Monk - A computer vision toolkit for everyone 
Why use Monk
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Issue: Want to begin learning computer vision
- Solution: Start with Monk's hands-on study roadmap tutorials
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Issue: Multiple libraries hence multiple syntaxes to learn
- Solution: Monk's one syntax to rule them all - pytorch, keras, mxnet, etc
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Issue: Tough to keep track of all the trial projects while participating in a deep learning competition
- Solution: Use monk's project management and work on multiple prototyping experiments
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Issue: Tough to set hyper-parameters while training a classifier
- Solution: Try out hyper-parameter analyser to find the right fit
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Issue: Looking for a library to build quick solutions for your customer
- Solution: Train, Infer and deploy with monk's low-code syntax
Create real-world Image Classification applications
| Medical Domain | Fashion Domain | Autonomous Vehicles Domain |
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| Agriculture Domain | Wildlife Domain | Retail Domain |
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| Satellite Domain | Healthcare Domain | Activity Analysis Domain |
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...... For more check out the Application Model Zoo!!!!
How does Monk make image classification easy
- Write less code and create end to end applications.
- Learn only one syntax and create applications using any deep learning library - pytorch, mxnet, keras, tensorflow, etc
- Manage your entire project easily with multiple experiments
For whom this library is built
- Students
- Seamlessly learn computer vision using our comprehensive study roadmaps
- Researchers and Developers
- Create and Manage multiple deep learning projects
- Competiton participants (Kaggle, Codalab, Hackerearth, AiCrowd, etc)
- Expedite the prototyping process and jumpstart with a higher rank
Table of Contents
Sample Showcase - Quick Mode
Create an image classifier.
#Create an experiment
ptf.Prototype("sample-project-1", "sample-experiment-1")
#Load Data
ptf.Default(dataset_path="sample_dataset/",
model_name="resnet18",
num_epochs=2)
# Train
ptf.Train()
Inference
predictions = ptf.Infer(img_name="sample.png", return_raw=True);
Compare Experiments
#Create comparison project
ctf.Comparison("Sample-Comparison-1");
#Add all your experiments
ctf.Add_Experiment("sample-project-1", "sample-experiment-1");
ctf.Add_Experiment("sample-project-1", "sample-experiment-2");
# Generate statistics
ctf.Generate_Statistics();
Installation
- CUDA 9.0 :
pip install -U monk-cuda90 - CUDA 9.0 :
pip install -U monk-cuda92 - CUDA 10.0 :
pip install -U monk-cuda100 - CUDA 10.1 :
pip install -U monk-cuda101 - CUDA 10.2 :
pip install -U monk-cuda102 - CPU (+Mac-OS) :
pip install -U monk-cpu - Google Colab :
pip install -U monk-colab - Kaggle :
pip install -U monk-kaggle
For More Installation instructions visit: Link
Study Roadmaps
- Getting started with Monk
- Essential notebooks to use all the monk's features
- Image Processing and Deep Learning
- Learn both the basic and advanced concepts of image processing and deep learning
- Transfer Learning
- Understand transfer learning in the AI field
- Image classification zoo
- A list of 50+ real world image classification examples
Documentation
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Functional Documentation (Will be merged with Latest docs soon)
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Features and Functions (In development):
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Complete Latest Docs (In Progress)
TODO-2020
Features
- Model Visualization
- Pre-processed data visualization
- Learned feature visualization
- NDimensional data input - npy - hdf5 - dicom - tiff
- Multi-label Image Classification
- Custom model development
General
- Functional Documentation
- Tackle Multiple versions of libraries
- Add unit-testing
- Contribution guidelines
- Python pip packaging support
Backend Support
- Tensorflow 2.0 provision support with v1
- Tensorflow 2.0 complete
- Chainer
External Libraries
- TensorRT Acceleration
- Intel Acceleration
- Echo AI - for Activation functions
Connect with the project contributors
Copyright
Copyright 2019 onwards, Tessellate Imaging Private Limited Licensed under the Apache License, Version 2.0 (the "License"); you may not use this project's files except in compliance with the License. A copy of the License is provided in the LICENSE file in this repository.
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