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:
1import torch
2import torchvision.transforms as transforms
3from PIL import Image
4
5# Load the model (example using torchvision)
6model = torch.load('path/to/model.pth')
7model.eval()
8
9# Prepare your image
10transform = transforms.Compose([
11 transforms.ToTensor(),
12])
13
14image = Image.open('path/to/image.jpg')
15image_tensor = transform(image).unsqueeze(0)
16
17# Run inference
18with torch.no_grad():
19 predictions = model(image_tensor)
20
21# Process results
22boxes = predictions[0]['boxes']
23scores = predictions[0]['scores']
24labels = 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:
- Region Proposal Network (RPN): Generates object proposals
- 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:
1@article{ren2015faster,
2 title={Faster r-cnn: Towards real-time object detection with region proposal networks},
3 author={Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
4 journal={Advances in neural information processing systems},
5 volume={28},
6 year={2015}
7}
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
