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iMTFA and MTFA

This is the code for the paper "Incremental Few-Shot Instance Segmentation", presented at CVPR2021.

Link to paper
Link to supplemental material

@InProceedings{Ganea_2021_CVPR,
    author    = {Ganea, Dan Andrei and Boom, Bas and Poppe, Ronald},
    title     = {Incremental Few-Shot Instance Segmentation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {1185-1194}
}

This code is based on Detectron2 and parts of TFA's source code. We have edited the detectron2 source code to better match the few-shot instance segmentation task.

We advise the users to create a new conda environment and install our source code in the same way as the detectron2 source code. See INSTALL.md.

After setting up the dependencies, installation should simply be:

pip install -e . in this folder.

Configurations

For simplicity in running/automating the scripts, the naming scheme of these experiments is different than that in the paper.

All configs can be found in the configs/coco-experiments directory.

MTFA's first training stage is: configs/coco-experiments/mask_rcnn_R_50_FPN_fc_fsdet_base.yaml

iMTFA's first training stage is: configs/coco-experiments/mask_rcnn_R_50_FPN_fc_fullclsag_base.yaml iMTFA's second training stage is: configs/coco-experiments/mask_rcnn_R_50_FPN_ft_fullclsag_cos_bh_base.yaml

1shot,5shot and 10_shot MTFA configs for the NOVEL classes are named as such:

configs/coco-experiments/mask_rcnn_R_50_FPN_ft_fsdet_cos_novel_{shot_number}shot.yaml

1shot,5shot and 10_shot MTFA configs for the ALL classes are named as such:

configs/coco-experiments/mask_rcnn_R_50_FPN_ft_fsdet_cos_correct_all_{shot_number}shot.yaml

1shot,5shot and 10_shot iMTFA configs for the NOVEL + ALL classes are named as such:

configs/coco-experiments/mask_rcnn_R_50_FPN_fullclsag_metric_avg_cos_bh_normalized_{all/novel}_{shot_number}shot.yaml

For COCO-Split-2 in the paper, all experiments have an appended _split1 or are in the related split1 directory.

Experiments on COCO2VOC

iMTFA :

configs/coco-experiments/mask_rcnn_R_50_FPN_fullclsag_metric_avg_cos_bh_normalized_novel_1shot_LIKE_FGN_CSSCALE_100_TEST_VOC.yaml

MTFA:

configs/coco-experiments/mask_rcnn_R_50_FPN_ft_fsdet_cos_novel_1shot_LIKE_FGN_TEST_VOC.yaml

Experiments for every shot are generated in the tools/run_experiments.py script. This is why there are no configs for the alpha value. These are generated automatically.

The *_metric_avg_directcos_normalized_moreiters_novel_{}shot experiments are for the One-Shot-Cosine and the _metric_avg_FC experiments are for the One-Shot-FC, detailed in the paper

Models

Models temporarily available here:

https://1drv.ms/u/s!Ako0GB-Fly5dgaI6a9w7V7qGexkiiA?e=UUzeYV

Note: Not 100% of the models here are in the paper. The naming scheme follows the naming scheme above. You'll notice that in order to have a fair comparison between a first and second stage of training, we use a 'moreiters' setup for the first stage. This is to account for a larger number of iteration steps when training. In practice, we notice that training the cosine head for more iterations does help a bit, but not enough vs the two-stage approach.

Running the scripts

Currently the scripts are somewhat convoluted to run. We entend to make this documentation nicer in the near future.

For now:

To run the training, the tools/run_train.py script is used. Run it with -h to get all available options

Seting up the data

We use the same datasets folder used in Detectron2 and TFA. Download and unzip the cocosplit folder here.

Also, setup a coco directory in datasets, exactly the same way as TFA. For this, just download COCO2014 train + val and place them in trainval, similarly download COCO2014 test.

Setting up the data for the VOC scenario can be done either with manually converted VOC->COCO or with the data here:

https://1drv.ms/u/s!Ako0GB-Fly5dgfcp-sBbUO-NE1k9cA?e=xcTCnw

Furthermore, to use this VOC dataset you might need to edit the builtin.py file which registers the VOC dataset via a register_coco_instances call. Editing that should be trivial.

Generating the few-shots

See prepare_coco_few_shot.py for generating them manually, but the cocosplit folder provided above already includes the splits

Results

The main results can be found in the paper_results_and_supp folder

Aggregating the results

tools/aggregate_seeds.py is the script which produces averages of all shots for an experiment. tools/aggregate_to_csv.py produces CSV files for all aggregate seeds for an experiment.

Additional comments

Additional explanations will be available soon.

Note: Not all experiments in configs are used. Not all experiments in configs/coco-experiments/ are used in the paper.