README.md

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datasets:
- iisc-aim/UVH-26
language:
- en
metrics:
- confusion_matrix
- accuracy
- precision
- recall
- f1
base_model:
- qualcomm/RF-DETR
pipeline_tag: object-detection
tags:
- indian-traffic
- inference-efficiency
- multi-vehicle-detection
- gpu-hungry
- roboflow
---

# VehicleNet-RFDETR-n

<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-Nano-red?logo=ultralytics&logoColor=white" alt="RFDETRNano">
</a>
<a href="#performance-metrics">
  <img src="https://img.shields.io/badge/mAP%4050:95-0.53883-darkgreen?style=flat" alt="mAP@50:95">
</a>

## Overview

**VehicleNet-RFDETR-n** 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 **nano variant** is optimized for low-latency inference, balancing speed and accuracy for deployment on resource-constrained hardware.

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


## Model Specifications

| Parameter                    | Value                        |
|-----------------------------|------------------------------|
| Base Architecture           | RFDETRNano                   |
| Number of Classes           | 14                           |
| Total Layers                | -                            |
| Parameters                  | 30.5 M                       |
| GFLOPs                      | -                            |
| Input Resolution            | 384 × 384                    |
| Training Epochs             | 8                            |
| 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          | RFDETRNano (Roboflow)        |

## Performance Metrics

| Metric        | Value   |
|--------------|---------|
| mAP@50       | 0.66771 |
| mAP@50:95    | 0.53883 |
| mAP@75       | 0.59782 |
| Precision    | 0.66409 |
| Recall       | 0.63997 |

### Training Curves

![Training_Curves](https://cdn-uploads.huggingface.co/production/uploads/66c6048d0bf40704e4159a23/8aJZt0i9-xCmbHqFM32Mn.png)

## Intended Use

VehicleNet-RFDETR-n8 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.