Instructions to use aspends/binary_tumor_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aspends/binary_tumor_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="aspends/binary_tumor_classifier") 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("aspends/binary_tumor_classifier") model = AutoModelForImageClassification.from_pretrained("aspends/binary_tumor_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from aspends/binary_tumor_classifier: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/aspends/binary_tumor_classifier/resolve/main/tf_model.h5
- Command line
-
hf download hf://aspends/binary_tumor_classifier/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/aspends/binary_tumor_classifier/resolve/main/tf_model.h5
343 MB
- Xet hash:
- b0e0c5f7c1b5e7b0572c1168ff0d95164eb1447235529240b99a498c2fd7dd8c
- Size of remote file:
- 343 MB
- SHA256:
- e8e287b059e51f8eb26845f39314d99e8b96bf6eac220acd448832c806971946
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