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====== Just Another Noise 2 Noise Implementation (JANNI) ====== | ====== Just Another Noise 2 Noise Implementation (JANNI) ====== | ||
- | JANNI implements a neural network denoising tool described in NVIDIA' | + | JANNI implements a neural network denoising tool described in NVIDIA' |
- | [[https:// | + | |
It can be trained on your data without the need of ground truth images. It supports MRC and TIFF format. | It can be trained on your data without the need of ground truth images. It supports MRC and TIFF format. | ||
- | JANNI can be used a command line tool but also provides an simple interface to integrate into other programs (see [[janni_tutorial# | + | JANNI can be used a command line tool but also provides an simple interface to integrate into other programs (see [[:janni_tutorial# |
* **License**: | * **License**: | ||
- | * **GitHub repository**: | + | * **GitHub repository**: |
+ | </ | ||
+ | |||
+ | {{ : | ||
+ | |||
+ | <note important> | ||
- | This is an example where we applied JANNI: | ||
- | {{ :: | ||
==== Download ==== | ==== Download ==== | ||
+ | |||
=== JANNI === | === JANNI === | ||
- | Version: 0.0.3 | ||
- | Uploaded: | + | Version: 0.2.2 |
+ | |||
+ | Uploaded: | ||
[[https:// | [[https:// | ||
=== JANNI General Model === | === JANNI General Model === | ||
+ | |||
Version: 20190703 (Trained on 1xFalcon 2, 3x Falcon 3, 7xK2 datasets from Arctica / Krios) | Version: 20190703 (Trained on 1xFalcon 2, 3x Falcon 3, 7xK2 datasets from Arctica / Krios) | ||
Uploaded: 03. July 2019 | Uploaded: 03. July 2019 | ||
- | [[https:// | + | [[https:// |
==== Known Issues ==== | ==== Known Issues ==== | ||
+ | |||
- < | - < | ||
==== Installation ==== | ==== Installation ==== | ||
- | The following instructions assume that pip and anaconda or miniconda are available. In case you have crYOLO installed, you can also install it into the crYOLO environment. In this case you can skip step 1-3. | ||
- | 1. In case you have a old JANNI environment installed, you might want to remove the old one with: | + | The following instructions assume that pip and anaconda or miniconda are available. |
+ | |||
+ | 1. In case you have a old JANNI environment installed, you might want to remove the old one with: | ||
< | < | ||
conda env remove --name janni | conda env remove --name janni | ||
+ | |||
</ | </ | ||
- | 2. After that, create a new virtual environment: | + | 2. After that, create a new virtual environment: |
< | < | ||
- | conda create -n janni -c anaconda python=3.6 cudnn=7.1.2 libtiff | + | conda create -n janni -c conda-forge |
</ | </ | ||
- | 3. Activate the environment: | + | 3. Activate the environment: |
< | < | ||
source activate janni | source activate janni | ||
+ | |||
</ | </ | ||
- | 4. Install JANNI form PyPi: | + | Now can install JANNI either for GPUs **or** for CPUs: |
+ | |||
+ | 4a. **For GPU: | ||
< | < | ||
- | pip install janni | + | pip install janni[gpu] |
</ | </ | ||
+ | |||
+ | 4b. **For CPU:** Install JANNI form PyPi for a CPU machine: | ||
+ | < | ||
+ | pip install janni[cpu] | ||
+ | |||
+ | </ | ||
+ | |||
==== Getting started ==== | ==== Getting started ==== | ||
- | [[janni_tutorial|Here you can find a tutorial how to denoise your microgragh | + | |
+ | [[:janni_tutorial|Here you can find a tutorial how to denoise your micrographs | ||
+ | |||
+ | |||
+ | ==== How to cite ==== | ||
+ | |||
+ | You can cite JANNI using the Zenodo DOI: | ||
+ | |||
+ | < | ||
+ | |||
+ | ==== Changelog ==== | ||
+ | |||
+ | **JANNI 0.3:** | ||
+ | |||
+ | * Update dependencies and fix setup. | ||
+ | |||
+ | **JANNI 0.2.2:** | ||
+ | |||
+ | * Fix an issue that JANNI can't denoise a single image (it only worked for folders) (Thanks to Sony Malhotra) | ||
+ | |||
+ | **JANNI 0.2.1:** | ||
+ | |||
+ | * Because of an libary update, JANNI was not working anymore (No supported model) (Thanks to | ||
+ | |||
+ | Sony Malhotra) . | ||
+ | |||
+ | **JANNI 0.2** | ||
+ | |||
+ | * Added support for binning. In case you recorded your movies in super resolution mode, but later want to apply JANNI to binned images, you need to bin your images during training. This now happens when ' | ||
+ | * JANNI now allows to choose between two loss functions: mean squared error (mse) or mean absolute error (mae). The latter is the new default. | ||
+ | |||
+ | **JANNI 0.1.2** | ||
+ | |||
+ | * Fix installation procedure. | ||
+ | |||
+ | **JANNI 0.1.0** | ||
+ | |||
+ | * Add GUI | ||
+ | * API changes for crYOLO 1.5 | ||
+ | |||
+ | **JANNI 0.0.5** | ||
+ | |||
+ | * Downgrade tensorflow and numpy again as it leads to long initialization times for some users. | ||
+ | * | ||
+ | |||
+ | **JANNI 0.0.4** | ||
+ | |||
+ | * Improved selection of validation data | ||
+ | * Add more data augmentation | ||
+ | * Updated libraries (To Tensorflow 1.12.3 and numpy 1.15.4) to make it compatible with crYOLO | ||
+ |