Instructions to use timm/tf_efficientnetv2_m.in21k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/tf_efficientnetv2_m.in21k with timm:
import timm model = timm.create_model("hf-hub:timm/tf_efficientnetv2_m.in21k", pretrained=True) - Transformers
How to use timm/tf_efficientnetv2_m.in21k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/tf_efficientnetv2_m.in21k") 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/tf_efficientnetv2_m.in21k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/tf_efficientnetv2_m.in21k: direct link, hf CLI and curl.
- Browser
- Download file 325 MB
-
https://huggingface.co/timm/tf_efficientnetv2_m.in21k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/tf_efficientnetv2_m.in21k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/tf_efficientnetv2_m.in21k/resolve/main/pytorch_model.bin
325 MB
- Xet hash:
- 467d213642f96c1c31f42e336d3d4c633b49cda9b14093286e383d770acc04ad
- Size of remote file:
- 325 MB
- SHA256:
- 83731ade6781264423a7c1fb134863e4cac9d40317f8c349359697c17dafcd7d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.