Image Classification
Transformers
Safetensors
mammo
feature-extraction
mammography
cancer
breast_cancer
radiology
breast_density
custom_code
Instructions to use ianpan/mammoscreen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ianpan/mammoscreen with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ianpan/mammoscreen", 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/mammoscreen", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "MammoEnsemble" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration.MammoConfig", | |
| "AutoModel": "modeling.MammoEnsemble" | |
| }, | |
| "backbone": "tf_efficientnetv2_s", | |
| "dropout": 0.1, | |
| "feature_dim": 1280, | |
| "image_sizes": [ | |
| [ | |
| 2048, | |
| 1024 | |
| ], | |
| [ | |
| 1920, | |
| 1280 | |
| ], | |
| [ | |
| 1536, | |
| 1536 | |
| ] | |
| ], | |
| "in_chans": 1, | |
| "model_type": "mammo", | |
| "num_classes": 5, | |
| "num_models": 3, | |
| "pad_to_aspect_ratio": [ | |
| true, | |
| true, | |
| false | |
| ], | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.47.0" | |
| } | |