• Given Images of Food, we are asked to provide Instance Segmentation over the images for the food items.
• The Training Data is provided in the COCO format, making it simpler to load with pre-available COCO data processors in popular libraries.
• The test set provided in the public dataset is similar to Validation set, but with no annotations.
• The test set after submission is much larger and contains private images upon which every submission is evaluated.
• Pariticipants have to submit their trained model along with trained weights. Immediately after the submission the AICrowd Grader picks up the submitted model and produces inference on the private test set using Cloud GPUs.
• This requires Users to structure their repositories and follow a provided paradigm for submission.
• The AICrowd AutoGrader picks up the Dockerfile provided with the repository, builds it and then mounts the tests folder in the container. Once inference is made, the final results are checked with the ground truth.

***For more submission related information, please check the AIcrowd Challenge page

This repository includes Notebooks and code relevant to this challenge.

Also see the official starter kit or a PyTorch implementation with mmdet.