Instructions to use Akshat/DysphagiaCls2label with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Akshat/DysphagiaCls2label with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Akshat/DysphagiaCls2label") 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("Akshat/DysphagiaCls2label") model = AutoModelForImageClassification.from_pretrained("Akshat/DysphagiaCls2label") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
license: openrail
tags:
- image-classification
datasets:
- TUFTS face dataset
Environmental Impact
- CO2 Emissions (in grams): 1.2933
Validation Metrics
- Loss: 0.359
- Accuracy: 0.871
- Precision: 0.909
- Recall: 0.909
- AUC: 0.880
- F1: 0.909
Training Details :
- Pre-trained on Tuft's Face Dataset 10,000 images for 350 epochs using a single NVIDIA A100 GPU
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