How to efficiently annotate a million-scale Internet image collection?
Overview of this page
We start from the public YFCC100M collection that contains Flickr images uploaded between 2004 and 2014. We downloaded a 8M subset to build CLEAR-10, and a 40M subset to build CLEAR-100.
We use the upload time to recreate the temporal stream and split the 8M/40M into 11 buckets of images, each spanning on average 1 year. The 0th bucket is reserved for unsupervised pre-training (e.g., MoCo), and we curate a small but high-quality labeled set (with CLEAR-10 and CLEAR-10 ontology defined here) for each of the 1st to 10th buckets.
To avoid excessive human annotation cost on web-scale data, we use the visio-linguistic dataset curation approach proposed in our NeurIPS'21 paper. The key idea is to leverage OpenAI's recent CLIP model and prompt engineering techniques for efficient image retrieval. The top-scoring images retrieved by CLIP are later verified by human annotators to ensure 99% precision (crowdsourced MTurk workers for CLEAR-10 and commericial labelling service for CLEAR-100).
The entire pipeline can be summarized:
Assets for Future Research
In addition to the high-quality labeled subset, we also release a wealth of assets per time bucket for future research on continual learning, including:
Abundant unlabeled data: For research on unsupervised continual learning. This includes ~0.8M unlabeled images per bucket for CLEAR-10, and ~3.6M unlabeled images per bucket for CLEAR-100.
Metadata: For research on continual multi-modal learning. This includes all the YFCC100M released metadata such as upload and captured timestamps, captured location, social media hashtags, user description, image title, and etc.
Instruction sets for human annotation: For improving dataset transparency. CLEAR-10 instruction set on MTurk platform can be found in the supplemental of NeurIPS'21 paper. CLEAR-100 instruction set can be found here (Chinese ver.).
Check out this repo if you want to make your own dataset out of YFCC100M with a visio-linguistic approach.