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https://github.com/i008/coco-dataset-explorer
Streamlit tool to explore coco datasets
https://github.com/i008/coco-dataset-explorer
coco-datasets instance-segmentation object-detection streamlit
Last synced: 3 months ago
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Streamlit tool to explore coco datasets
- Host: GitHub
- URL: https://github.com/i008/coco-dataset-explorer
- Owner: i008
- Created: 2020-04-23T09:02:23.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2022-04-13T19:06:39.000Z (almost 3 years ago)
- Last Synced: 2024-04-24T11:54:08.595Z (9 months ago)
- Topics: coco-datasets, instance-segmentation, object-detection, streamlit
- Language: Python
- Size: 8.9 MB
- Stars: 85
- Watchers: 3
- Forks: 14
- Open Issues: 7
-
Metadata Files:
- Readme: README.MD
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README
# Dear visitor,
If you think about using this software - there are better alternatives out there that do the same (and much much more) and are actively maintained.
I recommend you to check out fiftyone:
- https://voxel51.com/docs/fiftyone/
### 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
2. Setup using docker
```sh
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/
```
2. Setup using conda
```sh
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/
```
2. Setup using pip
```sh
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/
```
3. 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
