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Laudando-Associates-LLC/
d-fine-small

Name: d-fine-small
Availability: InStock

Laudando-Associates-LLC/pucks

2410.13842

Laudando-Associates-LLC/d-fine

finetune

apache-2.0

model-index

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D-FINE Small

This repository contains the D-FINE Small model, a real-time object detector designed for efficient and accurate object detection tasks.

Try it in the Browser

You can test this model using our interactive Gradio demo:

Model Overview

Architecture: D-FINE Small
Parameters: 10.3M
Performance:
- mAP@[0.50:0.95]: 0.816
- mAP@[0.50]: 0.983
- AR@[0.50:0.95]: 0.859
- F1 Score: 0.951
Framework: PyTorch / ONNX
Training Hardware: 2× NVIDIA RTX A6000 GPUs

Download

Format	Link
ONNX
PyTorch

Usage

To utilize this model, ensure you have the shared D-FINE processor:

python

from transformers import AutoProcessor, AutoModel

# Load processor
processor = AutoProcessor.from_pretrained("Laudando-Associates-LLC/d-fine", trust_remote_code=True)

# Load model
model = AutoModel.from_pretrained("Laudando-Associates-LLC/d-fine-small", trust_remote_code=True)

# Process image
inputs = processor(image)

# Run inference
outputs = model(**inputs, conf_threshold=0.4)

Evaluation

This model was trained and evaluated on the L&A Pucks Dataset.

License

This model is licensed under the Apache License 2.0.

Citation

If you use D-FINE or its methods in your work, please cite the following BibTeX entries:

latex

@misc{peng2024dfine,
      title={D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement},
      author={Yansong Peng and Hebei Li and Peixi Wu and Yueyi Zhang and Xiaoyan Sun and Feng Wu},
      year={2024},
      eprint={2410.13842},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}