https://github.com/royerlab/ultrack-i2k2023
Ultrack https://github.com/royerlab/ultrack from Images to Knowledge - 2023 workshop
https://github.com/royerlab/ultrack-i2k2023
Last synced: 12 months ago
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Ultrack https://github.com/royerlab/ultrack from Images to Knowledge - 2023 workshop
- Host: GitHub
- URL: https://github.com/royerlab/ultrack-i2k2023
- Owner: royerlab
- License: bsd-3-clause
- Created: 2023-10-17T11:17:11.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-08T00:25:32.000Z (about 2 years ago)
- Last Synced: 2024-06-08T01:34:35.708Z (about 2 years ago)
- Language: Jupyter Notebook
- Size: 2.28 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Ultrack Workshop I2K 2023
[Ultrack](https://github.com/royerlab/ultrack) from Images to Knowledge - 2023 workshop
There are two options to follow this tutorial, locally or with Google Colab.
## Local setup
You must have conda or mamba installed. If you don't have it, you can follow [their instructions](https://mamba.readthedocs.io/en/latest/mamba-installation.html#mamba-install).
First, clone this repository:
```bash
git clone https://github.com/royerlab/ultrack-i2k2023
```
Go to the repository folder:
```bash
cd ultrack-i2k2023
```
Then, you can create a new environment with the following command:
```bash
conda env create --file environment.yml
```
NOTE: If you're using apple silicon, you must use the `environment-apple.yml` file instead.
This will create a new environment called `ultrack-i2k2023`. You can activate it with:
```bash
conda activate ultrack-i2k2023
```
Then, you can browse the notebooks with:
```bash
jupyter lab
```
## Google Colab
For Google Colab, you can follow the links below. You will need a Google account.
To access GPUs on Google Colab, you can go to `Runtime > Change runtime type` and select `GPU` as the hardware accelerator.
- Introduction:
https://colab.research.google.com/github/royerlab/ultrack-i2k2023/blob/main/intro.ipynb
- Multiple hypotheses tracking:
https://colab.research.google.com/github/royerlab/ultrack-i2k2023/blob/main/multiple-labels.ipynb
## Additional Resources
For best performance we recommend using `gurobi`. It's a commercial software, but it's free for academic use. You can get a license [here](https://www.gurobi.com/academia/academic-program-and-licenses/).