100+ Open Audio and Video Datasets | Twine Blog The Youtube 8M is a huge dataset that has 6.1 million YouTube video IDs, 350,000 hours of video, 2.6 billion audio/visual features, 3862 classes, and an average of 3 labels for each video. Cityscapes Dataset The Mapillary Vistas Dataset Cityscapes is a large-scale database which focuses on semantic understanding of urban street scenes. The performance is measured in terms of pixel intersection-over-union averaged across the 21 classes (mIOU). Cityscapes Dataset. Note: * Some images from the train and validation sets don't have annotations. To facilitate common computer vision tasks, such as object detection and tracking, we annotate 23 object classes with accurate 3D bounding boxes at 2Hz over the entire dataset. Details on annotated classes and examples of our annotations are available at this webpage. Annotation is performed in a dense and fine-grained style by using polygons for delineating individual objects. This large-scale dataset contains a diverse set of stereo video sequences recorded in street scenes from 50 different cities, with high quality pixel-level annotations of 5 000 frames in addition to a larger set of 20 000 weakly ⦠The Cityscapes Dataset is intended for This large-scale dataset contains a diverse set of stereo video sequences recorded in street scenes from 50 different cities, with high quality pixel-level annotations of 5 000 frames in addition to a larger set of 20 000 weakly ⦠Semantic Segmentation Additionally we annotate object-level attributes such as visibility, activity and pose. This repository contains scripts for inspection, preparation, and evaluation of the Cityscapes dataset. It is open-source and contains high-quality pixel-level annotations of video sequences taken in 50 different city streets. It is widely used for video classification projects. Annotation is performed in a dense and fine-grained style by using polygons for delineating individual objects. dict â has a key âsegmâ, whose value is a dict of âAPâ and âAP50â.. class detectron2.evaluation.CityscapesSemSegEvaluator (dataset_name) [source] ¶. It is made of 30 classes, 50 cities and 5K annotated images. Additionally we annotate object-level attributes such as visibility, activity and pose. We recorded several suburbs of Karlsruhe, Germany, corresponding to over 320k images and 100k laser scans in a driving distance of 73.7km. Cityscapes is a large-scale dataset that contains a diverse set of stereo video sequences recorded in street scenes from 50 different cities, with high quality pixel-level annotations of 5,000 frames in addition to a larger set of 20,000 weakly annotated frames. We also provide careful dataset analysis as well as baselines for li-dar and image based detection and tracking. The GTA5 dataset contains 24966 synthetic images with pixel level semantic annotation. There are 19 semantic classes which are compatible with the ones of Cityscapes dataset. You can load it in TensorFlow just like you have seen above. Cityscapes is a large-scale database which focuses on semantic understanding of urban street scenes. In March 2019, we released the full nuScenes dataset with all 1,000 scenes. HMDB51 ¶ class torchvision.datasets.HMDB51 (root, annotation_path, frames_per_clip, step_between_clips=1, frame_rate=None, fold=1, train=True, transform=None, _precomputed_metadata=None, num_workers=1, _video_width=0, _video_height=0, _video_min_dimension=0, _audio_samples=0) [source] ¶. We augment the dataset by the extra annotations provided by [76], resulting in 10582 (trainaug) training images. Cityscapes is a large-scale database which focuses on semantic understanding of urban street scenes. At Twine, we specialize in helping AI companies create high-quality custom audio and video AI datasets.. During conversations with clients, we often get asked if there are any off-the-shelf audio and video datasets we would recommend, for testing and for them to use as a point of comparison with custom approaches. It features semantic, instance-wise, and dense pixel annotations for 30 classes grouped into 8 categories. Bases: detectron2.evaluation.cityscapes_evaluation.CityscapesEvaluator Evaluate semantic ⦠There are 50K training images and 10K test images. The Cityscapes Dataset is intended for Cityscapes 3D Benchmark Online October 17, 2020; Cityscapes 3D Dataset Released August 30, 2020; Coming Soon: Cityscapes 3D June 16, 2020; Robust Vision Challenge 2020 June 4, 2020; Panoptic Segmentation May 12, 2019 The provided ground truth includes instance segmentation, 2D bounding boxes, 3D bounding boxes and depth information! It features semantic, instance-wise, and dense pixel annotations for 30 classes grouped into 8 categories. * Coco 2014 and 2017 uses the same images, but different train/val/test splits * The test split don't have any annotations (only images). There are 19 semantic classes which are compatible with the ones of Cityscapes dataset. It is widely used for video classification projects. This is a dataset from the PASCAL Visual Object Classes Challenge. It is made of 30 classes, 50 cities and 5K annotated images. Data Link: Cityscapes dataset Cityscapes: Cityscapes contains high-quality pixel-level annotations of 5,000 frames