Instructions to use timm/convnextv2_tiny.fcmae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/convnextv2_tiny.fcmae with timm:
import timm model = timm.create_model("hf_hub:timm/convnextv2_tiny.fcmae", pretrained=True) - Transformers
How to use timm/convnextv2_tiny.fcmae with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/convnextv2_tiny.fcmae")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/convnextv2_tiny.fcmae", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 793 Bytes
af59f4a | 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 | {
"architecture": "convnextv2_tiny",
"num_classes": 0,
"num_features": 768,
"pretrained_cfg": {
"tag": "fcmae",
"custom_load": false,
"input_size": [
3,
224,
224
],
"fixed_input_size": false,
"interpolation": "bicubic",
"crop_pct": 0.875,
"crop_mode": "center",
"mean": [
0.485,
0.456,
0.406
],
"std": [
0.229,
0.224,
0.225
],
"num_classes": 0,
"pool_size": [
7,
7
],
"first_conv": "stem.0",
"classifier": "head.fc",
"license": "cc-by-nc-4.0",
"origin_url": "https://github.com/facebookresearch/ConvNeXt-V2",
"paper_name": "ConvNeXt-V2: Co-designing and Scaling ConvNets with Masked Autoencoders",
"paper_ids": "arXiv:2301.00808"
}
} |