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
| 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 | |