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### UrbanFlow Intelligence Engine | Model Access & Usage Agreement
Access to the UrbanFlow perception architectures is governed by this
professional agreement. By requesting access, downloading, or utilizing these
models, you confirm your commitment to the following terms and our
open-source licensing structure:
**Licensing & Attribution**: UrbanFlow utilizes the advanced transformer-based
architectures from the RF-DETR series. In alignment with the **Apache License
2.0**, we release this specialized model to the community while formally
acknowledging the innovative contributions of **Roboflow** and their
respective engineering teams. We thank them for their commitment to
open-source computer vision research and accessible model weights.
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2. **Intent of Usage**: UrbanFlow is provided for technical evaluation,
academic mobility
research, and urban planning analysis. Usage must remain in compliance with the
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3. **No Warranty for Critical Infrastructure**: This model is provided "as-is"
for research
and evaluation purposes. Perception365 makes no guarantees regarding absolute accuracy in
safety-critical autonomous navigation or high-stakes regulatory environments. Independent
validation is mandatory for any production-grade deployment.
4. **Operational Accountability**: You assume sole responsibility for the
deployment and
outputs of the model. Usage for unlawful surveillance or any application violating
individual privacy standards is strictly prohibited.
If you do not agree to these professional standards or the Apache-2.0
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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
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) 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. You are free to use, modify, and distribute this model subject to the terms of the license. See the LICENSE file for full details.