
# 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]("https://www.kaggle.com/datasets/andrewmvd/face-mask-detection").

## Evaluation results

This model achieves an AP (average precision) of **53.2**.

Accumulating evaluation results...

IoU metric: bbox

Metrics           | Metric Parameter      | Location    | Dets          | Value |
----------------  | --------------------- | ------------| ------------- | ----- |
Average Precision | (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] | 0.273 |
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 |

{"license": "apache-2.0", "tags": ["object-detection", "face-mask-detection"], "datasets": ["coco", "face-mask-detection"], "metrics": ["average precision", "recall", "IOU"], "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"}], "model-index": [{"name": "yolos-small-finetuned-masks", "results": []}]}
transformers pytorch safetensors yolos object-detection face-mask-detection dataset:coco dataset:face-mask-detection arxiv:2106.00666 license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #yolos #object-detection #face-mask-detection #dataset-coco #dataset-face-mask-detection #arxiv-2106.00666 #license-apache-2.0 #endpoints_compatible #has_space #region-us 

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 by Fang et al. and first released in this repository.


This model was further fine-tuned on the face mask dataset 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 to look for all available YOLOS models.


### How to use


Here is how to use this model:


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 face-mask-dataset.


Evaluation results
------------------


This model achieves an AP (average precision) of 53.2.


Accumulating evaluation results...


IoU metric: bbox




### How to use


Here is how to use this model:


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 face-mask-dataset.


Evaluation results
------------------


This model achieves an AP (average precision) of 53.2.


Accumulating evaluation results...


IoU metric: bbox
TAGS
#transformers #pytorch #safetensors #yolos #object-detection #face-mask-detection #dataset-coco #dataset-face-mask-detection #arxiv-2106.00666 #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 ### How to use


Here is how to use this model:


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 face-mask-dataset.


Evaluation results
------------------


This model achieves an AP (average precision) of 53.2.


Accumulating evaluation results...


IoU metric: bbox
passage: TAGS
#transformers #pytorch #safetensors #yolos #object-detection #face-mask-detection #dataset-coco #dataset-face-mask-detection #arxiv-2106.00666 #license-apache-2.0 #endpoints_compatible #has_space #region-us 
### How to use


Here is how to use this model:


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 face-mask-dataset.


Evaluation results
------------------


This model achieves an AP (average precision) of 53.2.


Accumulating evaluation results...


IoU metric: bbox