README.md

metadata

license: agpl-3.0
extra_gated_prompt: >-
  ### UrbanFlow Intelligence Engine | Model Access & Usage Agreement

  Access to the UrbanFlow perception architectures is governed by this
  professional agreement.  By requesting access, downloading, or utilizing these
  models, you confirm your commitment to the  following terms and our
  open-source licensing structure:

  **Licensing & Attribution**: UrbanFlow is built upon the foundational
  advancements of the  Ultralytics YOLO architectures. In alignment with the
  **AGPL-3.0 license**, we release this  fine-tuned engine to the community
  while strictly respecting the open-source contributions of  **Ultralytics**
  and their respective authors. We acknowledge their pivotal role in advancing 
  the boundaries of real-time object detection.

  1. **Legal Capacity**: You certify that you meet the legal age of majority in
  your 
     jurisdiction and possess the authority to accept and comply with these terms.

  2. **Intent of Usage**: UrbanFlow is provided for technical evaluation,
  academic mobility 
     research, and urban planning analysis. In accordance with the AGPL-3.0 "copyleft" 
     provisions, any derivative works or hosted services utilizing this model must also 
     be made available under the same license.

  3. **No Warranty for Critical Infrastructure**: This model is provided "as-is"
  for research 
     and evaluation purposes. Perception365 makes no guarantees regarding absolute accuracy in 
     safety-critical autonomous navigation or high-stakes regulatory environments. Independent 
     validation is mandatory for any production-grade deployment.

  4. **Operational Accountability**: You assume sole responsibility for the
  deployment and 
     outputs of the model. Usage for unlawful surveillance or any application violating 
     individual privacy standards is strictly prohibited.

  If you do not agree to these professional standards or the AGPL-3.0 licensing
  terms,  do not proceed with this access request.
extra_gated_fields:
  Full Name: text
  Organization or Institution: text
  Work or Student Email: text
  Country: country
  Professional or Academic Role:
    type: select
    options:
      - Undergraduate / Graduate Student
      - Academic Researcher / Professor
      - Computer Vision / AI Engineer
      - Traffic / Urban Planning Engineer
      - Other Professional / Consultant
  Primary Use Case:
    type: select
    options:
      - Academic Research & Publication
      - Model Benchmarking & Evaluation
      - Smart City Pilot / Deployment
      - Heterogeneous Traffic Analysis
      - Industrial Monitoring & Testing
  I certify that I have read and agree to the UrbanFlow Usage Agreement and AGPL License: checkbox
extra_gated_button_content: Request Access
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 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.