language:
- en
tags:
- object-detection
- license-plate-detection
- vehicle-detection
widget:
- src: https://drive.google.com/uc?id=1j9VZQ4NDS4gsubFf3m2qQoTMWLk552bQ
example_title: Skoda 1
- src: https://drive.google.com/uc?id=1p9wJIqRz3W50e2f_A0D8ftla8hoXz4T5
example_title: Skoda 2
metrics:
- average precision
- recall
- IOU
pipeline_tag: object-detection
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:
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 |