Instructions to use mwalmsley/zoobot-encoder-efficientnet_b0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use mwalmsley/zoobot-encoder-efficientnet_b0 with timm:
import timm model = timm.create_model("hf-hub:mwalmsley/zoobot-encoder-efficientnet_b0", pretrained=True) - Notebooks
- Google Colab
- Kaggle
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Download README.md from mwalmsley/zoobot-encoder-efficientnet_b0: direct link, hf CLI and curl.
- Browser
- Download file 505 Bytes
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https://huggingface.co/mwalmsley/zoobot-encoder-efficientnet_b0/resolve/main/README.md
- Command line
-
hf download hf://mwalmsley/zoobot-encoder-efficientnet_b0/README.md
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curl -L -o README.md https://huggingface.co/mwalmsley/zoobot-encoder-efficientnet_b0/resolve/main/README.md
505 Bytes
metadata
tags:
- zoobot
- image-feature-extraction
- timm
library_name: timm
license: gpl-3.0
pipeline_tag: image-feature-extraction
Model card for zoobot-encoder-efficientnet_b0
Please see the Zoobot docs for loading and finetuning instructions.
But minimally, you can use this like any timm encoder:
import timm
encoder = timm.create_model('hf_hub:mwalmsley/zoobot-encoder-some-name', pretrained=True, num_classes=0)