Faster R-CNN model fine-tuned on KITTI dataset to mitigate hallucination on out-of-distribution data for enhanced autonomous driving object detection.

Model Details

Model Type: Faster R-CNN Object Detection
Dataset: KITTI Object Detection
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 KITTI Object Detection dataset, which contains the following object classes:

car, pedestrian, cyclist

Dataset-specific Details:

KITTI Object Detection Dataset:

Real-world autonomous driving dataset
Contains stereo imagery from vehicle-mounted cameras
Focus on cars, pedestrians, and cyclists
Challenging scenarios with varying lighting and weather conditions

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 KITTI Object Detection 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:

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:

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, KITTI, Autonomous Driving, Deep Learning, Two-Stage Detection

HugoHE/faster-rcnn-kitti-finetune

Faster R-CNN - KITTI Object Detection Fine-tuned