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

alt text

alt text

Owner
Jakub Cieslik
yet another data scientist
Jakub Cieslik
GitHub Repository
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