collection curated by joecohen https://academictorrents.com/collection/pascal-visual-object-classes-challenge Introduction The main goal of this challenge is to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). It is fundamentally a supervised learning learning problem in that a training set of labelled images is provided. The twenty object classes that have been selected are: Person: person Animal: bird, cat, cow, dog, horse, sheep Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor Data To download the training/validation data, see the development kit. The training data provided consists of a set of images; each image has an annotation file giving a bounding box and object class label for each object in one of the twenty classes present in the image. Note that multiple objects from multiple classes may be present in the same image. Annotation was performed according to a set of guidelines distributed to all annotators. A subset of images are also annotated with pixel-wise segmentation of each object present, to support the segmentation competition. Images for the action classification task are disjoint from those of the classification/detection/segmentation tasks. They have been partially annotated with people, bounding boxes, reference points and their actions. Annotation was performed according to a set of guidelines distributed to all annotators. Images for the person layout taster, where the test set is disjoint from the main tasks, have been additionally annotated with parts of the people (head/hands/feet). The data will be made available in two stages; in the first stage, a development kit will be released consisting of training and validation data, plus evaluation software (written in MATLAB). One purpose of the validation set is to demonstrate how the evaluation software works ahead of the competition submission. In the second stage, the test set will be made available for the actual competition. As in the VOC2008-2011 challenges, no ground truth for the test data will be released. The data has been split into 50% for training/validation and 50% for testing. The distributions of images and objects by class are approximately equal across the training/validation and test sets. Statistics of the database are online. https://academictorrents.com/download/f6ddac36ac7ae2ef79dc72a26a065b803c9c7230 Introduction The goal of this challenge is to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). It is fundamentally a supervised learning learning problem in that a training set of labelled images is provided. The twenty object classes that have been selected are: Person: person Animal: bird, cat, cow, dog, horse, sheep Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor Data To download the training/validation data, see the development kit. The training data provided consists of a set of images; each image has an annotation file giving a bounding box and object class label for each object in one of the twenty classes present in the image. Note that multiple objects from multiple classes may be present in the same image. Some example images can be viewed online. A subset of images are also annotated with pixel-wise segmentation of each object present, to support the segmentation competition. Some segmentation examples can be viewed online. Annotation was performed according to a set of guidelines distributed to all annotators. The data will be made available in two stages; in the first stage, a development kit will be released consisting of training and validation data, plus evaluation software (written in MATLAB). One purpose of the validation set is to demonstrate how the evaluation software works ahead of the competition submission. In the second stage, the test set will be made available for the actual competition. As in the VOC2008-2010 challenges, no ground truth for the test data will be released. The data has been split into 50% for training/validation and 50% for testing. The distributions of images and objects by class are approximately equal across the training/validation and test sets. In total there are 28,952 images. Further statistics are online. https://academictorrents.com/download/408e318ba27031a533c709b7d696e34637bcfc0e Introduction The goal of this challenge is to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). It is fundamentally a supervised learning learning problem in that a training set of labelled images is provided. The twenty object classes that have been selected are: Person: person Animal: bird, cat, cow, dog, horse, sheep Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor Data To download the training/validation data, see the development kit. The training data provided consists of a set of images; each image has an annotation file giving a bounding box and object class label for each object in one of the twenty classes present in the image. Note that multiple objects from multiple classes may be present in the same image. Some example images can be viewed online. A subset of images are also annotated with pixel-wise segmentation of each object