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

Dynamic Capacity Networks using Tensorflow

Addition of pseudotorsion caclulation eta, theta, eta', and theta' to barnaba package

CarND-LaneLines-P1 - Lane Finding Project for Self-Driving Car ND

Exploring Classification Equilibrium in Long-Tailed Object Detection, ICCV2021

YOLOX-CondInst - Implement CondInst which is a instances segmentation method on YOLOX

Code for our ACL 2021 paper - ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer

Source code and notebooks to reproduce experiments and benchmarks on Bias Faces in the Wild (BFW).

Learning Tracking Representations via Dual-Branch Fully Transformer Networks

Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics

📖 Deep Attentional Guided Image Filtering

Redash reset for python

Pytorch implementation of Zero-DCE++

HashNeRF-pytorch - Pure PyTorch Implementation of NVIDIA paper on Instant Training of Neural Graphics primitives

Official PyTorch code for CVPR 2020 paper "Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision"

My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot

My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control

CS583: Deep Learning

An open-source, low-cost, image-based weed detection device for fallow scenarios.

A simple consistency training framework for semi-supervised image semantic segmentation

Pytorch implementation of the paper "Topic Modeling Revisited: A Document Graph-based Neural Network Perspective"