Image Classification
Transformers
Safetensors
efficientnet
Generated from Trainer
Eval Results (legacy)
Instructions to use jhoppanne/SkinCancerClassifier_smote-V0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jhoppanne/SkinCancerClassifier_smote-V0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jhoppanne/SkinCancerClassifier_smote-V0") 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("jhoppanne/SkinCancerClassifier_smote-V0") model = AutoModelForImageClassification.from_pretrained("jhoppanne/SkinCancerClassifier_smote-V0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 541bef7e8e65fd95f12904981bd5a029efc8a5d0083c6fa2bccdb2df2b00b58e
- Size of remote file:
- 5.18 kB
- SHA256:
- c165339fc8e99a1db6322e5558bbbe123df70aef24abe6cce9b0347a2c027691
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