ai4-metadata.yml

metadata_version: 2.0.0
title: Train an image classifier
summary: Train your own image classifier with your custom dataset. It comes also pretrained on the 1K ImageNet classes.
description: |-
  The deep learning revolution has brought significant advances in a number of fields [1], primarily linked to image and speech recognition. 
  The standardization of image classification tasks like the [ImageNet Large Scale Visual Recognition Challenge](http://www.image-net.org/challenges/LSVRC/) 
  [2] has resulted in a reliable way to compare top performing architectures. 

  This Docker container contains the tools to train an image classifier on your personal dataset.
  It is a highly customizable tool that let's you choose between tens of different [top performing architectures](https://github.com/keras-team/keras-applications) and training parameters. 

  The container also comes with a pretrained general-purpose image classifier trained on ImageNet. 
  The PREDICT method expects an RGB image as input (or the url of an RGB image) and will return a JSON with the top 5 predictions.

  <img class='fit', src='https://raw.githubusercontent.com/ai4os-hub/ai4os-image-classification-tf/main/docs/images/imagenet.png'/>
  
  **References**
  1. Yann LeCun, Yoshua Bengio, and Geofrey Hinton. [Deep learning](https://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf). Nature, 521(7553):436-444, May 2015.
  2. Olga Russakovsky et al. [ImageNet Large Scale Visual Recognition Challenge](https://arxiv.org/abs/1409.0575). International Journal of Computer Vision (IJCV), 115(3):211-252, 2015.
dates:
  created: '2019-01-01'
  updated: '2024-08-12'
links:
  source_code: https://github.com/ai4os-hub/ai4os-image-classification-tf
  docker_image: ai4oshub/ai4os-image-classification-tf
  ai4_template: ai4-template/1.9.9
  dataset: http://www.image-net.org/challenges/LSVRC/
  training_data: null
  citation: http://digital.csic.es/handle/10261/194498
tags:
  - deep learning
  - image classification
  - vo.imagine-ai.eu
  - general purpose
tasks:
  - Computer Vision
  - Classification
categories:
  - AI4 trainable
  - AI4 pre trained
  - AI4 inference
libraries:
  - TensorFlow
  - Keras
data-type:
  - Image