Instructions to use timm/resnet50d.ra4_e3600_r224_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/resnet50d.ra4_e3600_r224_in1k with timm:
import timm model = timm.create_model("hf-hub:timm/resnet50d.ra4_e3600_r224_in1k", pretrained=True) - Transformers
How to use timm/resnet50d.ra4_e3600_r224_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/resnet50d.ra4_e3600_r224_in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/resnet50d.ra4_e3600_r224_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update model config and README
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README.md
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@@ -13,7 +13,7 @@ A ResNet image classification model. Trained on ImageNet-1k by Ross Wightman.
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Trained with `timm` scripts using hyper-parameters inspired by the MobileNet-V4 small, mixed with go-to hparams from `timm` and "ResNet Strikes Back".
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A collection of
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## Model Details
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- **Model Type:** Image classification / feature backbone
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Trained with `timm` scripts using hyper-parameters inspired by the MobileNet-V4 small, mixed with go-to hparams from `timm` and "ResNet Strikes Back".
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A collection of hparams (timm .yaml config files) for this training series can be found here: https://gist.github.com/rwightman/f6705cb65c03daeebca8aa129b1b94ad
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## Model Details
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- **Model Type:** Image classification / feature backbone
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