Image Feature Extraction
timm
Safetensors
microscopy
single-cell
self-supervised
dinov2
vision-transformer
biology
Instructions to use CSEM-AI4LS/scdino-v2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use CSEM-AI4LS/scdino-v2-base with timm:
import timm model = timm.create_model("hf-hub:CSEM-AI4LS/scdino-v2-base", pretrained=True) - Notebooks
- Google Colab
- Kaggle
File size: 1,314 Bytes
b7d994b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | {
"architecture": "vit_base_patch16_224",
"num_classes": 0,
"num_features": 128,
"global_pool": "token",
"model_args": {
"img_size": 56,
"patch_size": 4,
"in_chans": 5,
"embed_dim": 128,
"depth": 12,
"num_heads": 8,
"mlp_ratio": 4,
"reg_tokens": 8,
"init_values": 1e-05,
"dynamic_img_size": true,
"num_classes": 0
},
"pretrained_cfg": {
"custom_load": false,
"input_size": [
5,
56,
56
],
"fixed_input_size": true,
"interpolation": "bilinear",
"crop_pct": 1.0,
"crop_mode": "center",
"mean": [
0.0972,
0.7408,
0.103,
0.0761,
0.0893
],
"std": [
0.141,
0.118,
0.1694,
0.1051,
0.0972
],
"num_classes": 0,
"pool_size": null,
"first_conv": null,
"classifier": "head"
},
"scdino_variant": "dinov2",
"scdino_preprocessing": {
"norm_type": "clip_max",
"clip_mode": "clip_and_scale",
"input_layout": "HWC",
"max_vals_clip": [
3713.48,
1297.59,
6904.38,
3412.43,
3133.96
],
"mean": [
0.0972,
0.7408,
0.103,
0.0761,
0.0893
],
"std": [
0.141,
0.118,
0.1694,
0.1051,
0.0972
],
"resize": 56
}
}
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