Scene Parsing

Contains more than 5,000 images of 10,000 liquid containers in context labelled with volume, amount of content, bounding box annotation, and corresponding similar 3D CAD models. Source: See the Glass Half Full: Reasoning about Liquid Containers, their Volume and Content

DeepLocCross is a localization dataset that contains RGB-D stereo images captured at 1280 x 720 pixels at a rate of 20 Hz. The ground-truth pose labels are generated using a LiDAR-based SLAM system. In addition to the 6-DoF localization poses of the robot, the dataset additionally contains tracked detections of the observable dynamic objects. Each tracked object is identified using a unique track ID, spatial coordinates, velocity and orientation angle. Furthermore, as the dataset contains multiple pedestrian crossings, labels at each intersection indicating its safety for crossing are provided. This dataset consists of seven training sequences with a total of 2264 images, and three testing sequences with a total of 930 images. The dynamic nature of the surrounding environment at which the dataset was captured renders the tasks of localization and visual odometry estimation extremely challenging due to the varying weather conditions, presence of shadows and motion blur caused by the movement of the robot platform. Furthermore, the presence of multiple dynamic objects often results in partial and full occlusions to the informative regions of the image. Moreover, the presence of repeated structures render the pose estimation task more challenging. Overall this dataset covers a wide range of perception related tasks such as loop closure detection, semantic segmentation, visual odometry estimation, global localization, scene flow estimation and behavior prediction. Source: http://deeploc.cs.uni-freiburg.de/ Image Source: http://deeploc.cs.uni-freiburg.de/

Contrary to prior scene graph datasets, Haystack contains explicit negative annotations, i.e. annotations that a given relation does not have a certain predicate class. Negative annotations are helpful especially in the field of scene graph generation and open up a whole new set of possibilities to improve current scene graph generation models. Haystack is 100% compatible with existing panoptic scene graph datasets and can easily be integrated with existing evaluation pipelines.

HOWS-CL-25 (Household Objects Within Simulation dataset for Continual Learning) is a synthetic dataset especially designed for object classification on mobile robots operating in a changing environment (like a household), where it is important to learn new, never seen objects on the fly. This dataset can also be used for other learning use-cases, like instance segmentation or depth estimation. Or where household objects or continual learning are of interest. Our dataset contains 150,795 unique synthetic images using 25 different household categories with 925 3D models in total. For each of those categories, we generated about 6000 RGB images. In addition, we also provide a corresponding depth, segmentation, and normal image. The dataset was created with BlenderProc [Denninger et al. (2019)], a procedural pipeline to generate images for deep learning. This tool created a virtual room with randomly textured floors, walls, and a light source with randomly chosen light intensity and color. After that, a 3D model is placed in the resulting room. This object gets customized by randomly assigning materials, including different textures, to achieve a diverse dataset. Moreover, each object might be deformed with a random displacement texture. We use 774 3D models from the ShapeNet dataset [A. X. Chang et al. (2015)] and the other models from various internet sites. Please note that we had to manually fix and filter most of the models with Blender before using them in the pipeline! For continual learning (CL), we provide two different loading schemes: - Five sequences with five categories each - Twelve sequences with three categories in the first and two in the other sequences. In addition to the RGB, depth, segmentation, and normal images, we also provide the calculated features of the RGB images (by ResNet50) as used in our RECALL paper. In those two loading schemes, ten percent of the images are used for validation, where we ensure that an object instance is either in the training or the validation set, not in both. This avoids learning to recognize certain instances by heart. We recommend using those loading schemes to compare your approach with others. For further information and code examples, please have a look at our website: https://github.com/DLR-RM/RECALL.

The IC13 dataset contains 561 images: 420 for training and 141 for testing. It inherits data from the IC03 dataset and extends it with new images. Similar to IC03 dataset, the IC13 dataset contains 1,015 cropped text instance images after removing the words with non-alphanumeric characters.

