mwalmsley / zoobot-encoder-tf_efficientnetv2_s

huggingface.co
Total runs: 18
24-hour runs: 0
7-day runs: 0
30-day runs: 13
Model's Last Updated: Abril 05 2024
image-classification

Introduction of zoobot-encoder-tf_efficientnetv2_s

Model Details of zoobot-encoder-tf_efficientnetv2_s

Model card for zoobot-encoder-tf_efficientnetv2_s

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)

Runs of mwalmsley zoobot-encoder-tf_efficientnetv2_s on huggingface.co

18
Total runs
0
24-hour runs
0
3-day runs
0
7-day runs
13
30-day runs

More Information About zoobot-encoder-tf_efficientnetv2_s huggingface.co Model

More zoobot-encoder-tf_efficientnetv2_s license Visit here:

https://choosealicense.com/licenses/apache-2.0

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zoobot-encoder-tf_efficientnetv2_s install

zoobot-encoder-tf_efficientnetv2_s is an open source model from GitHub that offers a free installation service, and any user can find zoobot-encoder-tf_efficientnetv2_s on GitHub to install. At the same time, huggingface.co provides the effect of zoobot-encoder-tf_efficientnetv2_s install, users can directly use zoobot-encoder-tf_efficientnetv2_s installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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Url of zoobot-encoder-tf_efficientnetv2_s

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