---
license: apache-2.0
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### VehicleNet-Y26 License & Usage Terms
VehicleNet-Y26m is a vehicle detection model released under the Apache
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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
- edge-computing
---
# VehicleNet-Y26m
**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.
This `m` variant is designed for speed and inferences on low-latency devices, offering significant speed and accuracy.
This model is finetuned on `YOLO26m`:[arXiv](https://arxiv.org/html/2509.25164v3) model by **`Ultralytics`** using **`UVH-26-MV Dataset`**.
## Model Overview and Parameters
- Pretrained_weights: YOLO26m
- Number of Classes: 14
- Layers: 132 layers
- Parameters(M): 20,360,246 parameters, 0 gradients
- GFLOPs: 67.9
- Input Resolution: 640 × 640
- Training Epochs: Up to 60 (early stopping applied, patience=5), best model at: 35/60
- Batch Size: 48
- Hardware: Dual NVIDIA Tesla T4 GPUs
- Framework: Ultralytics YOLO (PyTorch)
## Performance Summary
- `mAP@50`: 0.74967
- `mAP@50:95`: 0.6685
- `Precision`: 0.70126
- `Recall`: 0.71083
## 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:
- Edge device computation
- Traffic surveillance and analytics
- Academic research and benchmarking
## License
This model is released under the `Apache License 2.0`.