Instructions to use yashshinde0080/regnet_y_400mf-mlxim-finetuned-chest_xray-pneumonia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yashshinde0080/regnet_y_400mf-mlxim-finetuned-chest_xray-pneumonia with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="yashshinde0080/regnet_y_400mf-mlxim-finetuned-chest_xray-pneumonia") 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("yashshinde0080/regnet_y_400mf-mlxim-finetuned-chest_xray-pneumonia") model = AutoModelForImageClassification.from_pretrained("yashshinde0080/regnet_y_400mf-mlxim-finetuned-chest_xray-pneumonia", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 40b525242f6910475f9551636d774d6f978d6971fbc673906c1ae18fdd989c67
- Size of remote file:
- 78.6 MB
- SHA256:
- dd9daca29f5e1523d753f7238d781edb8446f969af0ae3582732180c3619e924
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