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Usage

The anchor free models used in this benchmark is based on the centermask2 architecture and the anchor based models are based on the detectron2-ResNeSt architectures, which is both built upon the detectron2 library.

The models trained for the LIVECell paper was done so in a distributed fashion on 8 Nvidia V100 GPUS. To help others reproduce our results, we provide pre-trained models and config files.

| Architecture | Dataset | box AP | mask AP | download | ------------ | : ------ : | : ---- : | :----:| :-----: | | Anchor_free | LIVECell | 48.45 | 47.78 | config / model | | | A172 | 31.49 | 34.57 | config / model |
| | BT-474 | 42.12 | 42.60 | config / model |
| | BV-2 | 42.62 | 45.69 | config / model |
| | Huh7 | 42.44 | 45.85 | config / model |
| | MCF7 | 36.53 | 37.30 | config / model |
| | SH-SY5Y | 25.20 | 23.91 | config / model |
| | SkBr3 | 64.35 | 65.85 | config / model |
| | SK-OV-3 | 46.43 | 49.39 | config / model |
| Anchor_based | LIVECell | 48.43 | 47.89 | config / model |
| | A172 | 36.37 | 38.02 | config / model |
| | BT-474 | 43.25 | 43.00 | config / model |
| | BV-2 | 54.36 | 52.60 | config / model |
| | Huh7 | 52.79 | 51.83 | config / model |
| | MCF7 | 37.53 | 37.94 | config / model |
| | SH-SY5Y | 27.87 | 24.92 | config / model |
| | SkBr3 | 64.41 | 65.39 | config / model |
| | SK-OV-3 | 53.29 | 54.12 | config / model |

To use our fully trained models download them from our S3 bucket, and use it togheter with appropriate config file as described below in the [traing and evaluation section](#Training and evaluation)

Installation

Requirements:

  • Linux or macOS with Python ≥ 3.6
  • PyTorch ≥ 1.3
  • torchvision that matches the PyTorch installation. You can install them together at pytorch.org to make sure of this.
  • OpenCV, optional, needed by demo and visualization
  • pycocotools: pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'

detectron2

Build from source

python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'

Or, to install it from a local clone:

git clone https://github.com/facebookresearch/detectron2.git
python -m pip install -e detectron2

Or if you are on macOS

CC=clang CXX=clang++ python -m pip install -e detectron2

To install a pre-built detectron for different torch and cuda versions and further information, see the detectron2 install document https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md

Model specific instructions

Anchor-free (centermask2)

Retrive the centermask2 code:

git clone https://github.com/youngwanLEE/centermask2.git

For further information, on installation and usage, see the centermask2 documentation

Anchor-based (detectron2-ResNeSt)

Retrive the detectron2-ResNeSt code:

git clone https://github.com/zhanghang1989/detectron2-ResNeSt.git

For further information, on installation and usage, see the detectron2-ResNeSt documentation

Common Installation Issues

  • Not compiled with GPU support" or "Detectron2 CUDA Compiler: not available".
CUDA is not found when building detectron2. You should make sure
python -c 'import torch; from torch.utils.cpp_extension import CUDA_HOME; print(torch.cuda.is_available(), CUDA_HOME)'
print valid outputs at the time you build detectron2.

Training and evaluation

Register LIVECell dataset

Using a custom dataset such as LIVECell together with the detectron2 code base is done by first registering the dataset via the detectron2 python API. In practice this can be done adding the following code to the train_net.py file in the cloned centermask2 repo:

https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html

from detectron2.data.datasets import register_coco_instances
register_coco_instances([dataset_name], {}, [/path/coco/annotations.json], [path/to/image/dir])

Were dataset_name will be the name of your dataset and will be how you decide what dataset to use in your config file. Per default, the config file will point to TRAIN and TEST, so registering a test dataset as TEST will work directly with the provided config files, for other names, make sure to update your config file accordingly.

  • In the config file change the dataset entries with the name used to register the dataset.
  • Set the output directory in the config file to save the models and results.

Train

To train a model, change the OUTPUT directory in the config file to where the models and checkpoints should be saved. Make sure you follow the previous step and register a TRAIN and TEST dataset, cd into the cloned directory (centermask2 or detectron2-ResNeSt), and run the following code:

python tools/train_net.py --num-gpus 8  --config-file your_config.yaml

To train a model on the dataset defined in you_config.yaml with 8 gpus.

Evaluate

To evaluate a model, make sure to register a TEST dataset and point to it in your config file and cd into the cloned directory (centermask2 or detectron2-ResNeSt), then run the following code

python train_net.py  --config-file your_config.yaml --eval-only MODEL.WEIGHTS /path/to/checkpoint_file.pth

This will evaluate a model defined in your_config.yaml with the weights saved in /path/to/checkpoint_file.pth

For further details on training, testing and inference, visit the centermask2 or detectron2-ResNeSt docs