Image Segmentation
Transformers
Safetensors
cxr_basic
feature-extraction
chest_x_ray
x_ray
medical_imaging
radiology
segmentation
classification
lungs
heart
custom_code
Instructions to use ianpan/chest-x-ray-basic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ianpan/chest-x-ray-basic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="ianpan/chest-x-ray-basic", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ianpan/chest-x-ray-basic", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "CXRModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration.CXRConfig", | |
| "AutoModel": "modeling.CXRModel" | |
| }, | |
| "backbone": "tf_efficientnetv2_s", | |
| "cls_dropout": 0.1, | |
| "cls_num_classes": 5, | |
| "decoder_attention_type": null, | |
| "decoder_center_block": false, | |
| "decoder_channels": [ | |
| 256, | |
| 128, | |
| 64, | |
| 32, | |
| 16 | |
| ], | |
| "decoder_n_blocks": 5, | |
| "decoder_norm_layer": "bn", | |
| "encoder_channels": [ | |
| 24, | |
| 48, | |
| 64, | |
| 160, | |
| 256 | |
| ], | |
| "feature_dim": 256, | |
| "img_size": [ | |
| 320, | |
| 320 | |
| ], | |
| "in_chans": 1, | |
| "model_type": "cxr_basic", | |
| "seg_dropout": 0.1, | |
| "seg_num_classes": 4, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.47.0" | |
| } | |