Instructions to use timm/dm_nfnet_f0.dm_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/dm_nfnet_f0.dm_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/dm_nfnet_f0.dm_in1k", pretrained=True) - Transformers
How to use timm/dm_nfnet_f0.dm_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/dm_nfnet_f0.dm_in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/dm_nfnet_f0.dm_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 632 Bytes
7cb8ea6 | 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 | {
"architecture": "dm_nfnet_f0",
"num_classes": 1000,
"num_features": 3072,
"pretrained_cfg": {
"tag": "dm_in1k",
"custom_load": false,
"input_size": [
3,
192,
192
],
"test_input_size": [
3,
256,
256
],
"fixed_input_size": false,
"interpolation": "bicubic",
"crop_pct": 0.9,
"crop_mode": "squash",
"mean": [
0.485,
0.456,
0.406
],
"std": [
0.229,
0.224,
0.225
],
"num_classes": 1000,
"pool_size": [
6,
6
],
"first_conv": "stem.conv1",
"classifier": "head.fc"
}
} |