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
| { | |
| "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" | |
| } | |