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### VehicleNet— Model Access Agreement
**VehicleNet-RFDETR-s** is a multi-vehicle detection model released under the
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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
## 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.
## 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
## 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.