present, to support the segmentation competition. Some segmentation examples can be viewed online. Annotation was performed according to a set of guidelines distributed to all annotators. The data will be made available in two stages; in the first stage, a development kit will be released consisting of training and validation data, plus evaluation software (written in MATLAB). One purpose of the validation set is to demonstrate how the evaluation software works ahead of the competition submission. In the second stage, the test set will be made available for the actual competition. As in the VOC2008/VOC2009 challenges, no ground truth for the test data will be released. The data has been split into 50% for training/validation and 50% for testing. The distributions of images and objects by class are approximately equal across the training/validation and test sets. In total there are 21,738 images. Further statistics are online. Best Practice The VOC challenge encourages two types of participation: (i) methods which are trained using only the provided "trainval" (training + validation) data; (ii) methods built or trained using any data except the provided test data, for example commercial systems. In both cases the test data must be used strictly for reporting of results alone - it must not be used in any way to train or tune systems, for example by runing multiple parameter choices and reporting the best results obtained. If using the training data we provide as part of the challenge development kit, all development, e.g. feature selection and parameter tuning, must use the "trainval" (training + validation) set alone. One way is to divide the set into training and validation sets (as suggested in the development kit). Other schemes e.g. n-fold cross-validation are equally valid. The tuned algorithms should then be run only once on the test data. In VOC2007 we made all annotations available (i.e. for training, validation and test data) but since then we have not made the test annotations available. Instead, results on the test data are submitted to an evaluation server. Since algorithms should only be run once on the test data we strongly discourage multiple submissions to the server (and indeed the number of submissions for the same algorithm is strictly controlled), as the evaluation server should not be used for parameter tuning. We encourage you to publish test results always on the latest release of the challenge, using the output of the evaluation server. If you wish to compare methods or design choices e.g. subsets of features, then there are two options: (i) use the entire VOC2007 data, where all annotations are available; (ii) report cross-validation results using the latest "trainval" set alone. https://academictorrents.com/download/96db21675f464480780637f1416477ac14a81107 Introduction The goal of this challenge is to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). It is fundamentally a supervised learning learning problem in that a training set of labelled images is provided. The twenty object classes that have been selected are: Person: person Animal: bird, cat, cow, dog, horse, sheep Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor Data To download the training/validation data, see the development kit. The training data provided consists of a set of images; each image has an annotation file giving a bounding box and object class label for each object in one of the twenty classes present in the image. Note that multiple objects from multiple classes may be present in the same image. Some example images can be viewed online. A subset of images are also annotated with pixel-wise segmentation of each object present, to support the segmentation competition. Some segmentation examples can be viewed online. Annotation was performed according to a set of guidelines distributed to all annotators. The data will be made available in two stages; in the first stage, a development kit will be released consisting of training and validation data, plus evaluation software (written in MATLAB). One purpose of the validation set is to demonstrate how the evaluation software works ahead of the competition submission. In the second stage, the test set will be made available for the actual competition. As in the VOC2008 challenge, no ground truth for the test data will be released. The data has been split into 50% for training/validation and 50% for testing. The distributions of images and objects by class are approximately equal across the training/validation and test sets. In total there are 14,743 images. Further statistics are online. https://academictorrents.com/download/e2209d95a13d364aad0811eacbf391a10c37d963 Data To download the training/validata data, see the development kit. In total there are 10,057 images [further statistics]. The training data provided consists of a set of images; each image has an annotation file giving a bounding box and object class label for each object in one of the twenty classes present in the image. Note that multiple objects from multiple classes may be