We present a fine-tuned YOLOs model for license plate detection, which achieved an AP of 47.9 on the test set. Our model was trained for 200 epochs on a single GPU using Google Colab, utilizing the DETR loss and a base-sized YOLOs model. We evaluated our model on various IoU thresholds and obtained an average recall of 0.676 at IoU=0.50:0.95, with location being medium.
We present a fine-tuned YOLOs model for license plate detection, which achieved an AP of 47.9 on the test set. Our model was trained for 200 epochs on a single GPU using Google Colab, utilizing the DETR loss and a base-sized YOLOs model. We evaluated our model on various IoU thresholds and obtained an average recall of 0.676 at IoU=0.50:0.95, with location being medium.
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The original YOLOS model was fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Fang et al. and first released in this repository. This model was further fine-tuned on the license plate dataset from Kaggle. The dataset consists of 735 images of annotations categorised as "vehicle" and "license-plate". The model was trained for 200 epochs on a single GPU using Google Colab
YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN).
You can use the raw model for object detection. See the model hub to look for all available YOLOS models.
The YOLOS model was pre-trained on ImageNet-1k and fine-tuned on COCO 2017 object detection, a dataset consisting of 118k/5k annotated images for training/validation respectively.
This model was fine-tuned for 200 epochs on the license plate dataset.
This model achieves an AP (average precision) of 47.9.
Accumulating evaluation results...
IoU metric: bbox
Metrics | Metric Parameter | Location | Dets | Value |
---|---|---|---|---|
Average Precision | (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] | 0.479 |
Average Precision | (AP) @[ IoU=0.50 | area= all | maxDets=100 ] | 0.752 |
Average Precision | (AP) @[ IoU=0.75 | area= all | maxDets=100 ] | 0.555 |
Average Precision | (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.147 |
Average Precision | (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.420 |
Average Precision | (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.804 |
Average Recall | (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] | 0.437 |
Average Recall | (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] | 0.641 |
Average Recall | (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] | 0.676 |
Average Recall | (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.268 |
Average Recall | (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.641 |
Average Recall | (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.870 |