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智能秤分析

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lee
2025-08-06 17:03:28 +08:00
parent 54898e30ec
commit 3392d76e38
17 changed files with 572 additions and 54 deletions
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20
configs/compare.yml
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@ -16,24 +16,24 @@ base:
# 模型配置
models:
backbone: 'resnet18'
channel_ratio: 0.75
channel_ratio: 1.0
# 训练参数
training:
epochs: 600 # 总训练轮次
epochs: 400 # 总训练轮次
batch_size: 128 # 批次大小
lr: 0.007 # 初始学习率
lr: 0.01 # 初始学习率
optimizer: "sgd" # 优化器类型
metric: 'arcface' # 损失函数类型（可选：arcface/cosface/sphereface/softmax）
loss: "cross_entropy" # 损失函数类型（可选：cross_entropy/cross_entropy_smooth/center_loss/center_loss_smooth/arcface/cosface/sphereface/softmax）
lr_step: 10 # 学习率调整间隔（epoch）
lr_step: 5 # 学习率调整间隔（epoch）
lr_decay: 0.95 # 学习率衰减率
weight_decay: 0.0005 # 权重衰减
scheduler: "cosine" # 学习率调度器（可选：cosine/cosine_warm/step/None）
scheduler: "step" # 学习率调度器（可选：cosine/cosine_warm/step/None）
num_workers: 32 # 数据加载线程数
checkpoints: "./checkpoints/resnet18_20250717_scale=0.75_nosub/" # 模型保存目录
restore: true
restore_model: "./checkpoints/resnet18_20250716_scale=0.75_nosub/best.pth" # 模型恢复路径
checkpoints: "./checkpoints/resnet18_electornic_20250806/" # 模型保存目录
restore: false
restore_model: "./checkpoints/resnet18_20250717_scale=0.75_nosub/best.pth" # 模型恢复路径
cosine_t_0: 10 # 初始周期长度
cosine_t_mult: 1 # 周期长度倍率
cosine_eta_min: 0.00001 # 最小学习率
@ -49,8 +49,8 @@ data:
train_batch_size: 128 # 训练批次大小
val_batch_size: 128 # 验证批次大小
num_workers: 32 # 数据加载线程数
data_train_dir: "../data_center/contrast_data/v2/train" # 训练数据集根目录
data_val_dir: "../data_center/contrast_data/v2/val" # 验证数据集根目录
data_train_dir: "../data_center/electornic/v1/train" # 训练数据集根目录
data_val_dir: "../data_center/electornic/v1/val" # 验证数据集根目录
transform:
img_size: 224 # 图像尺寸

53
configs/pic_pic_similar.yml Normal file
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@ -0,0 +1,53 @@
# configs/similar_analysis.yml
# 专为模型训练对比设计的配置文件
# 支持对比不同训练策略（如蒸馏vs独立训练）
# 基础配置
base:
experiment_name: "model_comparison" # 实验名称（用于结果保存目录）
device: "cuda" # 训练设备（cuda/cpu）
embedding_size: 256 # 特征维度
pin_memory: true # 是否启用pin_memory
distributed: true # 是否启用分布式训练
# 模型配置
models:
backbone: 'resnet18'
channel_ratio: 0.75
model_path: "../checkpoints/resnet18_1009/best.pth"
heatmap:
feature_layer: "layer4"
show_heatmap: true
# 数据配置
data:
dataset: "imagenet" # 数据集名称（示例用，可替换为实际数据集）
train_batch_size: 128 # 训练批次大小
val_batch_size: 8 # 验证批次大小
num_workers: 32 # 数据加载线程数
data_dir: "/home/lc/data_center/image_analysis/pic_pic_similar_maxtrix"
image_joint_pth: "/home/lc/data_center/image_analysis/error_compare_result"
total_pkl: "/home/lc/data_center/image_analysis/pic_pic_similar_maxtrix/total.pkl"
result_txt: "/home/lc/data_center/image_analysis/pic_pic_similar_maxtrix/result.txt"
transform:
img_size: 224 # 图像尺寸
img_mean: 0.5 # 图像均值
img_std: 0.5 # 图像方差
RandomHorizontalFlip: 0.5 # 随机水平翻转概率
RandomRotation: 180 # 随机旋转角度
ColorJitter: 0.5 # 随机颜色抖动强度
# 日志与监控
logging:
logging_dir: "./logs/resnet18_scale=0.75_nosub_log" # 日志保存目录
tensorboard: true # 是否启用TensorBoard
checkpoint_interval: 30 # 检查点保存间隔（epoch）
event:
oneToOne_max_th: 0.9
oneToSn_min_th: 0.6
event_save_dir: "/home/lc/works/realtime_yolov10s/online_yolov10s_resnetv11_20250702/yolos_tracking"
stdlib_image_path: "/testDataAndLogs/module_test_record/comparison/标准图测试数据/pic/stlib_base"
pickle_path: "event.pickle"

