Transformers
PyTorch
Safetensors
English
fundus
diabetic retinopathy
classification
Eval Results (legacy)
Instructions to use ClementP/FundusDRGrading-efficientnet_b2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ClementP/FundusDRGrading-efficientnet_b2 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ClementP/FundusDRGrading-efficientnet_b2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architecture": "efficientnet_b2", | |
| "num_classes": 1, | |
| "num_features": 1408, | |
| "pretrained_cfg": { | |
| "tag": "ra_in1k", | |
| "custom_load": false, | |
| "input_size": [ | |
| 3, | |
| 256, | |
| 256 | |
| ], | |
| "test_input_size": [ | |
| 3, | |
| 288, | |
| 288 | |
| ], | |
| "fixed_input_size": false, | |
| "interpolation": "bicubic", | |
| "crop_pct": 1.0, | |
| "crop_mode": "center", | |
| "mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "num_classes": 1000, | |
| "pool_size": [ | |
| 8, | |
| 8 | |
| ], | |
| "first_conv": "conv_stem", | |
| "classifier": "classifier" | |
| } | |
| } |