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---
license: mit
library_name: pytorch
tags:
- faster-rcnn
- object-detection
- computer-vision
- pytorch
- bdd100k
- autonomous-driving
- BDD 100K
- fine-tuned
- hallucination-mitigation
- out-of-distribution
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-finetune
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 Fine-tuned
Faster R-CNN model fine-tuned on Berkeley DeepDrive (BDD) 100K dataset to mitigate hallucination on out-of-distribution data in autonomous driving scenarios.
## Model Details
- **Model Type**: Faster R-CNN Object Detection
- **Dataset**: Berkeley DeepDrive (BDD) 100K
- **Training Method**: fine-tuned to mitigate hallucination on out-of-distribution data
- **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 fine-tuned to mitigate hallucination on out-of-distribution data on the Berkeley DeepDrive (BDD) 100K dataset using Faster R-CNN architecture.
**Fine-tuning Objective**: This model was specifically fine-tuned to mitigate hallucination on out-of-distribution (OOD) data, improving robustness when encountering images that differ from the training distribution.
## 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
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