faster-rcnn-rope-vit-tiny-coco

Faster R-CNN with RoPE-ViT Backbone for Object Detection

This model is a Faster R-CNN object detection model with a RoPE-ViT (Vision Transformer with Rotary Position Embeddings) backbone, trained on the COCO dataset.

Model Description

• Architecture: Faster R-CNN
• Backbone: RoPE-ViT Tiny
• Dataset: COCO
• Task: Object Detection
• Framework: MMDetection

Training Results

Usage

from mmdet.apis import init_detector, inference_detector

config_file = 'faster_rcnn_rope_vit_tiny_coco.py'
checkpoint_file = 'best_coco_bbox_mAP_epoch_12.pth'

# Initialize the model
model = init_detector(config_file, checkpoint_file, device='cuda:0')

# Inference on an image
result = inference_detector(model, 'demo.jpg')

Training Configuration

The model was trained with the following configuration:

• Input size: 512x512
• Training epochs: 12
• Optimizer: SGD with momentum
• Learning rate scheduler: Step decay

Citation

If you use this model, please cite:

@misc{rope-vit-detection,
  author = {VLG IITR},
  title = {Faster R-CNN with RoPE-ViT for Object Detection},
  year = {2026},
  publisher = {Hugging Face},
}

License

This model is released under the Apache 2.0 license.