Image Classification
Transformers
PyTorch
Safetensors
efficientnet
chest-xray
efficientnet-b0
medical-ai
radiology
deep-learning
Eval Results (legacy)
Instructions to use Dragonscypher/rayz_EfficientNet_B0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dragonscypher/rayz_EfficientNet_B0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dragonscypher/rayz_EfficientNet_B0") 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("Dragonscypher/rayz_EfficientNet_B0") model = AutoModelForImageClassification.from_pretrained("Dragonscypher/rayz_EfficientNet_B0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Dragonscypher/rayz_EfficientNet_B0: direct link, hf CLI and curl.
- Browser
- Download file 16.4 MB
-
https://huggingface.co/Dragonscypher/rayz_EfficientNet_B0/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Dragonscypher/rayz_EfficientNet_B0/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Dragonscypher/rayz_EfficientNet_B0/resolve/main/pytorch_model.bin
16.4 MB
- Xet hash:
- 07f4b0a10b513e156af0770172d55088c4f75e0738d105581ebbcec407bedaa1
- Size of remote file:
- 16.4 MB
- SHA256:
- 7cdb9ade471e74794a948386ae4970cf63c631d7481e0b8acfc4ea799830b634
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