8
configs/scatter_data.yml
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@ -4,10 +4,10 @@
# 数据配置
data:
dataset: "imagenet" # 数据集名称（示例用，可替换为实际数据集）
source_dir: "../../data_center/scatter/v5/source" # 原始数据
train_dir: "../../data_center/scatter/v5/train" # 训练数据集根目录
val_dir: "../../data_center/scatter/v5/val" # 验证数据集根目录
extra_dir: "../../data_center/scatter/v5/extra" # 验证数据集根目录
source_dir: "../../data_center/electornic/source" # 原始数据
train_dir: "../../data_center/electornic/v1/train" # 训练数据集根目录
val_dir: "../../data_center/electornic/v1/val" # 验证数据集根目录
extra_dir: "../../data_center/electornic/v1/extra" # 验证数据集根目录
split_ratio: 0.9
max_files: 10 # 数据集小于该阈值则归纳至extra

12
configs/similar_analysis.yml
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@ -43,7 +43,11 @@ logging:
tensorboard: true # 是否启用TensorBoard
checkpoint_interval: 30 # 检查点保存间隔（epoch）
# 分布式训练（可选）
distributed:
enabled: false # 是否启用分布式训练
backend: "nccl" # 分布式后端（nccl/gloo）
event:
oneToOneTxt: "/home/lc/detecttracking/oneToOne.txt"
oneToSnTxt: "/home/lc/detecttracking/oneToSn.txt"
oneToOne_max_th: 0.9
oneToSn_min_th: 0.6
event_save_dir: "/home/lc/works/realtime_yolov10s/online_yolov10s_resnetv11_20250702/yolos_tracking"
stdlib_image_path: "/testDataAndLogs/module_test_record/comparison/标准图测试数据/pic/stlib_base"
pickle_path: "event.pickle"

10
configs/test.yml
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@ -13,8 +13,10 @@ base:
# 模型配置
models:
backbone: 'resnet18'
channel_ratio: 1.0
model_path: "checkpoints/resnet18_scatter_7.3/best.pth"
channel_ratio: 0.75
model_path: "checkpoints/resnet18_1009/best.pth"
#resnet18_20250715_scale=0.75_sub
#resnet18_20250718_scale=0.75_nosub
half: false # 是否启用半精度测试（fp16）
contrast_learning: false
@ -22,9 +24,9 @@ models:
data:
test_batch_size: 128 # 训练批次大小
num_workers: 32 # 数据加载线程数
test_dir: "../data_center/scatter/v4/val" # 验证数据集根目录
test_dir: "../data_center/contrast_data/v1/extra" # 验证数据集根目录
test_group_json: "../data_center/contrast_learning/model_test_data/test/inner_group_pairs.json"
test_list: "../data_center/scatter/v4/standard_cross_same.txt"
test_list: "../data_center/contrast_data/v1/extra_cross_same.txt"
group_test: false
save_image_joint: true
image_joint_pth: "./joint_images"

4
configs/write_feature.yml
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@ -14,7 +14,7 @@ base:
models:
backbone: 'resnet18'
channel_ratio: 0.75
checkpoints: "../checkpoints/resnet18_1009/best.pth"
checkpoints: "../checkpoints/resnet18_20250715_scale=0.75_sub/best.pth"
# 数据配置
data:
@ -42,7 +42,7 @@ logging:
save:
json_bin: "../search_library/yunhedian_05-09.json" # 保存整个json文件
json_path: "/home/lc/data_center/baseStlib/feature_json/stlib_base" # 保存单个json文件路径
json_path: "/home/lc/data_center/baseStlib/feature_json/stlib_base_resnet18_sub" # 保存单个json文件路径
error_barcodes: "error_barcodes.txt"
barcodes_statistics: "../search_library/barcodes_statistics.txt"
create_single_json: true # 是否保存单个json文件