Image Classification
Transformers
Safetensors
pneumonia
feature-extraction
chest_x_ray
medical_imaging
radiology
custom_code
Instructions to use ianpan/pneumonia-cxr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ianpan/pneumonia-cxr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ianpan/pneumonia-cxr", 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/pneumonia-cxr", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 726 Bytes
de36d8f 1ab2c07 de36d8f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | {
"architectures": [
"PneumoniaModel"
],
"auto_map": {
"AutoConfig": "configuration.PneumoniaConfig",
"AutoModel": "modeling.PneumoniaModel"
},
"backbone": "tf_efficientnetv2_s",
"cls_dropout": 0.1,
"cls_num_classes": 1,
"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": [
512,
512
],
"in_chans": 1,
"model_type": "pneumonia",
"seg_dropout": 0.1,
"seg_num_classes": 1,
"torch_dtype": "float32",
"transformers_version": "4.47.0"
}
|