https://github.com/abailoni/lsimasks

Proposal-free instance segmentation from Latent Single-Instance Masks
https://github.com/abailoni/lsimasks

Last synced: 24 days ago
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Proposal-free instance segmentation from Latent Single-Instance Masks

• Host: GitHub
• URL: https://github.com/abailoni/lsimasks
• Owner: abailoni
• License: mit
• Created: 2020-12-09T11:38:01.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2021-09-26T13:14:34.000Z (over 3 years ago)
• Last Synced: 2024-11-05T11:55:25.303Z (2 months ago)
• Language: Python
• Size: 51.8 KB
• Stars: 1
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# LSIMasks

Proposal-free instance segmentation from Latent Single-Instance Masks

### Installation (on linux)

If you plan to use the code to train your model, then you will need to install some extra packages:

- Clone the repository: `git clone https://github.com/abailoni/LSIMasks.git`

- `cd LSIMasks`

- `chmod +x ./install_dependencies.sh`

- To install the dependencies, you will need [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) or [miniconda](https://docs.conda.io/en/latest/miniconda.html)

- Install the dependencies and the package by running `./install_dependencies.sh`. While the script is running, you will need to confirm twice.

- The script will create a new conda environment called `LSIMasks` including all you need

### Starting the training from scratch:

To start the training, run the following command:

```

CUDA_VISIBLE_DEVICES=0 ipython experiments/cremi/train_model.py --  --DATA_HOMEDIR  --inherit main_config.yml

```

(the one just given is a single command: for readability it was split into multiple lines)

### Visualizing the training results in tensorboard

Go to the experiment folder (by default placed in the `experiments/cremi/runs` folder) and then start tensorboard:

`tensorboard --logdir=./ --bind_all`

For this, you will need to install tensorflow, with `pip install --upgrade tensorflow`