Instructions to use Docty/Blood-Cell with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Docty/Blood-Cell with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Docty/Blood-Cell") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Docty/Blood-Cell") model = AutoModelForImageClassification.from_pretrained("Docty/Blood-Cell", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 922 Bytes
85fc15d 30a8532 85fc15d | 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 43 | {
"_name_or_path": "google/vit-base-patch16-224-in21k",
"architectures": [
"ViTForImageClassification"
],
"attention_probs_dropout_prob": 0.0,
"encoder_stride": 16,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 768,
"id2label": {
"0": "monocyte",
"1": "ig",
"2": "neutrophil",
"3": "basophil",
"4": "lymphocyte",
"5": "erythroblast",
"6": "eosinophil",
"7": "platelet"
},
"image_size": 224,
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"basophil": "3",
"eosinophil": "6",
"erythroblast": "5",
"ig": "1",
"lymphocyte": "4",
"monocyte": "0",
"neutrophil": "2",
"platelet": "7"
},
"layer_norm_eps": 1e-12,
"model_type": "vit",
"num_attention_heads": 12,
"num_channels": 3,
"num_hidden_layers": 12,
"patch_size": 16,
"qkv_bias": true,
"transformers_version": "4.46.2"
}
|