morsetechlab

This is a fine-tuned version of YOLOv11 (n, s, m, l, x) specialized for License Plate Detection, using a public dataset from Roboflow Universe: License Plate Recognition Dataset (10,125 images) - Smart Parking Systems - Tollgate / Access Control Automation - Traffic Surveillance & Enforcement - ALPR with OCR Integration - Base Model: YOLOv11 (`n`, `s`, `m`, `l`, `x`) - Training Epochs: 300 - Input Size: 640x640 - Optimizer: SGD (Ultralytics default) - Device: NVIDIA A100 - Data Format: YOLOv5-compatible (images + labels in txt) | Metric | Value | |---------------|---------| | Precision | 0.9893 | | Recall | 0.9508 | | mAP@50 | 0.9813 | | mAP@50-95 | 0.7260 | > For full table across models (n to x), please see the README - PyTorch (.pt) — for use with Ultralytics CLI and Python API - ONNX (.onnx) — for cross-platform inference - Base Model (YOLOv11): AGPLv3 by Ultralytics - Dataset: CC BY 4.0 by Roboflow Universe - This model: AGPLv3 (due to YOLOv11 license inheritance) In accordance with the AGPLv3 license: - If you use this model in a service or project, you must open source the code that uses it. - Please give proper attribution to Roboflow, Ultralytics, and MorseTechLab when using or deploying.