Instructions to use timm/resnet50d.a2_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/resnet50d.a2_in1k with timm:
import timm model = timm.create_model("hf-hub:timm/resnet50d.a2_in1k", pretrained=True) - Transformers
How to use timm/resnet50d.a2_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/resnet50d.a2_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 AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("timm/resnet50d.a2_in1k") model = AutoModelForImageClassification.from_pretrained("timm/resnet50d.a2_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/resnet50d.a2_in1k: direct link, hf CLI and curl.
- Browser
- Download file 103 MB
-
https://huggingface.co/timm/resnet50d.a2_in1k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/resnet50d.a2_in1k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/resnet50d.a2_in1k/resolve/main/pytorch_model.bin
103 MB
- Xet hash:
- 78cc1afc4d040a9242d92e69c04a56b2bc485057082a8807e5b1c866835da45c
- Size of remote file:
- 103 MB
- SHA256:
- 1c04328ffcf7cf092f0d3050801890e0edf600a71c82f85d50f6fa1be5bb8c4e
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