add yolo v10 and modify pipeline
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ultralytics/models/yolov10/card.py
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64
ultralytics/models/yolov10/card.py
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card_template_text = """
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---
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license: agpl-3.0
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library_name: ultralytics
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repo_url: https://github.com/THU-MIG/yolov10
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tags:
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- object-detection
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- computer-vision
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- yolov10
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datasets:
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- detection-datasets/coco
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inference: false
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---
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### Model Description
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[YOLOv10: Real-Time End-to-End Object Detection](https://arxiv.org/abs/2405.14458v1)
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- arXiv: https://arxiv.org/abs/2405.14458v1
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- github: https://github.com/THU-MIG/yolov10
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### Installation
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```
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pip install git+https://github.com/THU-MIG/yolov10.git
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```
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### Training and validation
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```python
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from ultralytics import YOLOv10
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model = YOLOv10.from_pretrained('jameslahm/yolov10n')
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# Training
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model.train(...)
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# after training, one can push to the hub
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model.push_to_hub("your-hf-username/yolov10-finetuned")
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# Validation
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model.val(...)
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```
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### Inference
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Here's an end-to-end example showcasing inference on a cats image:
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```python
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from ultralytics import YOLOv10
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model = YOLOv10.from_pretrained('jameslahm/yolov10n')
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source = 'http://images.cocodataset.org/val2017/000000039769.jpg'
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model.predict(source=source, save=True)
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```
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which shows:
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### BibTeX Entry and Citation Info
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```
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@article{wang2024yolov10,
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title={YOLOv10: Real-Time End-to-End Object Detection},
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author={Wang, Ao and Chen, Hui and Liu, Lihao and Chen, Kai and Lin, Zijia and Han, Jungong and Ding, Guiguang},
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journal={arXiv preprint arXiv:2405.14458},
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year={2024}
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}
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```
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""".strip()
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