Welcome to the EPIC-Kitchens 2018 Object Detection challenge. EPIC-Kitchens is an unscripted egocentric action dataset collected from 32 different people from 4 cities across the world.
This challenge is part of the ECCV 2018 workshop.
Detect and classify bounding boxes of objects in video frames from seen and unseen kitchens.
Submissions are evaluated across 2 test sets:
We evaluate model performance on the mean average precision (mAP) metric from PASCAL VOC using IoU thresholds of 0.05, 0.5, 0.75 for many shot and few shot classes.
To submit your results to the leaderboard you must construct a submission zip file containing two files:
seen.json- Model inference on the seen kitchens test set (S1)
unseen.json- Model inference on the unseen kitchens test set (S2)
Both of these files follow the same format detailed below:
The JSON submission format is composed of a single JSON object containing detections for every image in the test set. The JSON file should consist of a list of of each detected bounding box with the corresponsing video and frame, category and score. Each entry is should have the form:
You should submit 300 detections per image. Note: box coordinates should be in the range [0,1] in terms of the image. We recommend rounding coordinates to 5 decimal places to reduce the size of the JSON files.
To upload your results to CodaLab you have to zip both files into a flat zip archive (they can’t be inside a folder within the archive).
You can create a flat archive using the command providing the JSON files are in your current directory.
$ zip -j my-submission.zip seen.json unseen.json
Start: June 30, 2018, midnight
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