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aadex/
faster-rcnn-rope-vit-tiny-coco

Name: faster-rcnn-rope-vit-tiny-coco
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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

Metric	Value
bbox_mAP	0.0680
bbox_mAP_50	0.1510
bbox_mAP_75	0.0530
bbox_mAP_s (small)	0.0360
bbox_mAP_m (medium)	0.1260
bbox_mAP_l (large)	0.0640

Usage

python

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:

bibtex

@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.

aadex/faster-rcnn-rope-vit-tiny-coco