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VehicleNet-Y26 License & Usage Terms VehicleNet-Y26n is a vehicle detection model released under the Apache License, Version 2.0. By accessing, downloading, or using VehicleNet-Y26, you confirm that:
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VehicleNet-Y26n
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.
This model is finetuned on YOLO26n:arXiv(model by Ultralytics using UVH-26-MV Dataset.
Model Overview and Parameters
- Task: Multi-class vehicle detection
- Architecture: YOLO26n (Ultralytics)
- Number of Classes: 14
- Input Resolution: 640 × 640
- Training Epochs: 30/30
- Batch Size: 24
- Hardware: Dual NVIDIA Tesla T4 GPUs
- Framework: Ultralytics YOLO (PyTorch)
Performance Summary
mAP@50: 0.634mAP@50:95: 0.542Precision: 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:
- Edge device computation
- 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