Instructions to use eyad-ai/SmartChestXRay with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use eyad-ai/SmartChestXRay with PEFT:
Task type is invalid.
- Transformers
How to use eyad-ai/SmartChestXRay with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("eyad-ai/SmartChestXRay", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download backbone/config.json from eyad-ai/SmartChestXRay: direct link, hf CLI and curl.
- Browser
- Download file 899 Bytes
-
https://huggingface.co/eyad-ai/SmartChestXRay/resolve/main/backbone/config.json
- Command line
-
hf download hf://eyad-ai/SmartChestXRay/backbone/config.json
-
curl -L -o config.json https://huggingface.co/eyad-ai/SmartChestXRay/resolve/main/backbone/config.json
899 Bytes
| { | |
| "apply_layernorm": true, | |
| "architectures": [ | |
| "Dinov2Model" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "drop_path_rate": 0.0, | |
| "dtype": "float32", | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_size": 768, | |
| "image_size": 518, | |
| "initializer_range": 0.02, | |
| "layer_norm_eps": 1e-06, | |
| "layerscale_value": 1.0, | |
| "mlp_ratio": 4, | |
| "model_type": "dinov2", | |
| "num_attention_heads": 12, | |
| "num_channels": 3, | |
| "num_hidden_layers": 12, | |
| "out_features": [ | |
| "stage12" | |
| ], | |
| "out_indices": [ | |
| 12 | |
| ], | |
| "patch_size": 14, | |
| "qkv_bias": true, | |
| "reshape_hidden_states": true, | |
| "stage_names": [ | |
| "stem", | |
| "stage1", | |
| "stage2", | |
| "stage3", | |
| "stage4", | |
| "stage5", | |
| "stage6", | |
| "stage7", | |
| "stage8", | |
| "stage9", | |
| "stage10", | |
| "stage11", | |
| "stage12" | |
| ], | |
| "transformers_version": "5.13.1", | |
| "use_mask_token": true, | |
| "use_swiglu_ffn": false | |
| } | |