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")# pip install -U transformers accelerate # 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
End of training
Browse files
README.md
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metrics:
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [google/efficientnet-b0](https://huggingface.co/google/efficientnet-b0) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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## Model description
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metrics:
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type: accuracy
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value: 0.7625
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [google/efficientnet-b0](https://huggingface.co/google/efficientnet-b0) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.8552
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- Accuracy: 0.7625
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## Model description
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