present in the same image. Some example images can be viewed online. Annotation was performed according to a set of guidelines distributed to all annotators. The data will be made available in two stages; in the first stage, a development kit will be released consisting of training and validation data, plus evaluation software (written in MATLAB). One purpose of the validation set is to demonstrate how the evaluation software works ahead of the competition submission. In the second stage, the test set will be made available for the actual competition. As in the VOC2007 challenge, no ground truth for the test data will be released until after the challenge is complete. The data has been split into 50% for training/validation and 50% for testing. The distributions of images and objects by class are approximately equal across the training/validation and test sets. In total there are 10,057 images. Further statistics are online - statistics for the test data will be released after the challenge. Development Kit The development kit consists of the training/validation data, MATLAB code for reading the annotation data, support files, and example implementations for each competition. Download the training/validation data (550MB tar file) - includes patch of 14-Jul-2008 Download the development kit code and documentation (250KB tar file) Patch 14-Jul-08 There were errors in the 14-Apr-2008 release of the training/validation data as follows: image labels in x_train/x_trainval.txt (classification task) did not include the "don t care" (zero) label the test set for the main challenge (classification/detection) included images used for the layout challenge - these will be ignored in the evaluation some images contained only "difficult" objects - these will be ignored in the evaluation (classification/detection) The errors will not affect evaluation, but participants wanting to take advantage of the "don t care" label (without having to compute it themselves) should download the patch, which contains updated image lists, and can be untarred over the original development kit: Running on VOC2007 test data If at all possible, participants are requested to submit results for both the VOC2008 and VOC2007 test sets provided in the test data, to allow comparison of results across the years. In both cases, the VOC2008 training/validation data should be used for training i.e. Train on VOC2008 train+val, test on VOC2008 test. Train on VOC2008 train+val, test on VOC2007 test. The updated development kit provides a switch to select between test sets. Results are placed in two directories, results/VOC2007/ or results/VOC2008/ according to the test set. Publication Policy The main mechanism for dissemination of the results will be the challenge webpage. For VOC2008, the detailed output of each submitted method will be published online e.g. per-image confidence for the classification task, and bounding boxes for the detection task. The intention is to assist others in the community in carrying out detailed analysis and comparison with their own methods. The published results will not be anonymous - by submitting results, participants are agreeing to have their results shared online. Acknowledgements We gratefully acknowledge the following, who spent many long hours providing annotation for the VOC2008 database: Jan-Hendrik Becker, Patrick Buehler, Kian Ming Chai, Miha Drenik, Chris Engels, Jan Van Gemert, Hedi Harzallah, Nicolas Heess, Zdenek Kalal, Lubor Ladicky, Marcin Marszalek, Alastair Moore, Maria-Elena Nilsback, Paul Sturgess, David Tingdahl, Hirofumi Uemura, Martin Vogt. Support The preparation and running of this challenge is supported by the EU-funded PASCAL Network of Excellence on Pattern Analysis, Statistical Modelling and Computational Learning. https://academictorrents.com/download/577c99c831a03753c38764201123cbc5e9e3c03b Details of the contributor of each image can be found in the file "contrib.txt" included in the database. CategoriesViews of bicycles, buses, cats, cars, cows, dogs, horses, motorbikes, people, sheep in arbitrary pose. Number of images5,304 Number of annotated images5,304 https://academictorrents.com/download/db06b76152c0bf475af4093538e5a8d0e7971273 CategoriesViews of motorbikes, bicycles, people, and cars in arbitrary pose. Number of images1578 Number of annotated images1578 https://academictorrents.com/download/f758e9f976e3742b1349bf4b42e985b6ce1299ce ==Introduction The goal of this challenge is to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). It is fundamentally a supervised learning learning problem in that a training set of labelled images is provided. The twenty object classes that have been selected are: Person: person Animal: bird, cat, cow, dog, horse, sheep Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor There will be two main competitions, and two smaller scale "taster" competitions. https://academictorrents.com/download/c9db37df1eb2e549220dc19f70f60f7786d067d4 Free-fiewing fixations on a subset of 850 images from PASCAL VOC. Collected on 8 subjects, 3s viewing time, Eyelink II eye tracker. The performance of most algorithms suggest that PASCAL-S is less biased than most of the saliency datasets. 