Package: autoannotator
Auto AnnotatorAn extendable tool for automatic annotation of image data by a combination of deep neural networks.The primary objective of this annotator is to prioritize the accuracy and quality of predictions over speed. Theautoannotatorhas been specifically designed to surpass the precision offered by most publicly available tools. It leverages ensembles of deep neural models to ensure the utmost quality in its predictions. It is important to note that neural networks trained on clean datasets tend to yield superior results compared to those trained on larger but noisier datasets.Supported tasksHuman Face Detection and RecognitionHuman Face Detection and RecognitionFace and landmarks detectionFace alignment via keypointsFace descriptor extractionHuman Body DetectionUniHCPIterDETR (Progressive DETR)RTDETR, ResNet-101InternImage-XLOtherDBSCAN clustering📊 BenchmarksFace RecognitionSpeedTaskHardwareTime, sFace detection + landmarks + extractionXeon e5 2678 v3~1Human DetectionQualityAP@50 when RT-DETR ResNet-18 was trained on CUHK-SYSUTest setOriginal MarkupMarkup via DDQ-DETRMarkup via EnsemleCrowdHuman52.3076.9777.31WiderPerson54.6660.8963.71🏗 InstallationPIP packagepipinstallautoannotator🚲 Getting startedFace recognition exampleCheck out our demo face recognition pipeline at:examples/face_recognition_example.py[Optional] Run frontend and backendgitclonehttps://github.com/CatsWhoTrain/autoannotator_clientcdautoannotator_client
dockercomposeupThe webinterface could be found locally at:http://localhost:8080/Human full-body detectionDetailed description is given in the separatedocumentHuman detection exampleCheck out our demo face recognition pipeline at:examples/human_detection_example.pyFAQDo companies and engineers actually need this tool?We have asked engineers in the field of video analytics whether they are interested in such a library. Their responses were:IREX: would use this library and contribute to it.NapoleonIT: would use this library and contribute to it.Linza Metrics: would use this library.What are the reasons for choosing this data labeling tool over the alternative of employing human annotators?Human accuracy is not so goodLong time ago Andrej Karpathyobservedthat his accuracy was only 94% when he tried to label just 400 images of the CIFAR-10 dataset while SOTAEfficient adaptive ensembling for image classification(August 29, 2023) achieves >99.6% accuracy.
When expert labelers had to choose from ~100 labels while annotating ImageNet,their error rate increased to 13-15%.Andrej's error rate was determined to be 5.1%, and he initially invested approximately one minute in labeling a single image. Conversely, utilizingFlorenceor never models for the same task can deliver a top-5 error rate of less than 1%.Industry case: human face classification.A certain undisclosed company, bound by a non-disclosure agreement (NDA), has utilized a technique wherein face images captured under challenging environmental conditions are pre-processed. This procedure involves the application of both a facial recognition network and DBSCAN algorithm to divide the images into distinct individuals. Subsequently, human annotators undertook a validation process to verify the accuracy of the pre-processed data. The work conducted by the annotators was inspected by their team leader. Ultimately, it was determined by an ML engineer that 1.4% of the clustered face images were mislabeled.🏰 LegacyCurrent repository takes ideas and some code form the following projects:faces_detanator✒ LegalAll individuals depicted in the "assets" images have provided explicit consent to utilize their photos for the purpose of showcasing the current library's work. Kindly refrain from utilizing these images within your individual projects.