🚀 YOLOS (small-sized) model
This is a fine - tuned object detection model for license plate and vehicle detection, offering high - performance results on specific datasets.
🚀 Quick Start
This model is a fine - tuned version of hustvl/yolos-small on the licesne-plate-recognition dataset from Roboflow. The training set contains 5200 images and the validation set has 380 images. The original YOLOS model was fine - tuned on COCO 2017 object detection (118k annotated images).
✨ Features
- Object Detection: Can be used for general object detection tasks.
- License Plate Detection: Specifically fine - tuned for license plate recognition.
- Vehicle Detection: Capable of detecting vehicles.
- Metrics: Evaluated using average precision, recall, and IOU.
📦 Installation
No specific installation steps are provided in the original document.
💻 Usage Examples
Basic Usage
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)
logits = outputs.logits
bboxes = outputs.pred_boxes
Currently, both the feature extractor and model support PyTorch.
📚 Documentation
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.
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](https://app.roboflow.com/objectdetection - jhgr1/license - plates - recognition/2).
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 |