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Kainmueller-Lab / ACTIS

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ACTIS

Welcome to ACTIS - Augmentation Consistency Training for Instance Segmentation.

Feel free to checkout our method: ACTIS paper

Installation

To install the solution, follow these steps:

  1. Install conda (https://docs.conda.io/en/latest/miniconda.html)
  2. Create a new conda environment conda create -n actis python pip
  3. Activate the environment conda activate actis
  4. Clone the repository git clone https://github.com/Kainmueller-Lab/ACTIS.git
  5. Install our method pip install -e .
  6. use the help command to see how to use the method actis -h

Data

The data used in our study can be found in compressed numpy format inside this repository: data There are three datasets:

Each dataset has the following structure:

├── test │ └── test_data.npz └── train └── train_data.npz ## Parameter file The parameter file is a toml file that contains all the parameters needed to run an experiment. An example parameter file can be found in `src/actis/utils/resources/parameters.toml` The following parameters are possible. | Variable | Default Value | Explanation | |----------------------------|-------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------| | base_dir | actis | The base directory where results should live. | | data | Flywing | The dataset to train on. Can be an absolut path to the data or either of "Flywing", "Mouse", "DSB2018" if paired with setting base_dir to github root. | | checkpoint_path | ./experiments/myexperiment/train/checkpoints/best_model.pth | Where to store your model. | | experiment | myexperiment | Name of your experiment. | | num_annotated | 19 | Number of annotated labels to use. | | batch_size_labeled | 4 | Batch size of labeled data. Total btach size will be the sum of this and the batch_size_unlabeled. Make sure they fit on your GPU. | | batch_size_unlabeled | 8 | Batch size of unlabeled data. Total btach size will be the sum of this and the batch_size_labeled. Make sure they fit on your GPU. | | num_workers | 4 | Number of workers to use for data loading. | | training_steps | 50000 | Number of total training steps to do. | | in_channels | 1 | Input channels to your network architecture. Should be chosen based on your data. | | num_fmaps_out | 3 | Number of classes to predict. | | learning_rate | 0.01 | Learn rate to use. | | seed | 1 | Seed used for sampling. | | semi_loss_weight | 1.0 | Weighting factor for the QuantileLoss loss. | | quantile | 0.2 | Used for quantile loss scheduling. | | quantile_end | 0.1 | Used for quantile loss scheduling. | | quantile_warmup_steps | 5000 | Used for quantile loss scheduling. | | load_student_weights | true | Flag indicating to load student weights | | warmup_steps | 0 | Warmup steps before momentum is used. | | fast_update_slow_model | false | Whether to fast update teacher model. | | size | 128 | Affects GaussianBlur kernel size for mono color augmentations. | | encoder_weights | imagenet | Pre-trained weights used for encoder initialization | | no_moco | false | Do not use a slow teacher model. | | s | 1.0 | Affects mono color augmentations. | ## Running and evaluating experiments Create your parameter.toml file based on your specific needs. Then call the following command to run a supervised training experiment: `actis train_super --log-out=` To run a semi-supervised experiment, call the following command: `actis train_semi --log-out=` To evaluate any trained model call the following command: `actis evaluate --log-out=` ## Citation Please use the citation method from the [CITATION.cff](CITATION.cff) file! You can copy it from the "ABOUT" section of your repository.