A deep learning library containing implementations of popular algorithms and extensions to TensorFlow and Keras.
DeepToolKit provides implementations of popular machine learning algorithms, extensions to existing deep learning pipelines using TensorFlow and Keras, and convenience utilities to speed up the process of implementing, training, and testing deep learning models. In addition, DeepToolKit includes an inbuilt computer vision module containing implementations of facial detection and image processing algorithms.
DeepToolKit can be installed directly from the command line:
pip install deeptoolkit
You can then work with it either by importing the library as a whole, or by importing the functionality you need from the relevant submodules.
# Complete library import. import deeptoolkit as dtk # Module and function imports. from deeptoolkit.data import plot_data_cluster from deeptoolkit.blocks import SeparableConvolutionBlock from deeptoolkit.losses import CategoricalFocalLoss
If you want to install DeepToolKit directly from source, (i.e. for local development), then first install the git source:
git clone https://github.com/amogh7joshi/deeptoolkit.git
Then install system requirements and activate the virtual environment. A Makefile is included for installation:
DeepToolKit provides a number of features to either use standalone or integrated in a deep learning model construction pipeline. Below is a high-level list of features in the module. Proper documentation is under construction.
Model Architecture Blocks:
- Generic model architecture blocks, including convolution and depthwise separable convolution blocks, implemented as
tf.keras.layers.Layerobjects so you can directly use them in a Keras model.
- Applied model architecture blocks, including squeeze and excitation blocks and ResNet identity blocks.
from tensorflow.keras.models import Model from tensorflow.keras.layers import MaxPooling2D from tensorflow.keras.layers import Input, Dense, Flatten from deeptoolkit.blocks import ConvolutionBlock # Construct a Keras Functional model like normal. inp = Input((256, 256, 3)) x = ConvolutionBlock(32, kernel_size = (3, 3), activation = 'relu')(inp) x = MaxPooling2D(pool_size = (2, 2))(x) x = ConvolutionBlock(16, kernel_size = (3, 3), activation = 'relu')(x) x = MaxPooling2D(pool_size = (2, 2))(x) x = Flatten()(x) x = Dense(1024, activation = 'relu')(x) x = Dense(10, activation = 'relu')(x) model = Model(inp, x)
- Custom loss functions including binary and categorical focal loss, built as
tf.keras.losses.Lossobjects so you can use them in a Keras model training pipeline as well.
from tensorflow.keras.optimizers import Adam from deeptoolkit.losses import BinaryFocalLoss # Using the model from the above example. model.compile( optimizer = Adam(), loss = BinaryFocalLoss(), metrics = ['accuracy'] )
Data Processing and Visualization:
- Data preprocessing, including splitting data into train, validation, and test sets, and shuffling datasets while keeping data-label mappings intact.
- Data visualization, including cluster visualizations.
import numpy as np from deeptoolkit.data import train_val_test_split X = np.random.random(100) y = np.random.random(100) X_train, X_val, X_test, y_train, y_val, y_test = train_val_test_split(X, y, split = [0.6, 0.2, 0.2])
- Model evaluation resources, including visualization of model training metrics over time.
- A pre-built facial detection model:
deeptoolkit.vision.FacialDetector. A large number of modern computer vision algorithms include a facial detection component, and DeepToolKit's facial detection module provides fast and accurate face detection using OpenCV's DNN implementation. To use it, simply execute the following:
import cv2 from deeptoolkit.vision import FacialDetector # Initialize detector. detector = FacialDetector() # Detect face from image path and save image to path. detector.detect_face('image/path', save = 'image/save/path') # Detect face from existing image and continue to use it. image = cv2.imread('image/path') annotated_image = detector.detect_face(image)
All code in this repository is licensed under the MIT License.
If you notice any issues or bugs in the library, please create an issue under the issues tab. To get started and for more information, see the issue templates.
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