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