README.md

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  ### UrbanFlow Intelligence Engine | Model Access & Usage Agreement

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datasets:
- iisc-aim/UVH-26
language:
- en
metrics:
- confusion_matrix
library_name: ultralytics
base_model:
- Ultralytics/YOLO26
pipeline_tag: object-detection
tags:
- indian-traffic
- inference-efficiency
- multi-vehicle-detection
- ultralytics
---

# VehicleNet-Y26x

<a href="https://www.apache.org/licenses/LICENSE-2.0">
    <img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" alt="License">
</a>
<a href="https://github.com/ultralytics/ultralytics">
    <img src="https://img.shields.io/badge/YOLO26-x-blue?logo=ultralytics&logoColor=white" alt="Model">
</a>
<a href="#performance-metrics">
    <img src="https://img.shields.io/badge/mAP%4050:95-0.666-darkgreen?style=flat" alt="mAP">
</a>


**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](https://arxiv.org/html/2509.25164v3)(model by **`Ultralytics`** using **`UVH-26-MV Dataset`**.

![image](https://cdn-uploads.huggingface.co/production/uploads/66c6048d0bf40704e4159a23/ZNKszLuj-9tjTToKfcYWN.png)

## 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

![image](https://cdn-uploads.huggingface.co/production/uploads/66c6048d0bf40704e4159a23/__9bW8t5-HrxNNMi-d6Qf.png)

## Per-class mAP@50:95

![image](https://cdn-uploads.huggingface.co/production/uploads/66c6048d0bf40704e4159a23/iUYD_jjNBxvdLDNoYfpRh.png)

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.

![image](https://cdn-uploads.huggingface.co/production/uploads/66c6048d0bf40704e4159a23/6z63MdHDo7UJkMMaoC1K2.png)

## 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`.