Instructions to use Hemgg/Facemask-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hemgg/Facemask-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Hemgg/Facemask-detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Hemgg/Facemask-detection") model = AutoModelForImageClassification.from_pretrained("Hemgg/Facemask-detection") - Notebooks
- Google Colab
- Kaggle
Face-Mask-Detection
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0239
- Accuracy: 0.9953
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1218 | 1.0 | 147 | 0.0251 | 0.9953 |
| 0.0186 | 1.99 | 294 | 0.0239 | 0.9953 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
- 2,760
Model tree for Hemgg/Facemask-detection
Base model
google/vit-base-patch16-224-in21k