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")# 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
| { | |
| "do_resize": true, | |
| "size": 224, | |
| "resample": 2, | |
| "do_normalize": true, | |
| "image_mean": [0.485, 0.456, 0.406], | |
| "image_std": [0.229, 0.224, 0.225] | |
| } | |