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COCO-ReM (COCO with Refined Masks)

Framework: PyTorch HuggingFace Datasets

Shweta Singh<sup>†</sup>, Aayan Yadav<sup>†</sup>, Jitesh Jain, Humphrey Shi, Justin Johnson, Karan Desai

<sup>†</sup> Equal Contribution

[arxiv] [Dataset Website]

Random examples from COCO-ReM

Introducing COCO-ReM, a set of high-quality instance annotations for COCO images. COCO-ReM improves on imperfections prevailing in COCO-2017 such as coarse mask boundaries, non-exhaustive annotations, inconsistent handling of occlusions, and duplicate masks. Masks in COCO-ReM have a visibly better quality than COCO-2017, as shown below.

COCO and COCO-ReM

Contents

  1. News
  2. Setup Instructions
  3. Download COCO-ReM
  4. Mask Visualization
  5. Evaluation using COCO-ReM
  6. Training with COCO-ReM
  7. Annotation Pipeline
  8. Citation

News

Setup Instructions

Clone the repository, create a conda environment, and install all dependencies as follows:

git clone https://github.com/kdexd/coco-rem.git && cd coco-rem
conda create -n coco_rem python=3.10
conda activate coco_rem

Install PyTorch and torchvision following the instructions on pytorch.org. Install Detectron2, instructions are available here. Then, install the dependencies:

pip install -r requirements.txt
pip install git+https://github.com/facebookresearch/segment-anything.git
pip install git+https://github.com/bowenc0221/boundary-iou-api.git

python setup.py develop

Download COCO-ReM

COCO-ReM is hosted on Huggingface Datasets at @kdexd/coco-rem. Download the annotation files:

for name in trainrem valrem; do
    wget https://huggingface.co/datasets/kdexd/coco-rem/resolve/main/instances_$name.json.zip
    unzip instances_$name.json.zip
done

Dataset organization: COCO and COCO-ReM and must be organized inside datasets directory as follows.

$PROJECT_ROOT/datasets
    — coco/
        — train2017/         # Contains 118287 train images (.jpg files).
        — val2017/           # Contains 5000 val images (.jpg files).
        — annotations/
            — instances_train2017.json
            — instances_val2017.json
    - coco_rem/
            - instances_trainrem.json
            - instances_valrem.json
    -lvis
            - lvis_v1_val.json
            - lvis_v1_train.json

Mask Visualization

We include a lightweight script to quickly visualize masks of COCO-ReM and COCO-2017, both validation and training sets. For example, run the following command to visualize the masks for COCO-ReM validation set:

python scripts/visualize_coco.py \
    --input-json datasets/coco_rem/instances_valrem.json \
    --image-dir datasets/coco/val2017 \
    --output visualization_output

Read the documentation (python scripts/visualize_coco.py --help) for details about other arguments.

Evaluation using COCO-ReM

We support evaluation of all fifty object detectors available in the paper. First, run python checkpoints/download.py to download all the pre-trained models from their official repositories and save them in checkpoints/pretrained_weights.

For example, to evaluate a Mask R-CNN ViTDet-B model using 8 GPUs and calculate average precision (AP) metrics, run the following command:

python scripts/train_net.py --num-gpus 8 --eval-only \
    --config coco_rem/configs/vitdet/mask_rcnn_vitdet_b_100ep.py \
    train.init_checkpoint=checkpoints/pretrained_weights/vitdet/mask_rcnn_vitdet_b_100ep.pkl \
    dataloader.test.dataset.names=coco_rem_val \
    train.output_dir=evaluation_results

Training with COCO-ReM

We also support training ViTDet baselines on COCO-ReM using the Detectron2 library. Run the following command to train using 8 GPUs (with at least 32GB memory):

python scripts/train_net.py --num-gpus 8 \
    --config coco_rem/configs/vitdet/mask_rcnn_vitdet_b_100ep.py \
    dataloader.train.dataset.names=coco_rem_train \
    dataloader.test.dataset.names=coco_rem_val \
    train.output_dir=training_output \
    dataloader.train.total_batch_size=16 train.grad_accum_steps=4

For GPUs with less memory, update the parameters in the last line above: the batch size can be halved and gradient accumulation steps can be doubled, for same results.

Annotation Pipeline

<a id="stage-1-mask-boundary-refinement"></a>

Stage 1: Mask Boundary Refinement (automatic step)

Download checkpoint for SAM from segment-anything repository and place it in checkpoint folder.

Run the following command to refine the boundaries of validation set masks using 8 GPUs:

python scripts/refine_boundaries.py \
    --input-json datasets/coco/annotations/instances_val2017.json \
    --image-dir datasets/coco/val2017 \
    --num-gpus 8 \
    --output datasets/intermediate/cocoval_boundary_refined.json

Read the documentation (python scripts/refine_boundaries.py --help) for details about other arguments.

Use default values for other optional arguments to follow the strategy used in paper.

Do this stage for both COCO and LVIS datasets before the merging stage.

<a id="stage-2-exhaustive-instance-annotation"></a>

Stage 2: Exhaustive Instance Annotation (automatic step)

Run the following command to merge LVIS annotations for validation set of COCO using the strategy described in paper:

python scripts/merge_instances.py \
    --coco-json datasets/intermediate/cocoval_boundary_refined.json \
    --lvis-json datasets/intermediate/lvistrain_boundary_refined.json datasets/intermediate/lvisval_boundary_refined.json \
    --split val \
    --output datasets/intermediate/cocoval_lvis_merged.json

Read the documentation (python scripts/merge_instances.py --help) for details about above arguments.

Merging handpicked (image,category) non exhaustive instances from LVIS in validation set is done in the script of next stage.

<a id="stage-3-correction-of-labeling-errors"></a>

Stage 3: Correction of Labeling Errors

This stage is done only for validation set.

python scripts/correct_labeling_errors.py \
    --input datasets/intermediate/cocoval_lvis_merged.json \
    --output datasets/cocoval_refined.json

Note: For the above json to be COCO-ReM we also have to perform the manual parts of Stage 1 and Stage 2.

Citation

If you found COCO-ReM useful in your research, please consider starring ⭐ us on GitHub and citing 📚 us in your research!

@inproceedings{cocorem,
  title={Benchmarking Object Detectors with COCO: A New Path Forward},
  author={Singh, Shweta and Yadav, Aayan and Jain, Jitesh and Shi, Humphrey and Johnson, Justin and Desai, Karan},
  journal={ECCV},
  year={2024}
}