README.md

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license: apache-2.0
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library_name: roboflow 
tags:
- safetensors
- roboflow
- data-annotation
- transformers
tensor_type:
- F32
- BF16
- F8_E4M3
datasets:
- iisc-aim/UVH-26
language:
- en
metrics:
- accuracy
- precision
- recall
- f1
base_model:
- qualcomm/RF-DETR
pipeline_tag: object-detection
---

# VehicleNet-RFDETR-s

![Code_Generated_Image](https://cdn-uploads.huggingface.co/production/uploads/66c6048d0bf40704e4159a23/LvYg92_y8jp6wmA_anqSl.png)

<a href="https://www.apache.org/licenses/LICENSE-2.0">
  <img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" alt="Apache 2.0 License">
</a>
<a href="https://github.com/ultralytics/ultralytics">
  <img src="https://img.shields.io/badge/RFDETR-Small-red?logo=ultralytics&logoColor=white" alt="RFDETRSmall">
</a>
<a href="#performance-metrics">
  <img src="https://img.shields.io/badge/mAP%4050:95-0.60555-darkgreen?style=flat" alt="mAP@50:95">
</a>

## Overview

**VehicleNet-RFDETR-s** is a multi-class vehicle detection model designed for fine-grained vehicle type recognition in real-world traffic scenes. It is fine-tuned on the **UVH-26-MV Dataset**, curated and released by the **Indian Institute of Science (IISc), Bangalore**, which captures the highly complex, dense, and heterogeneous nature of Indian road traffic.

The model recognizes **14 vehicle categories**, including hatchbacks, sedans, SUVs, MUVs, two-wheelers, three-wheelers, buses, trucks, and a range of commercial vehicle types. This **small variant** is optimized for low-latency inference, balancing speed and accuracy for deployment on resource-constrained hardware.

The model is fine-tuned on the **RFDETRSmall** architecture ([arXiv: 2511.09554](https://arxiv.org/pdf/2511.09554)) by Roboflow, using `rfdetr` version 1.6.1.

![image](https://cdn-uploads.huggingface.co/production/uploads/66c6048d0bf40704e4159a23/fatMviv1dDhpWyF1Eyvqc.png)


## Model Specifications

| Parameter                    | Value                        |
|-----------------------------|------------------------------|
| Base Architecture           | RFDETRSmall                  |
| Number of Classes           | 14                           |
| Total Layers                | -                            |
| Parameters                  | 32.1 M                       |
| GFLOPs                      | -                            |
| Input Resolution            | 512 × 512                    |
| Training Epochs             | 10                           |
| Batch Size                  | 4                            |
| Gradient Accumulation Steps | 2                            |
| Effective Batch Size        | 16 *(batch × grad_accum × GPUs)* |
| Training Hardware           | Dual NVIDIA Tesla T4 GPUs    |
| Framework                   | Roboflow (PyTorch)           |
| Pretrained Weights          | RFDETRSmall (Roboflow)       |

## Performance Metrics

| Metric        | Value   |
|--------------|---------|
| mAP@50       | 0.71669 |
| mAP@50:95    | 0.60555 |
| mAP@75       | 0.66804 |
| Precision    | 0.68535 |
| Recall       | 0.6889  |

### Training Curves

![Training_Curves](https://cdn-uploads.huggingface.co/production/uploads/66c6048d0bf40704e4159a23/bNMIrTTxuKBsvZ8OzoR_R.png)

## Intended Use

VehicleNet-RFDETR-s is suitable for the following applications:

- **Traffic Surveillance & Analytics** — Automated vehicle classification in urban and highway environments.
- **Edge Device Deployment** — Optimized for low-latency inference on constrained hardware.
- **Academic Research & Benchmarking** — Evaluation of fine-grained vehicle detection in heterogeneous traffic conditions, particularly on Indian road datasets.

### Out-of-Scope Use

- Deployment in safety-critical systems without independent validation.
- Surveillance applications that violate individual privacy rights or applicable regulations.
- Any use case inconsistent with the Apache License 2.0 terms.

## Citation

If you use this model or the UVH-26-MV dataset in your research, please cite the respective dataset and model sources appropriately.

## License

This model is released under the **[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0)**. You are free to use, modify, and distribute this model subject to the terms of the license. See the `LICENSE` file for full details.