Image Feature Extraction
Transformers
PyTorch
pathology
vision
vit
feature-extraction
knowledge-distillation
Instructions to use luoxd96/PathAGG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use luoxd96/PathAGG with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="luoxd96/PathAGG")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("luoxd96/PathAGG", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 510 Bytes
b612017 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"architectures": ["PathAGGModel"],
"auto_map": {
"AutoConfig": "modeling_pathagg.PathAGGConfig",
"AutoModel": "modeling_pathagg.PathAGGModel"
},
"model_type": "pathagg",
"variant": "vitb",
"img_size": 224,
"patch_size": 14,
"embed_dim": 768,
"depth": 12,
"num_heads": 12,
"num_register_tokens": 4,
"mlp_ratio": 4.0,
"qkv_bias": true,
"init_values": null,
"no_embed_class": false,
"return_patch": false,
"torch_dtype": "float32",
"transformers_version": "4.40.0"
}
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