Instructions to use sgonzalez2000/dermai-efficientnet-b0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sgonzalez2000/dermai-efficientnet-b0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sgonzalez2000/dermai-efficientnet-b0") 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("sgonzalez2000/dermai-efficientnet-b0") model = AutoModelForImageClassification.from_pretrained("sgonzalez2000/dermai-efficientnet-b0") - Notebooks
- Google Colab
- Kaggle
| base_model: google/efficientnet-b0 | |
| library_name: transformers | |
| license: apache-2.0 | |
| tags: | |
| - image-classification | |
| - skin-lesion | |
| - ham10000 | |
| # dermai-efficientnet-b0 | |
| Fine-tuned [google/efficientnet-b0](https://huggingface.co/google/efficientnet-b0) for 7-class | |
| skin lesion classification on HAM10000. Part of the DermAI explainability | |
| project comparing CNN and Vision Transformer explanations. | |