The dfd_indoor dataset contains 110 images for training and 29 images for testing. The dfd_outdoor dataset contains 34 images for tests; no ground truth was given for this dataset, as the depth sensor only works on indoor scenes. Source: DFD dataset

MAI is a dataset for multi-scene recognition in single aerial images. It consists of 3,923 labelled large-scale images from Google Earth imagery that covers the United States, Germany, and France. The size of each image is 512 ×512, and spatial resolutions vary from 0.3 m/pixel to 0.6 m/pixel. After capturing aerial images, multiple scene-level labels were manually assigned to each image from in total 24 scene categories, including apron, baseball, beach, commercial, farmland, woodland, parking lot, port, residential, river, storage tanks, sea, bridge, lake, park, roundabout, soccer field, stadium, train station, works, golf course, runway, sparse shrub, and tennis court

NavigationNet is a computer vision dataset and benchmark to allow the utilization of deep reinforcement learning on scene-understanding-based indoor navigation. Source: NavigationNet: A Large-scale Interactive Indoor Navigation Dataset Image Source: https://arxiv.org/pdf/1808.08374v1.pdf

Panoramic Video Panoptic Segmentation Dataset is a large-scale dataset that offers high-quality panoptic segmentation labels for autonomous driving. The dataset has labels for 28 semantic categories and 2,860 temporal sequences that were captured by five cameras mounted on autonomous vehicles driving in three different geographical locations, leading to a total of 100k labeled camera images. Source: Waymo Open Dataset: Panoramic Video Panoptic Segmentation Image Source: https://arxiv.org/pdf/2206.07704v1.pdf

Scene Graph Generation (SGG) converts visual scenes into structured graph representations, providing deeper scene understanding for complex vision tasks. However, existing SGG models often overlook essential spatial relationships and struggle with generalization in open-vocabulary contexts. To address these limitations, we propose LLaVA-SpaceSGG, a multimodal large language model (MLLM) designed for open-vocabulary SGG with enhanced spatial relation modeling. To train it, we collect the SGG instruction-tuning dataset, named SpaceSGG. This dataset is constructed by combining publicly available datasets and synthesizing data using open-source models within our data construction pipeline. It combines object locations, object relations, and depth information, resulting in three data formats: spatial SGG description, question-answering, and conversation. To enhance the transfer of MLLMs' inherent capabilities to the SGG task, we introduce a two-stage training paradigm. Experiments show that LLaVA-SpaceSGG outperforms other open-vocabulary SGG methods, boosting recall by 8.6% and mean recall by 28.4% compared to the baseline. Our codebase, dataset, and trained models are publicly accessible on GitHub at the following URL: https://github.com/Endlinc/LLaVA-SpaceSGG.

Green family of datasets for emergent communications on relations. By comparison with other relations datasets, TexRel provides rapid training and experimentation, whilst being sufficiently large to avoid overfitting in the context of emergent communications.

The UTRSet-Real dataset is a comprehensive, manually annotated dataset specifically curated for Printed Urdu OCR research. It contains over 11,000 printed text line images, each of which has been meticulously annotated. One of the standout features of this dataset is its remarkable diversity, which includes variations in fonts, text sizes, colours, orientations, lighting conditions, noises, styles, and backgrounds. This diversity closely mirrors real-world scenarios, making the dataset highly suitable for training and evaluating models that aim to excel in real-world Urdu text recognition tasks. The availability of the UTRSet-Real dataset addresses the scarcity of comprehensive real-world printed Urdu OCR datasets. By providing researchers with a valuable resource for developing and benchmarking Urdu OCR models, this dataset promotes standardized evaluation and reproducibility and fosters advancements in the field of Urdu OCR. Further, to complement the UTRSet-Real for training purposes, we also present UTRSet-Synth, a high-quality synthetic dataset closely resembling real-world representations of Urdu text. For more information and details about the UTRSet-Real & UTRSet-Synth datasets, please refer to the paper "UTRNet: High-Resolution Urdu Text Recognition In Printed Documents"

