In conjunction with this workshop, we will hold three challenges this year.
This track targets on learning to perform object semantic segmentation using image-level annotations as supervision [1, 2, 3]. The dataset is built upon the image detection track of ImageNet Large Scale Visual Recognition Competition (ILSVRC) , which totally includes 456, 567 training images from 200 categories. We provide pixel-level annotations of 15K images (validation/testing: 5, 000/10, 000) for evaluation.
This track targets on learning to perform scene parsing using points-based annotation as supervision. The dataset is built upon the ADE20K dataset . There are 20,210 images in the training set, 2,000 images in the validation set, and 3,000 images in the testing set. We provide the additional point-based annotations on the training set .
This track targets on making the classification networks be equipped with the ability of object localization [7, 8, 9]. The dataset is built upon the image classification/localization track of ImageNet Large Scale Visual Recognition Competition (ILSVRC), which totally includes 1.2 million training images from 1000 categories. We provide pixel-level annotations of 44, 271 images (validation/testing: 23, 151/21, 120) for evaluation.
This year, we have two strict rules for all competitors.
This year, Baidu Inc will provide cash awards to the winners of each track. Participants are encouraged to submit the inference code based on the deep learning platform PaddlePaddle , especially on the semantic segmentation toolkit PaddleSeg. Winners will receive a cash award of USD 2000 if they use the PaddlePaddle platform or a USD 500 cash award if other deep learning platforms are used.
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