Instructions to use iamhmh/autotrain-isic-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iamhmh/autotrain-isic-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="iamhmh/autotrain-isic-classification") 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("iamhmh/autotrain-isic-classification") model = AutoModelForImageClassification.from_pretrained("iamhmh/autotrain-isic-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
tags:
- autotrain
- transformers
- image-classification
base_model: facebook/convnext-base-224-22k-1k
widget:
- src: >-
https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: >-
https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: >-
https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
datasets:
- iamhmh/Skin-Lesion-Dataset-cls
Model Trained Using AutoTrain
- Problem type: Image Classification
Validation Metrics
loss: 0.43574896454811096
f1_macro: 0.7652355174728005
f1_micro: 0.8454706927175843
f1_weighted: 0.8408555162880879
precision_macro: 0.8044349451647841
precision_micro: 0.8454706927175843
precision_weighted: 0.8433307863044311
recall_macro: 0.737854710508238
recall_micro: 0.8454706927175843
recall_weighted: 0.8454706927175843
accuracy: 0.8454706927175843