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:
- fc5cf98d1ed7d4a1138fe9179fc634eaf5c88eb92aa2625bf2991754b287444a
- Size of remote file:
- 5.91 kB
- SHA256:
- fc86c045a3ac8f21a13e88fc5c159249de5ac426cae3065d93c459042e6d1240
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