ODIN: Pluggable Meta-annotations and Metrics for the Diagnosis of Classification and Localization

Torres, Rocio Nahime; Milani, Federico; Fraternali, Piero

doi:10.1007/978-3-030-95467-3_28

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…False Positive (FP) examples can be analyzed by charting the error type distribution. For this evaluation, the ODIN framework [ 91 ] was used. Figure 6 shows the FP categorization for two ArtDL 2.0 classes (Anthony of Padua and John the Baptist) and two IconArt classes (Child Jesus and Virgin Mary).…”

Section: Discussionmentioning

confidence: 99%

Proposals Generation for Weakly Supervised Object Detection in Artwork Images

2022

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Object Detection requires many precise annotations, which are available for natural images but not for many non-natural data sets such as artworks data sets. A solution is using Weakly Supervised Object Detection (WSOD) techniques that learn accurate object localization from image-level labels. Studies have demonstrated that state-of-the-art end-to-end architectures may not be suitable for domains in which images or classes sensibly differ from those used to pre-train networks. This paper presents a novel two-stage Weakly Supervised Object Detection approach for obtaining accurate bounding boxes on non-natural data sets. The proposed method exploits existing classification knowledge to generate pseudo-ground truth bounding boxes from Class Activation Maps (CAMs). The automatically generated annotations are used to train a robust Faster R-CNN object detector. Quantitative and qualitative analysis shows that bounding boxes generated from CAMs can compensate for the lack of manually annotated ground truth (GT) and that an object detector, trained with such pseudo-GT, surpasses end-to-end WSOD state-of-the-art methods on ArtDL 2.0 (≈41.5% mAP) and IconArt (≈17% mAP), two artworks data sets. The proposed solution is a step towards the computer-aided study of non-natural images and opens the way to more advanced tasks, e.g., automatic artwork image captioning for digital archive applications.

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Section: Discussionmentioning

confidence: 99%

Proposals Generation for Weakly Supervised Object Detection in Artwork Images

2022

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“…Usage with the oDIN diagnostic tool. ODIN 21,32 is an open source diagnosis framework for generic ML classification tasks and for CV object detection and instance segmentation tasks. ODIN lets developers add custom annotations to their data sets, compute performance metrics split by annotation values, and visualize diagnosis reports.…”

Section: Usage Notesmentioning

confidence: 99%

AerialWaste dataset for landfill discovery in aerial and satellite images

Torres

Fraternali

2023

Sci Data

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Illegal landfills are sites where garbage is dumped violating waste management laws. Aerial images enable the use of photo interpretation for territory scanning and landfill detection but this practice is hindered by the manual nature of this task which also requires expert knowledge. Deep Learning methods can help capture the analysts’ expertise and build automated landfill discovery tools. However, this goal requires public high-quality datasets for model training and testing. At present no such datasets exist and this gap penalizes the research toward scalable and accurate landfill discovery methods. We present a dataset for landfill detection featuring airborne, WorldView-3, and GoogleEarth images annotated by professional photo interpreters. It comprises 3,478 positive and 6,956 negative examples. Most positive instances are characterized by metadata: the type of waste, its storage mode, the type of the site, and the evidence and severity of the illicit. The dataset has been technically validated by building an accurate landfill detector and is accompanied by a visualization and annotation tool.

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“…This paper introduces ODIN TS, the extension for anomaly detection and predictive maintenance of the ODIN machine learning diagnosis tool. ODIN [12] is an open-source, Python-based, black-box framework for error diagnosis initially conceived for generic classification and computer vision tasks. Contrary to explainability techniques that aim at "opening" the box by exploring the internals of the models (e.g., CNNs), black-box approaches study only the results of the model with regard to the input and its characteristics.…”

Section: Introductionmentioning

confidence: 99%