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DeepTrackAI / repository
DeepTrack2 is a modular Python library for generating, manipulating, and analyzing image data pipelines for machine learning and experimental imaging.
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DeepTrack2 is a modular Python library for generating, manipulating, and analyzing image data pipelines for machine learning and digital microscopy.
TensorFlow Compatibility Notice: DeepTrack2 version 2.0 and subsequent do not support TensorFlow. If you need TensorFlow support, please install the legacy version 1.7.
The following quick-start guide is intended for complete beginners to understand how to use DeepTrack2, from installation to training your first model. Let's get started!
DeepTrack2 requires at least python 3.10.
To install DeepTrack2, open a terminal or command prompt and run:
pip install deeptrack
or
python -m pip install deeptrack
This will automatically install the required dependencies.
Here you find a series of notebooks providing an overview of the core features of DeepTrack2 and how to use them:
DTGS101 Introduction to DeepTrack2
Overview of how to use DeepTrack2. Creating images combining DeepTrack2 features, extracting properties, and using them to train a neural network.
DTGS106 Simulating Different Image Modalities
Simulating a spherical particle with different image modalities and generating a movie where this particle diffuses with passive Brownian motion.
DTGS111 Loading Image Files Using Sources
Using sources to load image files and to train a neural network.
DTGS121 Tracking a Point Particle with a CNN
Tracking a point particle with a convolutional neural network (CNN) using simulated particles resolved through a microscope with aberrations.
Characterizing spherical aberrations of an optical device with a convolutional neural network (CNN) using simulated images in the training process.
DTGS127 Characterizing Aberrations with Optuna
Characterizing aberrations of an optical device with the optimization framework Optuna.
DTGS131 Tracking Multiple Particles with a U-Net
Tracking multiple particles using a U-net trained on simulated images.
DTGS141 Distinguishing Particles with a U-Net
Tracking and distinguishing particles of different sizes in brightfield microscopy using a U-net trained on simulated images.
DTGS151 Unsupervised Object Detection
Single-shot unsupervised object detection using LodeSTAR.
DTGS161 Fitting Using PyTorch Gradients
Using PyTorch gradients to fit a Gaussian generated by a DeepTrack2 pipeline.
DTGS171 Creating Custom Scatterers
Creating custom scatterers of arbitrary shapes.
DTGS172 Simulating Bacteria
Creating custom scatterers in the shape of bacteria.
These are examples of how DeepTrack2 can be used on real datasets:
DTEx211 MNIST
Training a fully connected neural network to identify handwritten digits using MNIST dataset.
DTEx212 Single Particle Tracking
Tracks experimental videos of a single particle.
DTEx213 Multi-Particle Tracking
Detecting quantum dots in a low SNR image.
DTEx214 Particle Feature Extraction
Extracting the radius and refractive index of particles.
DTEx215 Cell Counting
Counting the number of cells in fluorescence images.
DTEx216 3D Multi-Particle tracking
Tracking multiple particles in 3D for holography.
DTEx217 GAN image generation
Using a GAN to create cell image from masks.
Specific examples for label-free particle tracking using LodeSTAR:
DTEx231A LodeSTAR to Detect Particles