Instructions to use timm/maxxvitv2_rmlp_base_rw_224.sw_in12k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/maxxvitv2_rmlp_base_rw_224.sw_in12k with timm:
import timm model = timm.create_model("hf-hub:timm/maxxvitv2_rmlp_base_rw_224.sw_in12k", pretrained=True) - Transformers
How to use timm/maxxvitv2_rmlp_base_rw_224.sw_in12k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/maxxvitv2_rmlp_base_rw_224.sw_in12k") 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/maxxvitv2_rmlp_base_rw_224.sw_in12k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/maxxvitv2_rmlp_base_rw_224.sw_in12k: direct link, hf CLI and curl.
- Browser
- Download file 509 MB
-
https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_224.sw_in12k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/maxxvitv2_rmlp_base_rw_224.sw_in12k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_224.sw_in12k/resolve/main/pytorch_model.bin
509 MB
- Xet hash:
- 4fb6587a1d7ee084eda4f807fa1166af20112b87934350f8e9cdc1f146bea7d4
- Size of remote file:
- 509 MB
- SHA256:
- c178084adbf80b8031359e8f68b433c9ff4397e1f05403ed4cea36a4c39948d1
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