in addition to a larger set of 20,000 poorly annotated frames. The dataset consists of around 5000 fine annotated images and 20000 coarse annotated ones. dict â has a key âsegmâ, whose value is a dict of âAPâ and âAP50â.. class detectron2.evaluation.CityscapesSemSegEvaluator (dataset_name) [source] ¶. ... Cityscapes Dataset The main focus of this dataset is semantic understanding of semantic scenes. process (inputs, outputs) [source] ¶ evaluate [source] ¶ Returns. This is an undesirable behavior and introduces confusion because if the classes are not set, the dataset only filter the empty GT images when filter_empty_gt=True and test_mode=False. COCO is a large-scale object detection, segmentation, and captioning dataset. At Twine, we specialize in helping AI companies create high-quality custom audio and video AI datasets.. During conversations with clients, we often get asked if there are any off-the-shelf audio and video datasets we would recommend, for testing and for them to use as a point of comparison with custom approaches. CityScapes is a large-scale dataset focused on the semantic understanding of urban street scenes in 50 German cities. 23 classes and 8 attributes. 23 classes and 8 attributes. The cityscapes dataset is a dataset for Computer Vision projects. Cityscapes is a large-scale dataset that contains a diverse set of stereo video sequences recorded in street scenes from 50 different cities, with high quality pixel-level annotations of 5,000 frames in addition to a larger set of 20,000 weakly annotated frames. The Cityscapes Dataset. Parameters. Before MMDetection v2.5.0, the dataset will filter out the empty GT images automatically if the classes are set and there is no way to disable that through config. We augment the dataset by the extra annotations provided by [76], resulting in 10582 (trainaug) training images. Config description: The Stanford Question Answering Dataset is a question-answering dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). We recorded several suburbs of Karlsruhe, Germany, corresponding to over 320k images and 100k laser scans in a driving distance of 73.7km. The Mapillary Vistas Dataset is a novel, large-scale street-level image dataset containing 25,000 high-resolution images annotated into 66 object categories with additional, instance-specific labels for 37 classes. The original dataset contains 1464 (train), 1449 (val), and 1456 (test) pixel-level annotated images. The provided ground truth includes instance segmentation, 2D bounding boxes, 3D bounding boxes and depth information! You can load it in TensorFlow just like you have seen above. The Mapillary Vistas Dataset is a novel, large-scale street-level image dataset containing 25,000 high-resolution images annotated into 66 object categories with additional, instance-specific labels for 37 classes. We also provide careful dataset analysis as well as baselines for li-dar and image based detection and tracking. COCO is a large-scale object detection, segmentation, and captioning dataset. It is open-source and contains high-quality pixel-level annotations of video sequences taken in 50 different city streets. Data Link: Cityscapes dataset HMDB51 ¶ class torchvision.datasets.HMDB51 (root, annotation_path, frames_per_clip, step_between_clips=1, frame_rate=None, fold=1, train=True, transform=None, _precomputed_metadata=None, num_workers=1, _video_width=0, _video_height=0, _video_min_dimension=0, _audio_samples=0) [source] ¶. 1. Cityscapes 3D Benchmark Online October 17, 2020; Cityscapes 3D Dataset Released August 30, 2020; Coming Soon: Cityscapes 3D June 16, 2020; Robust Vision Challenge 2020 June 4, 2020; Panoptic Segmentation May 12, 2019 root (string) â Root directory of dataset where directory caltech101 exists or will be saved to if download is set to True.. target_type (string or list, optional) â Type of target to use, category or annotation.Can also be a list to output a tuple with all specified target types. Before MMDetection v2.5.0, the dataset will filter out the empty GT images automatically if the classes are set and there is no way to disable that through config. root (string) â Root directory of dataset where directory caltech101 exists or will be saved to if download is set to True.. target_type (string or list, optional) â Type of target to use, category or annotation.Can also be a list to output a tuple with all specified target types. ; train (bool, optional) â If True, creates dataset from training set, otherwise creates from test set. 1. The classes considered in this dataset are void, sky, building, road, sidewalk, fence, vegetation, pole, car, traffic sign, pedestrian, bycicle, lanemarking, and traffic light. Data, devel-opment kit and more information are available online1. There are 50K training images and 10K test images. This large-scale dataset contains a diverse set of stereo video sequences recorded in street scenes from 50 different cities, with high quality pixel-level annotations of 5 000 frames in addition to a larger set of 20 000 weakly ⦠Bases: detectron2.evaluation.cityscapes_evaluation.CityscapesEvaluator Evaluate semantic ⦠The performance is measured in terms of pixel intersection-over-union averaged across the 21 classes (mIOU). We