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license: apache-2.0
extra_gated_prompt: >-
### VehicleNet-Y26 — Model Access Agreement
**VehicleNet-RFDETR-n8** is a multi-vehicle detection model released under the
Apache License, Version 2.0. Access to this model is granted exclusively to
individuals who meet the legal age requirements of their jurisdiction and possess
the authority to accept and comply with the terms set forth herein.
By requesting access, downloading, or using VehicleNet-RFDETR-n8 in any capacity,
you represent and warrant that:
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2. You are duly authorized to enter into and be bound by this agreement.
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applicable local, national, and international laws and regulations.
**Disclaimer of Warranties:** VehicleNet-RFDETR-n8 is provided "as-is," without
warranties of any kind, whether express or implied, including but not limited to
warranties of accuracy, reliability, fitness for a particular purpose, or suitability
for deployment in safety-critical or regulated environments. The authors and affiliated
institutions assume no liability for any direct, indirect, incidental, or consequential
damages arising from the use or misuse of this model.
**User Responsibility:** You bear sole responsibility for all use of this model and its
outputs. Deployment in production systems, safety-critical applications, or any context
without adequate validation and human oversight is strongly discouraged. Any unlawful,
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If you do not agree to these terms, or if you lack the authority to accept them,
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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-n8
<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-n8** 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
## 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. |