README.md

---
license: apache-2.0
tags:
- object-detection
- face-mask-detection
datasets:
- coco
- face-mask-detection
widget:
- src: https://drive.google.com/uc?id=1VwYLbGak5c-2P5qdvfWVOeg7DTDYPbro
  example_title: "City Folk"
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg
  example_title: "Football Match"
metrics:
- average precision
- recall
model-index:
- name: yolos-small-finetuned-masks
  results: []
---

# YOLOS (small-sized) model

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](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in [this repository](https://github.com/hustvl/YOLOS). 

This model was further fine-tuned on the [face mask dataset]("https://www.kaggle.com/datasets/andrewmvd/face-mask-detection") from Kaggle. The dataset consists of 853 images of people with annotations categorised as "with mask","without mask" and "mask not worn correctly". The model was trained for 200 epochs on a single GPU usins Google Colab

## 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](https://huggingface.co/models?search=hustvl/yolos) 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=1VwYLbGak5c-2P5qdvfWVOeg7DTDYPbro'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = YolosFeatureExtractor.from_pretrained('nickmuchi/yolos-small-finetuned-masks')
model = YolosForObjectDetection.from_pretrained('nickmuchi/yolos-small-finetuned-masks')
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](https://huggingface.co/datasets/imagenet2012) and fine-tuned on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively. 

### Training

This model was fine-tuned for 200 epochs on the face-mask-dataset.

## Evaluation results

This model achieves an AP (average precision) of **53.1**.

Accumulating evaluation results...
DONE (t=0.14s).
IoU metric: bbox
Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.273
Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.532
Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.257
Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.220
Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.341
Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.545
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.154
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.361
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.415
Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.349
Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.469
Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.584