Image Classification
PyTorch
Safetensors
timm
medical-imaging
retinal-imaging
fundus
ophthalmology
multi-label-classification
ensemble
Eval Results (legacy)
Instructions to use Mjolnirslams/retinal-disease-ensemble with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use Mjolnirslams/retinal-disease-ensemble with timm:
import timm model = timm.create_model("hf_hub:Mjolnirslams/retinal-disease-ensemble", pretrained=True) - Notebooks
- Google Colab
- Kaggle
File size: 941 Bytes
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"architecture": "retinal_dual_eye_ensemble",
"backbones": [
"efficientnet_b4",
"inception_resnet_v2"
],
"num_classes": 8,
"dropout": 0.5,
"image_size": 448,
"norm_means": [
[
0.485,
0.456,
0.406
],
[
0.5,
0.5,
0.5
]
],
"norm_stds": [
[
0.229,
0.224,
0.225
],
[
0.5,
0.5,
0.5
]
],
"labels": [
"N",
"D",
"G",
"C",
"A",
"H",
"M",
"O"
],
"label_names": {
"N": "Normal",
"D": "Diabetes",
"G": "Glaucoma",
"C": "Cataract",
"A": "AMD",
"H": "Hypertension",
"M": "Myopia",
"O": "Other"
},
"thresholds": [
0.45268115401268005,
0.5955283641815186,
0.7139220833778381,
0.7515495419502258,
0.5114160180091858,
0.3302590250968933,
0.540062427520752,
0.5132951736450195
],
"weights_file": "model.safetensors"
}
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