TIDE: A General Toolbox for Identifying Object Detection Errors

By Daniel Bolya, Sean Foley, James Hays, Judy Hoffman. 2020

283 citations recorded by the source corpus.

We introduce TIDE, a framework and associated toolbox for analyzing the sources of error in object detection and instance segmentation algorithms. Importantly, our framework is applicable across datasets and can be applied directly to output prediction files without required knowledge of the underlying prediction system. Thus, our framework can be used as a drop-in replacement for the standard mAP computation while providing a comprehensive analysis of each model's strengths and weaknesses. We segment errors into six types and, crucially, are the first to introduce a technique for measuring the contribution of each error in a way that isolates its effect on overall performance. We show that such a representation is critical for drawing accurate, comprehensive conclusions through in-depth analysis across 4 datasets and 7 recognition models. Available at this https URL

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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Citations in the corpus

Benchmarking Detection Transfer Learning with Vision Transformers (2021, methodology): “(See [3] for more details and discussion.)”
Occluded Video Instance Segmentation: A Benchmark (2021, background): “For segmentation error rates, following [4], we consider masks whose IoU with its matched ground-truth is greater Co w Ca t”
Vision Transformer Adapter for Dense Predictions (2022, methodology): “TIDE Error Type Analysis.”
REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets (2020, methodology): “TIDE (Bolya et al., 2020) which also surfaces object detection errors, as well as tools in the video domain (Alwassel et al.”
FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery (2021, methodology): “Furthermore, the FAIR1M dataset will be released as the ISPRS benchmark dataset on the benchmark website 1 .”
You Only Look One-level Feature (2021, methodology): “We adopt the recent proposed tool TIDE [2] to compare YOLOF with DETR [4].”
Boundary IoU: Improving Object-Centric Image Segmentation Evaluation (2021, methodology): “Incorporation of the measure in the performance analysis tools like TIDE [2] can provide better insights into specific error types of instance segmentation models.”
Delving into Localization Errors for Monocular 3D Object Detection (2021, methodology): “we conduct intensive diagnostic experiments for this task, inspired by the error identifying methods [20, 43, 17, 1] commonly used in the 2D detection scope.”