850 IMAGES FROM PASCAL 2010 1296 OBJECT INSTANCES 12 SUBJECTS Folders in archive: algmaps/ algmaps/pascal algmaps/pascal/mcg_gbvs algmaps/pascal/humanFix algmaps/pascal/gc algmaps/pascal/dva algmaps/pascal/ft algmaps/pascal/sig algmaps/pascal/aim algmaps/pascal/pcas algmaps/pascal/gbvs algmaps/pascal/sun algmaps/pascal/aws algmaps/pascal/sf algmaps/pascal/itti algmaps/bruce algmaps/bruce/dva algmaps/bruce/sig algmaps/bruce/aim algmaps/bruce/gbvs algmaps/bruce/sun algmaps/bruce/aws algmaps/bruce/itti algmaps/cerf algmaps/cerf/dva algmaps/cerf/sig algmaps/cerf/aim algmaps/cerf/gbvs algmaps/cerf/sun algmaps/cerf/aws algmaps/cerf/itti algmaps/imgsal algmaps/imgsal/humanFix algmaps/imgsal/gc algmaps/imgsal/cpmc_gbvs algmaps/imgsal/dva algmaps/imgsal/ft algmaps/imgsal/sig algmaps/imgsal/aim algmaps/imgsal/pcas algmaps/imgsal/gbvs algmaps/imgsal/sun algmaps/imgsal/aws algmaps/imgsal/sf algmaps/imgsal/itti algmaps/ft algmaps/ft/gc algmaps/ft/cpmc_gbvs algmaps/ft/dva algmaps/ft/ft algmaps/ft/sig algmaps/ft/aim algmaps/ft/pcas algmaps/ft/gbvs algmaps/ft/sun algmaps/ft/aws algmaps/ft/sf algmaps/ft/itti algmaps/judd algmaps/judd/dva algmaps/judd/sig algmaps/judd/aim algmaps/judd/gbvs algmaps/judd/sun algmaps/judd/aws algmaps/judd/itti https://academictorrents.com/download/6c49defd6f0e417c039637475cde638d1363037e This dataset is a set of additional annotations for PASCAL VOC 2010. It goes beyond the original PASCAL semantic segmentation task by providing annotations for the whole scene. The statistics section has a full list of 400+ labels. Every pixel has a unique class label. Instance information (i.e, different masks to separate different instances of the same class in the same image) are currently provided for the 20 PASCAL objects. Statistics Since the dataset is an annotation of PASCAL VOC 2010, it has the same statistics as those of the original dataset. Training and validation contains 10,103 images while testing contains 9,637 images. Usage Considerations The classes are not drawn from a fixed pool. Instead labelers were free to either select or type in what they believe to be the appropriate class and to determine what the appropriate object granularity is. We decided to merge/split some of the categories so the current number of categories is different from what we mentioned in the CVPR 2014 paper. When using this dataset it is important that you examine classes to ensure they match your intended use. For example, sand is often labeled independently despite also being considered ground. Those interested in ground may want to cluster sand and ground together along with other classes. Citation The Role of Context for Object Detection and Semantic Segmentation in the Wild Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, Alan Yuille IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 Acknowledgements We would like to acknowledge the support by Implementation of Technologies for Identification, Behavior, and Location of Human based on Sensor Network Fusion Program through the Korean Ministry of Trade, Industry and Energy (Grant Number: 10041629). We would also like to thank National Science Foundation for grant 1317376 (Visual Cortex on Silicon. NSF Expedition in Computing). We thank Viet Nguyen for coordinating and leading the efforts for cleaning up the annotations. https://academictorrents.com/download/eec6177ad62f4c47086e4cbec93ac4c08857ddbe This dataset is a set of additional annotations for PASCAL VOC 2010. It goes beyond the original PASCAL object detection task by providing segmentation masks for each body part of the object. For categories that do not have a consistent set of parts (e.g., boat), we provide the silhouette annotation. Statistics Since the dataset is an annotation of the PASCAL VOC 2010, it has the same statistics as those of the original dataset. Training and validation contains 10,103 images while testing contains 9,637 images. Usage Considerations We provide segmentation masks for detailed body parts. One can merge several parts to get appropriate object part granularity for different tasks. For instance, "eyes", "ears", "nose", etc. can be merged into a single "head" part. Citation Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts Xianjie Chen, Roozbeh Mottaghi, Xiaobai Liu, Sanja Fidler, Raquel Urtasun, Alan Yuille IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 Acknowledgements We thank Viet Nguyen for coordinating and leading the efforts for cleaning up the annotations. We would like to acknowledge the support by grants ARO 62250-CS and N00014-12-1-0883. https://academictorrents.com/download/f86670296bff85bcdffea6c4fc2e791446f9fb5e ##Introduction The main goal of this challenge is to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). It is fundamentally a supervised learning learning problem in that a training set of labelled images is provided. The twenty object classes that have been selected are: * Person: person * Animal: bird, cat, cow, dog, horse, sheep * Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train * Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor There are three main object recognition competitions: classification, detection, and segmentation, a competition on action classification, and a competition on large scale recognition run by ImageNet. In addition there is a "taster" competition on person layout. ##Classification/Detection Competitions Classification: For each of the twenty classes, predicting presence/absence of an example of that class in the test image. Detection: Predicting the bounding box and label of each object from the twenty target classes in the test image. 