The UTRSet-Synth dataset is introduced as a complementary training resource to the UTRSet-Real Dataset, specifically designed to enhance the effectiveness of Urdu OCR models. It is a high-quality synthetic dataset comprising 20,000 lines that closely resemble real-world representations of Urdu text. To generate the dataset, a custom-designed synthetic data generation module which offers precise control over variations in crucial factors such as font, text size, colour, resolution, orientation, noise, style, and background, was employed. Moreover, the UTRSet-Synth dataset tackles the limitations observed in existing datasets. It addresses the challenge of standardizing fonts by incorporating over 130 diverse Urdu fonts, which were thoroughly refined to ensure consistent rendering schemes. It overcomes the scarcity of Arabic words, numerals, and Urdu digits by incorporating a significant number of samples representing these elements. Additionally, the dataset is enriched by randomly selecting words from a vocabulary of 100,000 words during the text generation process. As a result, UTRSet-Synth contains a total of 28,187 unique words, with an average word length of 7 characters. The availability of the UTRSet-Synth dataset, a synthetic dataset that closely emulates real-world variations, addresses the scarcity of comprehensive real-world printed Urdu OCR datasets. By providing researchers with a valuable resource for developing and benchmarking Urdu OCR models, this dataset promotes standardized evaluation, and reproducibility, and fosters advancements in the field of Urdu OCR. For more information and details about the UTRSet-Real & UTRSet-Synth datasets, please refer to the paper "UTRNet: High-Resolution Urdu Text Recognition In Printed Documents"

This repository contains datasets and baselines for benchmarking Chinese text recognition. Please see the corresponding paper for more details regarding the datasets, baselines, the empirical study, etc. Highlights 🌟 All datasets are transformed to lmdb format for convenient usage. 🌟 The experimental results of all baselines are available at link with format (index [pred] [gt]). 🌟 The code and trained weights of all baselines are available at link for direct use.

This dataset is an extremely challenging set of over 5000+ original India food images captured and crowdsourced from over 800+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at ****DC Labs. ### Dataset Features - Dataset size : 5000+ - Captured by : Over 800+ crowdsource contributors - Resolution : 99% images HD and above (1920x1080 and above) - Location : Captured with 800+ cities accross India - Diversity : Various lighting conditions like day, night, varied distances, view points, dimlight etc. - Device used : Captured using mobile phones in 2020-2021 - Usage : Indian food classification, Dish classification, Food plate detection, etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform

Scene Recognition is a problem, where a set of visible objects must be correctly associated with objects marked on a semantic map - this problem is also sometimes called a Data Association. Please note, that Scene Recognition in terms where the observed scene must be labeled in terms such as 'kitchen', 'bedroom' and so on is a different problem. Scene Recognition is used in different mobile robot localization problems, such as semantic mapping, global localization, kidnapping, loop-closing and so on. But problem is that there is no special dataset for that task (at least I found none). Such dataset should have the following features: - a Semantic map with globally marked objects; - scenes data (views from robot camera), where objects are also labeled and their IDs are correctly matched with IDs on the Semantic map. For full info please follow https://github.com/MoscowskyAnton/scene_recognition_kitti_360

This dataset contains images and annotations for scene text detection and recognition. It is made up of two parts: (1) 1,175 images manually labeled with a total of 59,588 text instances at the line and word levels; and (2) 929 signboard images collected from the VinText, Total-Text, and ICDAR15 datasets. Each text instance in the first part of our dataset has a quadrilateral bounding box and a ground truth character sequence associated with it. In the second part, images are selected if they contain signboards. This portion of the dataset comprises 20,261 text instances at word levels. This brings the total text instances in our final dataset up to 79,814. Following the ICDAR15 standard, we annotated each image with all of the text instances, polygons, and content that were present. Manual annotations were done on each and every image.

This dataset is an extremely challenging set of over 3000+ originally Stair images captured and crowdsourced from over 500+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs. ### Dataset Features - Dataset size : 3000+ - Captured by : Over 500+ crowdsource contributors - Resolution : 100% images HD and above (1920x1080 and above) - Location : Captured with 500+ cities accross India - Diversity : Various lighting conditions like day, night, varied distances, view points etc. - Device used : Captured using mobile phones in 2020-2022 - Usage : Stair detection , Stair Edge detection , Computer Vision , etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform

The Turkish Scene Text Recognition (TS-TR) dataset was primarily developed to fill the gap in non-English text recognition resources, specifically addressing the unique challenges presented by the Turkish language, such as special characters and diacritics. This dataset mirrors real-world conditions with texts displayed in various fonts, sizes, orientations, and complex backgrounds from multiple urban and rural environments. Such diversity ensures the training of models that can generalize across different scenarios, including varying lighting conditions and complex visual layouts.