This repository includes classifier projects made with Tensorflow, Caffe, Keras, Intel Movidius (NCS & NCS2) and OpenVino. We aim to create projects in Python, Java, C++, R etc and compare results to find out which types of classifiers are more accurate.
This repository hosts a collection of classifiers that have been developed by the team using the Python programming language. These classifiers include Caffe, FastAI, Movidius, OpenVino, pure Python and Tensorflow classifiers each project may have multiple classifiers.
| Projects | Language | Description | Status |
|---|---|---|---|
| Caffe Classifiers | Python | AML/ALL classifiers created using the Caffe framework. | Ongoing |
| FastAI Classifiers | Python | AML/ALL classifiers created using the FastAI framework. | Ongoing |
| Keras Classifiers | Python | AML/ALL classifiers created using the Keras framework. | Ongoing |
| Movidius Classifiers | Python | AML/ALL classifiers created using Intel Movidius(NC1/NCS2). | Ongoing |
| OpenVino Classifiers | Python | AML/ALL classifiers created using Intel OpenVino. | Ongoing |
| Pure Python Classifiers | Python | AML/ALL classifiers created using pure Python. | Ongoing |
| Tensorflow Classifiers | Python | AML/ALL classifiers created using the Tensorflow framework. | Ongoing |
This repository hosts a collection of classifiers that have been developed by the team using Python and Intel Movidius NCS/NCS2.
| Project | Language | Description | Status |
|---|---|---|---|
| Movidius NCS | Python | AML/ALL classifiers created using Intel Movidius NCS. | Ongoing |
| Movidius NCS2 | Python | AML/ALL classifiers created using Intel Movidius NCS2 & OpenVino. | Ongoing |
The Peter Moss Acute Myeloid & Lymphoblastic Leukemia Python FastAI classifier projects are a collection of projects that use computer vision programs written using FastAI to classify Acute Myeloid & Lymphoblastic Leukemia in unseen images.
| Model | Project | Language | Description | Status | Author |
|---|---|---|---|---|---|
| Resnet | FastAI Resnet50 Classifier | Python | A FastAI model trained using Resnet50 | Ongoing | Salvatore Raieli / Adam Milton-Barker |
| Resnet | FastAI Resnet50(a) Classifier | Python | A FastAI model trained using Resnet50 | Ongoing | Salvatore Raieli / Adam Milton-Barker |
| Resnet | FastAI Resnet34 Classifier | Python | A FastAI model trained using Resnet34 | Ongoing | Salvatore Raieli / Adam Milton-Barker |
| Resnet | FastAI Resnet18 Classifier | Python | A FastAI model trained using Resnet18 | Ongoing | Salvatore Raieli / Adam Milton-Barker |
The Peter Moss Acute Myeloid & Lymphoblastic Leukemia Python Keras classifier projects are a collection of projects that use computer vision programs written using Keras to classify Acute Myeloid & Lymphoblastic Leukemia in unseen images.
| Dataset | Project | Language | Description | Status | Author |
|---|---|---|---|---|---|
| ALL_IDB2 | QuantisedCode | Python | A model trained using Keras with Tensorflow Backend | Ongoing | Amita Kapoor & Taru Jain |
A series of articles / tutorials by Adam Milton-Barker that take you through attempting to replicate the work carried out in the Acute Myeloid Leukemia Classification Using Convolution Neural Network In Clinical Decision Support System paper.
- Introduction to convolutional neural networks in Caffe*
- Preparing the Acute Lymphoblastic Leukemia dataset
The Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research project encourages and welcomes code contributions, bug fixes and enhancements from the Github.
Please read the CONTRIBUTING document for a full guide to forking our repositories and submitting your pull requests. You will also find information about our code of conduct on this page.
- Adam Milton-Barker - Bigfinite IoT Network Engineer & Intel Software Innovator, Barcelona, Spain
- Salvatore Raieli - PhD Immunolgy / Bioinformaticia, Bologna, Italy
- Dr Amita Kapoor - Delhi University, Delhi, India
We use SemVer for versioning. For the versions available, see Releases.
This project is licensed under the MIT License - see the LICENSE file for details.
We use the repo issues to track bugs and general requests related to using this project.
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