Instructions to use PRadecki/convnextv2-small-k5-in1k-128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use PRadecki/convnextv2-small-k5-in1k-128 with timm:
import timm model = timm.create_model("hf_hub:PRadecki/convnextv2-small-k5-in1k-128", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Model card for convnextv2-small-k5-in1k-128
ConvNeXtV2-Small (k=5) trained from scratch on ImageNet-1K @128x128.
Training Recipe:
- ConvNeXt supervised recipe -- Table 5.
- BF16 AMP
Model Details
- Model: convnextv2_small
- Model Kwargs:
- kernel_sizes: 5
- Input Size:
- 3x128x128
- Top-1 Accuracy: 81.34599998031617
Model Usage
import timm
model = timm.create_model('hf-hub:PRadecki/convnextv2-small-k5-in1k-128', pretrained=True)
model.eval()
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