present a large-scale dataset that contains rich sensory information and full annotations. There are 19 semantic classes which are compatible with the ones of Cityscapes dataset. The Youtube 8M is a huge dataset that has 6.1 million YouTube video IDs, 350,000 hours of video, 2.6 billion audio/visual features, 3862 classes, and an average of 3 labels for each video. The cityscapes dataset is a dataset for Computer Vision projects. Parameters. We augment the dataset by the extra annotations provided by [76], resulting in 10582 (trainaug) training images. We present a large-scale dataset that contains rich sensory information and full annotations. The Cityscapes Dataset. The original dataset contains 1464 (train), 1449 (val), and 1456 (test) pixel-level annotated images. CIFAR-10: The CIFAR-10 dataset consists of 60000 32×32 colour images in 10 classes, with 6000 images per class. Citation:; @inproceedings{socher2013recursive, title={Recursive deep models for semantic compositionality over a sentiment treebank}, author={Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D and Ng, Andrew and Potts, Christopher}, booktitle={Proceedings of the 2013 conference on empirical methods in natural ⦠We deï¬ne novel 3D detection and tracking metrics. It contains 20 different classes and 24640 annotated objects. The Cityscapes Dataset is intended for HMDB51 dataset.. HMDB51 is an ⦠This is an undesirable behavior and introduces confusion because if the classes are not set, the dataset only filter the empty GT images when filter_empty_gt=True and test_mode=False. Cityscapes: Cityscapes contains high-quality pixel-level annotations of 5,000 frames in addition to a larger set of 20,000 poorly annotated frames. The Youtube 8M is a huge dataset that has 6.1 million YouTube video IDs, 350,000 hours of video, 2.6 billion audio/visual features, 3862 classes, and an average of 3 labels for each video. Data, devel-opment kit and more information are available online1. Cityscapes Dataset. Cityscapes is a large-scale dataset that contains a diverse set of stereo video sequences recorded in street scenes from 50 different cities, with high quality pixel-level annotations of 5,000 frames in addition to a larger set of 20,000 weakly annotated frames. HMDB51 dataset.. HMDB51 is an ⦠It contains 20 different classes and 24640 annotated objects. The images have been rendered using the open-world video game Grand Theft Auto 5 and are all from the car perspective in the streets of American-style virtual cities. Note: * Some images from the train and validation sets don't have annotations. It is widely used for video classification projects. ... Cityscapes Dataset The main focus of this dataset is semantic understanding of semantic scenes. We deï¬ne novel 3D detection and tracking metrics. It features semantic, instance-wise, and dense pixel annotations for 30 classes grouped into 8 categories. We deï¬ne novel 3D detection and tracking metrics. Config description: The Stanford Question Answering Dataset is a question-answering dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). It provides semantic, instance-wise, and dense pixel annotations for 30 classes grouped into 8 categories (flat surfaces, humans, vehicles, constructions, objects, nature, sky, and void). Parameters. Cityscapes 3D Benchmark Online October 17, 2020; Cityscapes 3D Dataset Released August 30, 2020; Coming Soon: Cityscapes 3D June 16, 2020; Robust Vision Challenge 2020 June 4, 2020; Panoptic Segmentation May 12, 2019 ... Cityscapes Dataset The main focus of this dataset is semantic understanding of semantic scenes. Annotation is performed in a dense and fine-grained style by using polygons for delineating individual objects. The classes considered in this dataset are void, sky, building, road, sidewalk, fence, vegetation, pole, car, traffic sign, pedestrian, bycicle, lanemarking, and traffic light. The images have been rendered using the open-world video game Grand Theft Auto 5 and are all from the car perspective in the streets of American-style virtual cities. The cityscapes dataset is a dataset for Computer Vision projects. You can load it in TensorFlow just like you have seen above. We recorded several suburbs of Karlsruhe, Germany, corresponding to over 320k images and 100k laser scans in a driving distance of 73.7km. The dataset is thus an order of magnitude larger than similar previous attempts. Data, devel-opment kit and more information are available online1. It has 7x as many annotations and 100x as many images as the pioneering KITTI dataset. The dataset is useful in training deep neural networks to understand the urban scene. In March 2019, we released the full nuScenes dataset with all 1,000 scenes. ; transform (callable, optional) â A function/transform that takes in an PIL image and returns a transformed version. The dataset consists of around 5000 fine annotated images and 20000 coarse annotated ones. CIFAR-10: The CIFAR-10 dataset consists of 60000 32×32 colour images in 10 classes, with 6000 images per class. CityScapes is a large-scale dataset focused on the semantic understanding of urban street scenes in 50 German cities. The images have been rendered using the open-world video game Grand Theft Auto 5 and are all from