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nickmuchi/
yolos-small-finetuned-license-plate-detection

Name: yolos-small-finetuned-license-plate-detection
Availability: InStock

license-plate-detection

vehicle-detection

endpoints_compatible

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YOLOS (small-sized) model

This model is a fine-tuned version of hustvl/yolos-small on the licesne-plate-recognition dataset from Roboflow which contains 5200 images in the training set and 380 in the validation set. The original YOLOS model was fine-tuned on COCO 2017 object detection (118k annotated images).

Model description

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).

Intended uses & limitations

You can use the raw model for object detection. See the model hub to look for all available YOLOS models.

How to use

Here is how to use this model:

python

from transformers import YolosFeatureExtractor, YolosForObjectDetection
from PIL import Image
import requests

url = 'https://drive.google.com/uc?id=1p9wJIqRz3W50e2f_A0D8ftla8hoXz4T5'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = YolosFeatureExtractor.from_pretrained('nickmuchi/yolos-small-finetuned-license-plate-detection')
model = YolosForObjectDetection.from_pretrained('nickmuchi/yolos-small-finetuned-license-plate-detection')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)

# model predicts bounding boxes and corresponding face mask detection classes
logits = outputs.logits
bboxes = outputs.pred_boxes

Currently, both the feature extractor and model support PyTorch.

Training data

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.

Training

This model was fine-tuned for 200 epochs on the licesne-plate-recognition.

Evaluation results

This model achieves an AP (average precision) of 49.0.

Accumulating evaluation results...

IoU metric: bbox

Metrics	Metric Parameter	Location	Dets	Value
Average Precision	(AP) @[ IoU=0.50:0.95	area= all	maxDets=100 ]	0.490
Average Precision	(AP) @[ IoU=0.50	area= all	maxDets=100 ]	0.792
Average Precision	(AP) @[ IoU=0.75	area= all	maxDets=100 ]	0.585
Average Precision	(AP) @[ IoU=0.50:0.95	area= small	maxDets=100 ]	0.167
Average Precision	(AP) @[ IoU=0.50:0.95	area=medium	maxDets=100 ]	0.460
Average Precision	(AP) @[ IoU=0.50:0.95	area= large	maxDets=100 ]	0.824
Average Recall	(AR) @[ IoU=0.50:0.95	area= all	maxDets= 1 ]	0.447
Average Recall	(AR) @[ IoU=0.50:0.95	area= all	maxDets= 10 ]	0.671
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 ]	0.278
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 ]	0.890