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--- |
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license: apache-2.0 |
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extra_gated_prompt: >- |
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### VehicleNet-Y26 License & Usage Terms |
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VehicleNet-Y26n is a vehicle detection model released under the Apache License, |
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Version 2.0. By accessing, downloading, or using VehicleNet-Y26, you confirm that: |
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1. You meet the legal age requirements applicable in your country. |
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2. You have the authority to accept this agreement. |
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3. You agree to use the model in compliance with the Apache-2.0 license and all applicable laws and regulations. |
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VehicleNet is provided as-is, without warranties of any kind, express or implied. |
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The authors make no guarantees regarding accuracy, reliability, fitness for a particular |
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purpose, or suitability for deployment in safety-critical or regulated environments. |
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You are solely responsible for how the model and its outputs are used. Any misuse, |
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unlawful application, or deployment without proper validation is strictly discouraged. |
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**Your access will be granted automatically upon submission.** |
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extra_gated_fields: |
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First Name: text |
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Last Name: text |
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Country: country |
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Job title: |
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type: select |
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options: |
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- Undergraduate Student |
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- Research Graduate |
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- AI researcher |
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- AI developer/engineer |
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- Other |
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geo: ip_location |
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I acknowledge and accept these terms and conditions: checkbox |
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extra_gated_button_content: Get Access |
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datasets: |
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- iisc-aim/UVH-26 |
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language: |
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- en |
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metrics: |
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- confusion_matrix |
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library_name: ultralytics |
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base_model: |
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- onnx-community/yolo26x-ONNX |
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pipeline_tag: object-detection |
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tags: |
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- indian-traffic |
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- inference-efficiency |
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- multi-vehicle-detection |
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- ultralytics |
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--- |
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# VehicleNet-Y26n |
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**VehicleNet-Y26n** is a multi-class vehicle detection model designed for speed & efficiency for fine-grained vehicle type recognition in real-world traffic scenes. This model is trained on **`UVH-26-MV Dataset`** released by IISc Banaglore. The dataset is based on Indian traffic which is highly challenging, dense and heterogeneous. It contains 14 vehicle categories such as hatchback, sedan, SUV, MUV, two-wheelers, three-wheelers, buses, trucks, and commercial vehicles. |
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This model is finetuned on `YOLO26n`:[arXiv](https://arxiv.org/html/2509.25164v3)(model by **`Ultralytics`** using **`UVH-26-MV Dataset`**. |
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## Model Overview and Parameters |
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- Task: Multi-class vehicle detection |
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- Architecture: YOLO26n (Ultralytics) |
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- Number of Classes: 14 |
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- Input Resolution: 640 × 640 |
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- Training Epochs: 30/30 |
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- Batch Size: 24 |
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- Hardware: Dual NVIDIA Tesla T4 GPUs |
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- Framework: Ultralytics YOLO (PyTorch) |
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## Performance Summary |
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- `mAP@50`: 0.634 |
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- `mAP@50:95`: 0.542 |
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- `Precision`: 0.706 |
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- `Recall`: 0.701 |
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The model showed strong detection performance for structurally distinct vehicle categories such as two-wheelers, three-wheelers, buses, and trucks. Fine-grained car subclasses (hatchback, sedan, SUV, MUV) exhibit expected inter-class confusion/challenge due to visual similarity and viewpoint overlap, as reflected in the confusion matrix. |
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## Intended Use |
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The model is suitable for: |
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- Edge device computation |
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- Traffic surveillance and analytics |
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- Academic research and benchmarking |
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## License |
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This model is released under the `Apache License 2.0`. |
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