Create README.md

README.md

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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:
+
+  1. You satisfy the minimum legal age requirements applicable in your country or region.
+  2. You are duly authorized to enter into and be bound by this agreement.
+  3. You will use this model strictly in accordance with the Apache License 2.0 and all
+     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,
+  unethical, or unauthorized application of this model is strictly prohibited.
+
+  If you do not agree to these terms, or if you lack the authority to accept them,
+  you must refrain from accessing or using this model.
+
+extra_gated_fields:
+  First Name: text
+  Last Name: text
+  Country: country
+  Job title:
+    type: select
+    options:
+    - Undergraduate Student
+    - Research Graduate
+    - AI Researcher
+    - AI Developer / Engineer
+    - Other
+  geo: ip_location
+  By submitting an access request, I acknowledge and accept the terms above: checkbox
+extra_gated_button_content: Submit Access Request
+
+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/YOLO26-s-blue?logo=ultralytics&logoColor=white" alt="YOLO26-s">
+</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
+
+![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.