README.md

metadata

license: apache-2.0
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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: 

  1. You meet the legal age requirements applicable in your country. 

  2. You have the authority to accept this agreement. 

  3. 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.

  If you do not agree to these terms or lack the authority to accept them, do
  not use this model.
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datasets:
  - iisc-aim/UVH-26
language:
  - en
metrics:
  - confusion_matrix
library_name: ultralytics
base_model:
  - onnx-community/yolo26x-ONNX
pipeline_tag: object-detection
tags:
  - indian-traffic
  - inference-efficiency
  - multi-vehicle-detection
  - ultralytics

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: 0.746
• mAP@50:95: 0.666
• Precision: 0.706
• Recall: 0.701

Per-class mAP@50:95

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.