https://github.com/aminehorseman/mobilenet-v2-custom-dataset

Using Keras MobileNet-v2 model with your custom images dataset
https://github.com/aminehorseman/mobilenet-v2-custom-dataset

computer-vision custom-data custom-dataset deep-learning deep-neural-networks digits-recognition image-classification keras keras-models keras-tensorflow machine-learning python python-3 tensorflow tensorflow-models

Last synced: 2 months ago
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Using Keras MobileNet-v2 model with your custom images dataset

• Host: GitHub
• URL: https://github.com/aminehorseman/mobilenet-v2-custom-dataset
• Owner: amineHorseman
• License: gpl-3.0
• Created: 2018-10-02T22:08:49.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-04-21T13:37:37.000Z (almost 6 years ago)
• Last Synced: 2023-03-08T16:32:10.265Z (almost 2 years ago)
• Topics: computer-vision, custom-data, custom-dataset, deep-learning, deep-neural-networks, digits-recognition, image-classification, keras, keras-models, keras-tensorflow, machine-learning, python, python-3, tensorflow, tensorflow-models
• Language: Python
• Size: 20.5 KB
• Stars: 5
• Watchers: 2
• Forks: 6
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# mobilenet-v2-custom-dataset

Using Keras MobileNet-v2 model with your custom images dataset

The Keras implementation of MobileNet-v2 (from Keras-Application package) uses by default famous datasets such as imagenet, cifar in a encoded format. But what if we want to use our own custom dataset?

The problem is that if we load all images in a single numpy array, the memory will quickly overload, that's why in this repository we use keras `ImageDataGenerator` class to generate batches during the runtime. The advantage of using `ImageDataGenerator` to generate batches instream of making a hand-made loop over our dataset is that it is directly supported by keras models and we just have to call `fit_generator` method to train on the batches. Moreover, we can easily activate the data augmentation option.  

Our custom dataset need to have the following structure: for every class create a folder containing .jpg sample images:

```

dataset_folder\

    class1\

        image1.jpg

        image2.jpg

    class2\

        image1.jpg

        image2.jpg

        image3.jpg

```

## How to use?

1. Configure the parameters in config.json

2. Train the model using `python train.py`

3. Evaluate the model on test dataset using: `python test.py`