nickmuchi/yolos-small-finetuned-masks

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:

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 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

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