Instructions to use timm/resnet200d.ra2_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/resnet200d.ra2_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/resnet200d.ra2_in1k", pretrained=True) - Transformers
How to use timm/resnet200d.ra2_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/resnet200d.ra2_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/resnet200d.ra2_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d4b2604f8f938af6e6c207bbd0f846cb8818c995044f7229b94112a5d44909f
- Size of remote file:
- 260 MB
- SHA256:
- c89c911e50aeecbfe4ae118276a97a6cd8dc56ed42659c34b3c5115fe8ff4d78
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