Instructions to use seige-ml/DERETFound_AMD_AREDS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use seige-ml/DERETFound_AMD_AREDS with timm:
import timm model = timm.create_model("hf_hub:seige-ml/DERETFound_AMD_AREDS", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Download config.json from seige-ml/DERETFound_AMD_AREDS: direct link, hf CLI and curl.
- Browser
- Download file 585 Bytes
-
https://huggingface.co/seige-ml/DERETFound_AMD_AREDS/resolve/main/config.json
- Command line
-
hf download hf://seige-ml/DERETFound_AMD_AREDS/config.json
-
curl -L -o config.json https://huggingface.co/seige-ml/DERETFound_AMD_AREDS/resolve/main/config.json
585 Bytes
| { | |
| "architecture": "vit_large_patch16_224", | |
| "num_classes": 4, | |
| "num_features": 1024, | |
| "global_pool": "token", | |
| "pretrained_cfg": { | |
| "tag": "augreg_in21k_ft_in1k", | |
| "custom_load": true, | |
| "input_size": [ | |
| 3, | |
| 224, | |
| 224 | |
| ], | |
| "fixed_input_size": true, | |
| "interpolation": "bicubic", | |
| "crop_pct": 0.9, | |
| "crop_mode": "center", | |
| "mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "num_classes": 1000, | |
| "pool_size": null, | |
| "first_conv": "patch_embed.proj", | |
| "classifier": "head" | |
| } | |
| } |