Add comprehensive model card for yolov10-bdd-vanilla
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README.md
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---
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license: mit
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library_name: ultralytics
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tags:
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- yolov10
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- object-detection
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- computer-vision
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- pytorch
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- bdd100k
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- autonomous-driving
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- BDD 100K
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- from-scratch
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pipeline_tag: object-detection
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datasets:
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- bdd100k
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widget:
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- src: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bounding-boxes-sample.png
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example_title: "Sample Image"
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model-index:
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- name: yolov10-bdd-vanilla
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results:
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- task:
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type: object-detection
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dataset:
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type: bdd100k
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name: Berkeley DeepDrive (BDD) 100K
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metrics:
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- type: mean_average_precision
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name: mAP
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value: "TBD"
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---
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# YOLOv10 - Berkeley DeepDrive (BDD) 100K Vanilla
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YOLOv10 model trained from scratch on Berkeley DeepDrive (BDD) 100K dataset for object detection in autonomous driving scenarios.
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## Model Details
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- **Model Type**: YOLOv10 Object Detection
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- **Dataset**: Berkeley DeepDrive (BDD) 100K
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- **Training Method**: trained from scratch
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- **Framework**: PyTorch/Ultralytics
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- **Task**: Object Detection
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## Dataset Information
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This model was trained on the **Berkeley DeepDrive (BDD) 100K** dataset, which contains the following object classes:
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car, truck, bus, motorcycle, bicycle, person, traffic light, traffic sign, train, rider
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### Dataset-specific Details:
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**Berkeley DeepDrive (BDD) 100K Dataset:**
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- 100,000+ driving images with diverse weather and lighting conditions
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- Designed for autonomous driving applications
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- Contains urban driving scenarios from multiple cities
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- Annotations include bounding boxes for vehicles, pedestrians, and traffic elements
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## Usage
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This model can be used with the Ultralytics YOLOv10 framework:
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```python
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from ultralytics import YOLO
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# Load the model
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model = YOLO('path/to/best.pt')
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# Run inference
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results = model('path/to/image.jpg')
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# Process results
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for result in results:
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boxes = result.boxes.xyxy # bounding boxes
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scores = result.boxes.conf # confidence scores
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classes = result.boxes.cls # class predictions
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```
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## Model Performance
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This model was trained from scratch on the Berkeley DeepDrive (BDD) 100K dataset using YOLOv10 architecture.
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## Intended Use
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- **Primary Use**: Object detection in autonomous driving scenarios
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- **Suitable for**: Research, development, and deployment of object detection systems
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- **Limitations**: Performance may vary on images significantly different from the training distribution
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## Citation
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If you use this model, please cite:
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```bibtex
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@article{yolov10,
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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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## License
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This model is released under the MIT License.
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## Keywords
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YOLOv10, Object Detection, Computer Vision, BDD 100K, Autonomous Driving, Deep Learning
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