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VehicleNet-Y26 License & Usage Terms
VehicleNet-Y26x is a vehicle detection model released under the Apache License, Version 2.0. Access to this model is permitted only to individuals of legal age who have the authority to accept and comply with the license terms in their jurisdiction. By accessing, downloading, or using VehicleNet-Y26, you confirm that:
- You meet the legal age requirements applicable in your country.
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- You agree to use the model in compliance with the Apache-2.0 license and all applicable laws and regulations.
VehicleNet is provided as-is, without warranties of any kind, express or implied. The authors make no guarantees regarding accuracy, reliability, fitness for a particular purpose, or suitability for deployment in safety-critical or regulated environments.
You are solely responsible for how the model and its outputs are used. Any misuse, unlawful application, or deployment without proper validation is strictly discouraged.
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VehicleNet-Y26x
VehicleNet-Y26x is a multi-class vehicle detection model designed for fine-grained vehicle type recognition in real-world traffic scenes. The 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.
This model is finetuned on YOLO26x:arXiv(model by Ultralytics using UVH-26-MV Dataset.
Model Overview and Parameters
- Pretrained_weights: YOLO26x
- Number of Classes: 14
- Layers: 190 layers
- Parameters(M): 55,649,718 parameters, 0 gradients
- GFLOPs: 193.5
- Input Resolution: 640 × 640
- Training Epochs: Up to 40 (early stopping applied), best model at: 28/40
- Batch Size: 24
- Hardware: Dual NVIDIA Tesla T4 GPUs
- Framework: Ultralytics YOLO (PyTorch)
Performance Summary
mAP@50: 0.746mAP@50:95: 0.666Precision: 0.706Recall: 0.701
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.
Intended Use
The model is suitable for:
- cloud based inference
- Traffic surveillance and analytics
- Academic research and benchmarking
License
This model is released under the Apache License 2.0.
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onnx-community/yolo26x-ONNX