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
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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
| 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](https://zoobot.readthedocs.io/en/latest/pretrained_models.html) for loading and finetuning instructions. | |
| But minimally, you can use this like any timm encoder: | |
| ```python | |
| import timm | |
| encoder = timm.create_model('hf_hub:mwalmsley/zoobot-encoder-some-name', pretrained=True, num_classes=0) | |
| ``` | |