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--- |
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license: apache-2.0 |
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extra_gated_prompt: >- |
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VehicleNet-Y26m is a vehicle detection model released under the Apache |
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License, Version 2.0. Access to this model is permitted only to individuals of |
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legal age who have the authority to accept and comply with the license terms |
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in their jurisdiction. By accessing, downloading, or using VehicleNet-Y26, you |
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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 |
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all applicable laws and regulations. |
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VehicleNet is provided as-is, without warranties of any kind, express or |
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implied. The authors make no guarantees regarding accuracy, reliability, |
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fitness for a particular purpose, or suitability for deployment in |
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safety-critical or regulated environments. |
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You are solely responsible for how the model and its outputs are used. Any |
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misuse, unlawful application, or deployment without proper validation is |
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strictly discouraged. |
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If you do not agree to these terms or lack the authority to accept them, do |
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not use this model. |
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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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By submitting an access request, I acknowledge & accept these conditions: checkbox |
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extra_gated_button_content: Submit |
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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/yolo26m-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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- edge-computing |
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--- |
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# VehicleNet-Y26m |
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<a href="https://www.apache.org/licenses/LICENSE-2.0"> |
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<img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" alt="License"> |
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</a> |
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<a href="https://github.com/ultralytics/ultralytics"> |
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<img src="https://img.shields.io/badge/YOLO26-m-blue?logo=ultralytics&logoColor=white" alt="Model"> |
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</a> |
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<a href="#performance-metrics"> |
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<img src="https://img.shields.io/badge/mAP%4050:95-0.6685-darkgreen?style=flat" alt="mAP"> |
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</a> |
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**VehicleNet-Y26m** is another 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. |
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This `m` variant is designed for speed and inferences on low-latency devices, offering significant speed and accuracy. |
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This model is finetuned on `YOLO26m`:[arXiv](https://arxiv.org/html/2509.25164v3) model by **`Ultralytics`** using **`UVH-26-MV Dataset`**. |
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<!--  --> |
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## Model Overview and Parameters |
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- Pretrained_weights: YOLO26m |
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- Number of Classes: 14 |
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- Layers: 132 layers |
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- Parameters(M): 20,360,246 parameters, 0 gradients |
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- GFLOPs: 67.9 |
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- Input Resolution: 640 × 640 |
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- Training Epochs: Up to 60 (early stopping applied, patience=5), best model at: 35/60 |
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- Batch Size: 48 |
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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`: 0.74967 |
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- `mAP@50:95`: 0.6685 |
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- `Precision`: 0.70126 |
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- `Recall`: 0.71083 |
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## Per-class mAP@50:95 |
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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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