Note that you can use parentheses to highlight details, for example: BERT Large (12 layers), FoveaBox (ResNeXt-101), EfficientNet-B7 (NoisyStudent). What are the model naming conventions? Model name should be straightforward, as presented in the paper. ImageNet on Image Classification already exists with metrics Top 1 Accuracy and Top 5 Accuracy. You should check if a benchmark already exists to prevent duplication if it doesn’t exist you can create a new dataset.
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Then choose a task, dataset and metric name from the Papers With Code taxonomy. You can manually edit the incorrect or missing fields. How do I add a new result from a table? Click on a cell in a table on the left hand side where the result comes from. Help! Don’t worry! If you make mistakes we can revert them: everything is versioned! So just tell us on the Slack channel if you’ve accidentally deleted something (and so on) - it’s not a problem at all, so just go for it! I’m editing for the first time and scared of making mistakes. Where do referenced results come from? If we find referenced results in a table to other papers, we show a parsed reference box that editors can use to annotate to get these extra results from other papers. Where do suggested results come from? We have a machine learning model running in the background that makes suggestions on papers. Blue is a referenced result that originates from a different paper. What do the colors mean? Green means the result is approved and shown on the website.
A result consists of a metric value, model name, dataset name and task name. What are the colored boxes on the right hand side? These show results extracted from the paper and linked to tables on the left hand side. It shows extracted results on the right hand side that match the taxonomy on Papers With Code. What is this page? This page shows tables extracted from arXiv papers on the left-hand side. We advocate for providing conversion tools to common formats (e.g., COCO format for computer vision tasks) and always providing a set of evaluation metrics, instead of just one, to make results comparable across studies. We further discuss gaps and challenges in this domain. In addition, we provide the benchmark tasks and results from these papers or recent competitions. We present the statistics, document type, language, tasks, input visual aspects, and ground truth information for every dataset. We summarize each study by assigning it to one of three pre-defined tasks: document classification, layout structure, or content analysis. After a systematic selection process (according to PRISMA guidelines), we select 65 studies that are chosen based on different factors, such as the year of publication, number of methods implemented in the article, reliability of the chosen algorithms, dataset size, and journal outlet. This work fills this gap, presenting a meta-study on existing datasets. However, because of the very large variety of the actual data (e.g., scripts, tasks, dates, support systems, and amount of deterioration), the different formats for data and label representation, and the different evaluation processes and benchmarks, finding appropriate datasets is a difficult task. Finding appropriate datasets for historical document analysis is a crucial prerequisite to facilitate research using different machine learning algorithms. This paper presents a systematic literature review of image datasets for document image analysis, focusing on historical documents, such as handwritten manuscripts and early prints. A Survey of Historical Document Image Datasets
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