Transfer Learning Suite in Keras. Perform transfer learning using any built-in Keras image classification model easily!

GeorgeSeif Last update: Jan 04, 2024

Transfer Learning Suite in Keras

News

Description

This repository serves as a Transfer Learning Suite. The goal is to easily be able to perform transfer learning using any built-in Keras image classification model! Any suggestions to improve this repository or any new features you would like to see are welcome!

You can also check out my Semantic Segmentation Suite.

Models

All of the Keras built in models are made available:

Model	Size	Top-1 Accuracy	Top-5 Accuracy	Parameters	Depth
VGG16	528 MB	0.715	0.901	138,357,544	23
VGG19	549 MB	0.727	0.910	143,667,240	26
ResNet50	99 MB	0.759	0.929	25,636,712	168
Xception	88 MB	0.790	0.945	22,910,480	126
InceptionV3	92 MB	0.788	0.944	23,851,784	159
InceptionResNetV2	215 MB	0.804	0.953	55,873,736	572
MobileNet	17 MB	0.665	0.871	4,253,864	88
DenseNet121	33 MB	0.745	0.918	8,062,504	121
DenseNet169	57 MB	0.759	0.928	14,307,880	169
DenseNet201	80 MB	0.770	0.933	20,242,984	201
NASNetMobile	21 MB	NA	NA	5,326,716	NA
NASNetLarge	342 MB	NA	NA	88,949,818	NA

Files and Directories

main.py: Training and Prediction mode
utils.py: Helper utility functions
checkpoints: Checkpoint files for each epoch during training
Predictions: Prediction results

Installation

This project has the following dependencies:

Numpy sudo pip install numpy
OpenCV Python sudo apt-get install python-opencv
TensorFlow sudo pip install --upgrade tensorflow-gpu
Keras sudo pip install keras

Usage

The only thing you have to do to get started is set up the folders in the following structure:

├── "dataset_name"                   
|   ├── train
|   |   ├── class_1_images
|   |   ├── class_2_images
|   |   ├── class_X_images
|   |   ├── .....
|   ├── val
|   |   ├── class_1_images
|   |   ├── class_2_images
|   |   ├── class_X_images
|   |   ├── .....
|   ├── test
|   |   ├── class_1_images
|   |   ├── class_2_images
|   |   ├── class_X_images
|   |   ├── .....

Then you can simply run main.py! Check out the optional command line arguments:

usage: main.py [-h] [--num_epochs NUM_EPOCHS] [--mode MODE] [--image IMAGE]
               [--continue_training CONTINUE_TRAINING] [--dataset DATASET]
               [--resize_height RESIZE_HEIGHT] [--resize_width RESIZE_WIDTH]
               [--batch_size BATCH_SIZE] [--dropout DROPOUT] [--h_flip H_FLIP]
               [--v_flip V_FLIP] [--rotation ROTATION] [--zoom ZOOM]
               [--shear SHEAR] [--model MODEL]

optional arguments:
  -h, --help            show this help message and exit
  --num_epochs NUM_EPOCHS
                        Number of epochs to train for
  --mode MODE           Select "train", or "predict" mode. Note that for
                        prediction mode you have to specify an image to run
                        the model on.
  --image IMAGE         The image you want to predict on. Only valid in
                        "predict" mode.
  --continue_training CONTINUE_TRAINING
                        Whether to continue training from a checkpoint
  --dataset DATASET     Dataset you are using.
  --resize_height RESIZE_HEIGHT
                        Height of cropped input image to network
  --resize_width RESIZE_WIDTH
                        Width of cropped input image to network
  --batch_size BATCH_SIZE
                        Number of images in each batch
  --dropout DROPOUT     Dropout ratio
  --h_flip H_FLIP       Whether to randomly flip the image horizontally for
                        data augmentation
  --v_flip V_FLIP       Whether to randomly flip the image vertically for data
                        augmentation
  --rotation ROTATION   Whether to randomly rotate the image for data
                        augmentation
  --zoom ZOOM           Whether to randomly zoom in for data augmentation
  --shear SHEAR         Whether to randomly shear in for data augmentation
  --model MODEL         Your pre-trained classification model of choice