area: 10 # threshold for min box areas(for tracker evaluation, not used for now)
|
||||
# mot20: False # for tracker evaluation(not used for now)
|
||||
|
||||
Reference in New Issue
Block a user