Streamlit tool to explore coco datasets

Last update: Dec 16, 2022

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.

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

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

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/

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/

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/

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.

Streamlit tool to explore coco datasets

Related tags

Overview

What is this

Running the explorer on example data

Running on your own data

Examples

Owner

Jakub Cieslik

Training, generation, and analysis code for Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics

Data and extra materials for the food safety publications classifier

Code for the Active Speakers in Context Paper (CVPR2020)

Robust Consistent Video Depth Estimation

Quickly comparing your image classification models with the state-of-the-art models (such as DenseNet, ResNet, ...)

Face recognition project by matching the features extracted using SIFT.

Implementation of ProteinBERT in Pytorch

Official and maintained implementation of the paper "OSS-Net: Memory Efficient High Resolution Semantic Segmentation of 3D Medical Data" [BMVC 2021].

A Human-in-the-Loop workflow for creating HD images from text

Exemplo de implementação do padrão circuit breaker em python

Segmentation for medical image.

The code repository for EMNLP 2021 paper "Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization".

A curated list of awesome resources related to Semantic Search🔎 and Semantic Similarity tasks.

unet-family: Ultimate version

Group Activity Recognition with Clustered Spatial Temporal Transformer

Weakly Supervised Scene Text Detection using Deep Reinforcement Learning

An end-to-end machine learning web app to predict rugby scores (Pandas, SQLite, Keras, Flask, Docker)

[NeurIPS 2021] PyTorch Code for Accelerating Robotic Reinforcement Learning with Parameterized Action Primitives

Reading Group @mila-iqia on Computational Optimal Transport for Machine Learning Applications

Hyperbolic Image Segmentation, CVPR 2022