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--- |
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license: mit |
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library_name: pytorch |
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tags: |
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- faster-rcnn |
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- object-detection |
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- computer-vision |
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- pytorch |
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- kitti |
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- autonomous-driving |
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- from-scratch |
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pipeline_tag: object-detection |
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datasets: |
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- kitti |
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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: faster-rcnn-kitti-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: kitti |
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name: KITTI Object Detection |
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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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# Faster R-CNN - KITTI Object Detection Vanilla |
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Faster R-CNN model trained from scratch on KITTI dataset for autonomous driving object detection. |
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## Model Details |
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- **Model Type**: Faster R-CNN Object Detection |
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- **Dataset**: KITTI Object Detection |
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- **Training Method**: trained from scratch |
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- **Framework**: PyTorch |
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- **Task**: Object Detection |
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## Dataset Information |
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This model was trained on the **KITTI Object Detection** dataset, which contains the following object classes: |
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car, pedestrian, cyclist |
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### Dataset-specific Details: |
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**KITTI Object Detection Dataset:** |
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- Real-world autonomous driving dataset |
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- Contains stereo imagery from vehicle-mounted cameras |
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- Focus on cars, pedestrians, and cyclists |
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- Challenging scenarios with varying lighting and weather conditions |
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## Usage |
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This model can be used with PyTorch and common object detection frameworks: |
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```python |
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import torch |
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import torchvision.transforms as transforms |
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from PIL import Image |
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# Load the model (example using torchvision) |
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model = torch.load('path/to/model.pth') |
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model.eval() |
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# Prepare your image |
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transform = transforms.Compose([ |
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transforms.ToTensor(), |
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]) |
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image = Image.open('path/to/image.jpg') |
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image_tensor = transform(image).unsqueeze(0) |
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# Run inference |
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with torch.no_grad(): |
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predictions = model(image_tensor) |
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# Process results |
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boxes = predictions[0]['boxes'] |
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scores = predictions[0]['scores'] |
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labels = predictions[0]['labels'] |
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``` |
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## Model Performance |
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This model was trained from scratch on the KITTI Object Detection dataset using Faster R-CNN architecture. |
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## Architecture |
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**Faster R-CNN** (Region-based Convolutional Neural Network) is a two-stage object detection framework: |
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1. **Region Proposal Network (RPN)**: Generates object proposals |
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2. **Fast R-CNN detector**: Classifies proposals and refines bounding box coordinates |
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Key advantages: |
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- High accuracy object detection |
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- Precise localization |
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- Good performance on small objects |
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- Well-established architecture with extensive research backing |
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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{ren2015faster, |
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title={Faster r-cnn: Towards real-time object detection with region proposal networks}, |
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author={Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian}, |
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journal={Advances in neural information processing systems}, |
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volume={28}, |
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year={2015} |
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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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Faster R-CNN, Object Detection, Computer Vision, KITTI, Autonomous Driving, Deep Learning, Two-Stage Detection |
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