Streamlit tool to explore coco datasets

Related tags

Deep Learningobject-detectioninstance-segmentationstreamlitcoco-datasets
Overview

What is this

This tool given a COCO annotations file and COCO predictions file will let you explore your dataset, visualize results and calculate important metrics.

Running the explorer on example data

You can use the predictions I prepared and explore the results on the COCO validation dataset. The predictions are coming from a Mask R-CNN model trained with mmdetection.

  1. Download (and extract in project directory) the labels, annotations and images:

https://drive.google.com/open?id=1wxIagenNdCt_qphEe8gZYK7H2_to9QXl

  1. Setup using docker
sudo docker run -p 8501:8501 -it -v "$PWD"/coco_data:/coco_data i008/cocoexp:latest  \
    --coco_train /coco_data/ground_truth_annotations.json \
    --coco_predictions /coco_data/predictions.json  \
    --images_path /coco_data/images/
  1. Setup using conda
conda env update
conda activate cocoexplorer
streamlit run coco_explorer.py -- --coco_train ./coco_data/ground_truth_annotations.json --coco_predictions ./coco_data/predictions.json  --images_path ./coco_data/val2017/
  1. Setup using pip
python3 -m venv .venv
. .venv/bin/activate
pip install -r requirements.txt
streamlit run coco_explorer.py -- --coco_train ./coco_data/ground_truth_annotations.json --coco_predictions ./coco_data/predictions.json  --images_path ./coco_data/val2017/
  1. go to http://localhost:8501

Running on your own data

In the same way you can explore your own results. Just follow the official COCO dataset format for annotations and predictions.

Examples

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Owner
Jakub Cieslik
yet another data scientist
Jakub Cieslik
GitHub Repository
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