Add comprehensive model card for faster-rcnn-bdd-vanilla

README.md

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+---
+license: mit
+library_name: pytorch
+tags:
+- faster-rcnn
+- object-detection
+- computer-vision
+- pytorch
+- bdd100k
+- autonomous-driving
+- BDD 100K
+- from-scratch
+pipeline_tag: object-detection
+datasets:
+- bdd100k
+widget:
+- src: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bounding-boxes-sample.png
+  example_title: "Sample Image"
+model-index:
+- name: faster-rcnn-bdd-vanilla
+  results:
+  - task:
+      type: object-detection
+    dataset:
+      type: bdd100k
+      name: Berkeley DeepDrive (BDD) 100K
+    metrics:
+    - type: mean_average_precision
+      name: mAP
+      value: "TBD"
+---
+
+# Faster R-CNN - Berkeley DeepDrive (BDD) 100K Vanilla
+
+Faster R-CNN model trained from scratch on Berkeley DeepDrive (BDD) 100K dataset for object detection in autonomous driving scenarios.
+
+## Model Details
+
+- **Model Type**: Faster R-CNN Object Detection
+- **Dataset**: Berkeley DeepDrive (BDD) 100K
+- **Training Method**: trained from scratch
+- **Framework**: PyTorch
+- **Task**: Object Detection
+
+## Dataset Information
+
+This model was trained on the **Berkeley DeepDrive (BDD) 100K** dataset, which contains the following object classes:
+
+car, truck, bus, motorcycle, bicycle, person, traffic light, traffic sign, train, rider
+
+### Dataset-specific Details:
+
+**Berkeley DeepDrive (BDD) 100K Dataset:**
+- 100,000+ driving images with diverse weather and lighting conditions
+- Designed for autonomous driving applications
+- Contains urban driving scenarios from multiple cities
+- Annotations include bounding boxes for vehicles, pedestrians, and traffic elements
+
+## Usage
+
+This model can be used with PyTorch and common object detection frameworks:
+
+```python
+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+
+# Load the model (example using torchvision)
+model = torch.load('path/to/model.pth')
+model.eval()
+
+# Prepare your image
+transform = transforms.Compose([
+    transforms.ToTensor(),
+])
+
+image = Image.open('path/to/image.jpg')
+image_tensor = transform(image).unsqueeze(0)
+
+# Run inference
+with torch.no_grad():
+    predictions = model(image_tensor)
+
+# Process results
+boxes = predictions[0]['boxes']
+scores = predictions[0]['scores']
+labels = predictions[0]['labels']
+```
+
+## Model Performance
+
+This model was trained from scratch on the Berkeley DeepDrive (BDD) 100K dataset using Faster R-CNN architecture.
+
+## Architecture
+
+**Faster R-CNN** (Region-based Convolutional Neural Network) is a two-stage object detection framework:
+
+1. **Region Proposal Network (RPN)**: Generates object proposals
+2. **Fast R-CNN detector**: Classifies proposals and refines bounding box coordinates
+
+Key advantages:
+- High accuracy object detection
+- Precise localization
+- Good performance on small objects
+- Well-established architecture with extensive research backing
+
+## Intended Use
+
+- **Primary Use**: Object detection in autonomous driving scenarios
+- **Suitable for**: Research, development, and deployment of object detection systems
+- **Limitations**: Performance may vary on images significantly different from the training distribution
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@article{ren2015faster,
+  title={Faster r-cnn: Towards real-time object detection with region proposal networks},
+  author={Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
+  journal={Advances in neural information processing systems},
+  volume={28},
+  year={2015}
+}
+```
+
+## License
+
+This model is released under the MIT License.
+
+## Keywords
+
+Faster R-CNN, Object Detection, Computer Vision, BDD 100K, Autonomous Driving, Deep Learning, Two-Stage Detection