
# Image Classification repository template

This is a template repository for image classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps

1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `pipeline.py` `__init__` and `__call__` methods. These methods are called by the Inference API. The `__init__` method should load the model and preload all the elements needed for inference (model, processors, tokenizers, etc.). This is only called once. The `__call__` method performs the actual inference. Make sure to follow the same input/output specifications defined in the template for the pipeline to work.

Example repos
* https://huggingface.co/osanseviero/fastai_cat_vs_dog/tree/main

## How to start
First create a repo in https://hf.co/new. 
Then clone this template and push it to your repo.
```
git clone https://huggingface.co/templates/image-classification
cd image-classification
git remote set-url origin https://huggingface.co/$YOUR_USER/$YOUR_REPO_NAME
git push --force
```
{"library_name": "generic", "tags": ["image-classification"]}
generic image-classification region:us
TAGS
#generic #image-classification #region-us 


# Image Classification repository template

This is a template repository for image classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps

1. Specify the requirements by defining a 'URL' file.
2. Implement the 'URL' '__init__' and '__call__' methods. These methods are called by the Inference API. The '__init__' method should load the model and preload all the elements needed for inference (model, processors, tokenizers, etc.). This is only called once. The '__call__' method performs the actual inference. Make sure to follow the same input/output specifications defined in the template for the pipeline to work.

Example repos
* URL

## How to start
First create a repo in URL 
Then clone this template and push it to your repo.

# Image Classification repository template

This is a template repository for image classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps

1. Specify the requirements by defining a 'URL' file.
2. Implement the 'URL' '__init__' and '__call__' methods. These methods are called by the Inference API. The '__init__' method should load the model and preload all the elements needed for inference (model, processors, tokenizers, etc.). This is only called once. The '__call__' method performs the actual inference. Make sure to follow the same input/output specifications defined in the template for the pipeline to work.

Example repos
* URL ## How to start
First create a repo in URL 
Then clone this template and push it to your repo.
TAGS
#generic #image-classification #region-us 
 # Image Classification repository template

This is a template repository for image classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps

1. Specify the requirements by defining a 'URL' file.
2. Implement the 'URL' '__init__' and '__call__' methods. These methods are called by the Inference API. The '__init__' method should load the model and preload all the elements needed for inference (model, processors, tokenizers, etc.). This is only called once. The '__call__' method performs the actual inference. Make sure to follow the same input/output specifications defined in the template for the pipeline to work.

Example repos
* URL ## How to start
First create a repo in URL 
Then clone this template and push it to your repo.
passage: TAGS
#generic #image-classification #region-us 
# Image Classification repository template

This is a template repository for image classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps

1. Specify the requirements by defining a 'URL' file.
2. Implement the 'URL' '__init__' and '__call__' methods. These methods are called by the Inference API. The '__init__' method should load the model and preload all the elements needed for inference (model, processors, tokenizers, etc.). This is only called once. The '__call__' method performs the actual inference. Make sure to follow the same input/output specifications defined in the template for the pipeline to work.

Example repos
* URL## How to start
First create a repo in URL 
Then clone this template and push it to your repo.