Image Classification
Transformers
Safetensors
bone_age
feature-extraction
radiology
medical_imaging
x_ray
custom_code
Instructions to use ianpan/bone-age with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ianpan/bone-age with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ianpan/bone-age", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ianpan/bone-age", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from ianpan/bone-age: direct link, hf CLI and curl.
- Browser
- Download file 398 Bytes
-
https://huggingface.co/ianpan/bone-age/resolve/main/config.json
- Command line
-
hf download hf://ianpan/bone-age/config.json
-
curl -L -o config.json https://huggingface.co/ianpan/bone-age/resolve/main/config.json
398 Bytes
| { | |
| "architectures": [ | |
| "BoneAgeEnsembleModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration.BoneAgeConfig", | |
| "AutoModel": "modeling.BoneAgeEnsembleModel" | |
| }, | |
| "backbone": "convnextv2_tiny", | |
| "dropout": 0.1, | |
| "feature_dim": 768, | |
| "in_chans": 2, | |
| "model_type": "bone_age", | |
| "num_classes": 240, | |
| "num_models": 3, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.47.0" | |
| } | |