the car perspective in the streets of American-style virtual cities. At Twine, we specialize in helping AI companies create high-quality custom audio and video AI datasets.. During conversations with clients, we often get asked if there are any off-the-shelf audio and video datasets we would recommend, for testing and for them to use as a point of comparison with custom approaches. It provides semantic, instance-wise, and dense pixel annotations for 30 classes grouped into 8 categories (flat surfaces, humans, vehicles, constructions, objects, nature, sky, and void). Bases: detectron2.evaluation.cityscapes_evaluation.CityscapesEvaluator Evaluate semantic ⦠* Coco 2014 and 2017 uses the same images, but different train/val/test splits * The test split don't have any annotations (only images). Cityscapes: Cityscapes contains high-quality pixel-level annotations of 5,000 frames in addition to a larger set of 20,000 poorly annotated frames. The classes considered in this dataset are void, sky, building, road, sidewalk, fence, vegetation, pole, car, traffic sign, pedestrian, bycicle, lanemarking, and traffic light. The dataset consists of around 5000 fine annotated images and 20000 coarse annotated ones. CityScapes is a large-scale dataset focused on the semantic understanding of urban street scenes in 50 German cities. The GTA5 dataset contains 24966 synthetic images with pixel level semantic annotation. It is open-source and contains high-quality pixel-level annotations of video sequences taken in 50 different city streets. 23 classes and 8 attributes. dict â has a key âsegmâ, whose value is a dict of âAPâ and âAP50â.. class detectron2.evaluation.CityscapesSemSegEvaluator (dataset_name) [source] ¶. COCO is a large-scale object detection, segmentation, and captioning dataset. CIFAR-10: The CIFAR-10 dataset consists of 60000 32×32 colour images in 10 classes, with 6000 images per class. It is made of 30 classes, 50 cities and 5K annotated images. 1. To facilitate common computer vision tasks, such as object detection and tracking, we annotate 23 object classes with accurate 3D bounding boxes at 2Hz over the entire dataset. It contains 20 different classes and 24640 annotated objects. Data Link: Cityscapes dataset The dataset is useful in training deep neural networks to understand the urban scene. Details on annotated classes and examples of our annotations are available at this webpage. The performance is measured in terms of pixel intersection-over-union averaged across the 21 classes (mIOU). The original dataset contains 1464 (train), 1449 (val), and 1456 (test) pixel-level annotated images. Parameters: root (string) â Root directory of dataset where directory cifar-10-batches-py exists or will be saved to if download is set to True. This repository contains scripts for inspection, preparation, and evaluation of the Cityscapes dataset. We also provide careful dataset analysis as well as baselines for li-dar and image based detection and tracking. Additionally we annotate object-level attributes such as visibility, activity and pose. This repository contains scripts for inspection, preparation, and evaluation of the Cityscapes dataset. The provided ground truth includes instance segmentation, 2D bounding boxes, 3D bounding boxes and depth information! There are 50K training images and 10K test images. Details on annotated classes and examples of our annotations are available at this webpage. The dataset is useful in training deep neural networks to understand the urban scene. * Coco 2014 and 2017 uses the same images, but different train/val/test splits * The test split don't have any annotations (only images). It has 7x as many annotations and 100x as many images as the pioneering KITTI dataset. process (inputs, outputs) [source] ¶ evaluate [source] ¶ Returns. The dataset is thus an order of magnitude larger than similar previous attempts. To facilitate common computer vision tasks, such as object detection and tracking, we annotate 23 object classes with accurate 3D bounding boxes at 2Hz over the entire dataset. In March 2019, we released the full nuScenes dataset with all 1,000 scenes. We present a large-scale dataset that contains rich sensory information and full annotations. It has 7x as many annotations and 100x as many images as the pioneering KITTI dataset. The GTA5 dataset contains 24966 synthetic images with pixel level semantic annotation. It provides semantic, instance-wise, and dense pixel annotations for 30 classes grouped into 8 categories (flat surfaces, humans, vehicles, constructions, objects, nature, sky, and void). process (inputs, outputs) [source] ¶ evaluate [source] ¶ Returns. Cityscapes Dataset. This is an undesirable behavior and introduces confusion because if the classes are not set, the dataset only filter the empty GT images when filter_empty_gt=True and test_mode=False. Note: * Some images from the train and validation sets don't have annotations. Before MMDetection v2.5.0, the dataset will filter out the empty GT images automatically if the classes are set and there is no way to disable that through config. The Cityscapes Dataset. This is a dataset from the PASCAL Visual Object Classes Challenge. 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