Instructions to use tta1301/xray-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tta1301/xray-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tta1301/xray-classifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("tta1301/xray-classifier") model = AutoModelForImageClassification.from_pretrained("tta1301/xray-classifier") - Notebooks
- Google Colab
- Kaggle
File size: 1,274 Bytes
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"architectures": [
"ViTForImageClassification"
],
"attention_probs_dropout_prob": 0.0,
"dtype": "float32",
"encoder_stride": 16,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 768,
"id2label": {
"0": "Atelectasis",
"1": "Cardiomegaly",
"2": "Consolidation",
"3": "Edema",
"4": "Effusion",
"5": "Emphysema",
"6": "Fibrosis",
"7": "Hernia",
"8": "Infiltration",
"9": "Mass",
"10": "No Finding",
"11": "Nodule",
"12": "Pleural_Thickening",
"13": "Pneumonia",
"14": "Pneumothorax"
},
"image_size": 224,
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"Atelectasis": 0,
"Cardiomegaly": 1,
"Consolidation": 2,
"Edema": 3,
"Effusion": 4,
"Emphysema": 5,
"Fibrosis": 6,
"Hernia": 7,
"Infiltration": 8,
"Mass": 9,
"No Finding": 10,
"Nodule": 11,
"Pleural_Thickening": 12,
"Pneumonia": 13,
"Pneumothorax": 14
},
"layer_norm_eps": 1e-12,
"model_type": "vit",
"num_attention_heads": 12,
"num_channels": 3,
"num_hidden_layers": 12,
"patch_size": 16,
"pooler_act": "tanh",
"pooler_output_size": 768,
"qkv_bias": true,
"transformers_version": "5.0.0",
"use_cache": false
}
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