20 classes ![]() * aeroplane * bicycle * bird * boat * bottle * bus * car * cat * chair * cow * dining table * dog * horse * motorbike * person * potted plant * sheep * sofa * train * tv/monitor Participants may enter either (or both) of these competitions, and can choose to tackle any (or all) of the twenty object classes. The challenge allows for two approaches to each of the competitions: 1. Participants may use systems built or trained using any methods or data excluding the provided test sets. 2. Systems are to be built or trained using only the provided training/validation data. The intention in the first case is to establish just what level of success can currently be achieved on these problems and by what method; in the second case the intention is to establish which method is most successful given a specified training set. Segmentation Competition Segmentation: Generating pixel-wise segmentations giving the class of the object visible at each pixel, or "background" otherwise. ![]() ##Action Classification Competition Action Classification: Predicting the action(s) being performed by a person in a still image. ![]() * jumping * phoning * playinginstrument * reading * ridingbike * ridinghorse * running * takingphoto * usingcomputer * walking In 2012 there are two variations of this competition, depending on how the person whose actions are to be classified is identified in a test image: (i) by a tight bounding box around the person; (ii) by only a single point located somewhere on the body. The latter competition aims to investigate the performance of methods given only approximate localization of a person, as might be the output from a generic person detector. ##ImageNet Large Scale Visual Recognition Competition The goal of this competition is to estimate the content of photographs for the purpose of retrieval and automatic annotation using a subset of the large hand-labeled ImageNet dataset (10,000,000 labeled images depicting 10,000+ object categories) as training. Test images will be presented with no initial annotation - no segmentation or labels - and algorithms will have to produce labelings specifying what objects are present in the images. In this initial version of the challenge, the goal is only to identify the main objects present in images, not to specify the location of objects. Further details can be found at the ImageNet website. ##Person Layout Taster Competition Person Layout: Predicting the bounding box and label of each part of a person (head, hands, feet). ![]() ##Data To download the training/validation data, see the development kit. The training data provided consists of a set of images; each image has an annotation file giving a bounding box and object class label for each object in one of the twenty classes present in the image. Note that multiple objects from multiple classes may be present in the same image. Annotation was performed according to a set of guidelines distributed to all annotators. A subset of images are also annotated with pixel-wise segmentation of each object present, to support the segmentation competition. Images for the action classification task are disjoint from those of the classification/detection/segmentation tasks. They have been partially annotated with people, bounding boxes, reference points and their actions. Annotation was performed according to a set of guidelines distributed to all annotators. Images for the person layout taster, where the test set is disjoint from the main tasks, have been additionally annotated with parts of the people (head/hands/feet). The data will be made available in two stages; in the first stage, a development kit will be released consisting of training and validation data, plus evaluation software (written in MATLAB). One purpose of the validation set is to demonstrate how the evaluation software works ahead of the competition submission. In the second stage, the test set will be made available for the actual competition. As in the VOC2008-2011 challenges, no ground truth for the test data will be released. The data has been split into 50% for training/validation and 50% for testing. The distributions of images and objects by class are approximately equal across the training/validation and test sets. Statistics of the database are online. https://academictorrents.com/download/df0aad374e63b3214ef9e92e178580ce27570e59