README.md

---
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