Explore the Synthetic Rock Paper Scissors Dataset featuring a diverse collection of augmented images for training and testing machine learning models.

Part 2 of the synthetic facial data rendered from female fbx models. The total dataset contains around 13k facial images generated from 12 identity and the corresponding raw facial depth and head pose.

The dataset contains 3D point cloud data of a synthetic plant with 10 sequences. Each sequence contains 0-19 days data at every growth stage of the specific sequence.

See the other two datasets in this project for more specific information.Baseline datasetComprised of images (and the corresponding labels in labels) as well as the data in images_new and labels_new. The data in images was collected from the power plant satellite imagery dataset, and the data in images_new is naip imagery that was collected through EarthOnDemand and then hand labeled.Modified DatasetContains all of the labels and images in the dataset, including the synthetic. Same as the baseline dataset, but supplementing training set with the synthetic images.

SYNTHETIC dataset to replicate the results in "Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis", accepted to IEEE/RSJ IROS 2022.

In order to fully reproduce the experiments, download also the REAL dataset.

To automatically download the REAL and SYNTHETIC dataset, run the script provided at the link below.

Code to replicate the results available at: https://github.com/hsp-iit/prosthetic-grasping-experiments

The global Artificial Intelligence (AI) in Computer Vision Market size is expected to reach USD 111.47 billion by 2032 according to a new study by Polaris Market Research.

Synthetic dataset of over 13,000 images of damaged and intact parcels with full 2D and 3D annotations in the COCO format. For details see our paper and for visual samples our project page.

Relevant computer vision tasks:

bounding box detection

classification

instance segmentation

keypoint estimation

3D bounding box estimation

3D voxel reconstruction

3D reconstruction

The dataset is for academic research use only, since it uses resources with restrictive licenses. For a detailed description of how the resources are used, we refer to our paper and project page.

Licenses of the resources in detail:

Google Scanned Objects: CC BY 4.0 (for details on which files are used, see the respective meta folder)

Cardboard Dataset: CC BY 4.0

Shipping Label Dataset: CC BY-NC 4.0

Other Labels: See file misc/source_urls.json

LDR Dataset: License for Non-Commercial Use

Large Logo Dataset (LLD): Please notice that this dataset is made available for academic research purposes only. All the images are collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to you and you would like it removed, please kindly inform us, we will remove it from our dataset immediately.

You can use our textureless models (i.e. the obj files) of damaged parcels under CC BY 4.0 (note that this does not apply to the textures).

If you use this resource for scientific research, please consider citing

@inproceedings{naumannParcel3DShapeReconstruction2023, author = {Naumann, Alexander and Hertlein, Felix and D"orr, Laura and Furmans, Kai}, title = {Parcel3D: Shape Reconstruction From Single RGB Images for Applications in Transportation Logistics}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {4402-4412} }

The global market size in the 'Computer Vision' segment of the artificial intelligence market was forecast to continuously increase between 2024 and 2030 by in total 21.2 billion U.S. dollars (+82.17 percent). After the eighth consecutive increasing year, the market size is estimated to reach 46.96 billion U.S. dollars and therefore a new peak in 2030. Find more key insights for the market size in countries and regions like the market size in the 'Generative AI' segment of the artificial intelligence market in Australia and the number of AI tools users in the 'AI Tool Users' segment of the artificial intelligence market in the world. The Statista Market Insights cover a broad range of additional markets.

Today, the cutting edge of computer vision research greatly depends on the availability of large datasets, which are critical for effectively training and testing new methods. Manually annotating visual data, however, is not only a labor-intensive process but also prone to errors. In this study, we present NOVA, a versatile framework to create realistic-looking 3D rendered worlds containing procedurally generated humans with rich pixel-level ground truth annotations. NOVA can simulate various environmental factors such as weather conditions or different times of day, and bring an exceptionally diverse set of humans to life, each having a distinct body shape, gender and age. To demonstrate NOVA's capabilities, we generate two synthetic datasets for person tracking. The first one includes 108 sequences, each with different levels of difficulty like tracking in crowded scenes or at nighttime and aims for testing the limits of current state-of-the-art trackers. A second dataset of 97 sequences with normal weather conditions is used to show how our synthetic sequences can be utilized to train and boost the performance of deep-learning based trackers. Our results indicate that the synthetic data generated by NOVA represents a good proxy of the real-world and can be exploited for computer vision tasks.

The statistic shows the size of the computer vision market worldwide, by segment, from 2014 to 2019. In 2014, the global computer vision AI hardware market was said to be worth 1.38 billion U.S. dollars.

About this dataset

This dataset contains 6,000 example images generated with the process described in Roboflow's How to Create a Synthetic Dataset tutorial.

The images are composed of a background (randomly selected from Google's Open Images dataset) and a number of fruits (from Horea94's Fruit Classification Dataset) superimposed on top with a random orientation, scale, and color transformation. All images are 416x550 to simulate a smartphone aspect ratio.

To generate your own images, follow our tutorial or download the code.

Example: https://blog.roboflow.ai/content/images/2020/04/synthetic-fruit-examples.jpg" alt="Example Image">

As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on recognizing textures and materials in real-world images, which plays an important role in object recognition and scene understanding. Aiming at describing objects or scenes with more detailed information, we explore how to computationally characterize apparent or latent properties (e.g. surface smoothness) of materials, i.e., computational material characterization, which moves a step further beyond material recognition. For this purpose, we introduce a large, publicly available dataset named challenging microscopic material surface dataset (CoMMonS). We utilize a powerful microscope to capture high-resolution images with fine details of fabric surfaces. The CoMMonS dataset consists of 6,912 images covering 24 fabric samples in a controlled environment under varying imaging conditions such as lighting, zoom levels, geometric variations, and touching directions. This dataset can be used to assess the performance of existing deep learning-based algorithms and to develop our own method for material characterization in terms of fabric properties such as fiber length, surface smoothness, and toweling effect. Please refer to our GitHub page for code, papers, and more information.

Global Artificial Intelligence (AI) in Computer Vision Market size was valued at USD 20.7 billion in 2022 and is poised to grow from USD 25.8 billion in 2023 to USD 148.8 billion by 2031, growing at a CAGR of 24.5% during the forecast period (2024-2031).

The global Artificial Intelligence (AI) in computer vision market was valued at USD 117.47 billion in 2023 and is predicted to reach at a CAGR of 23.6% during 2024 - 2032.

We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and 2 male professional actors performing various full-body movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc.), along with multi-RGBD (mRGBD), volumetric and audio data. Despite the existence of multi-view color datasets captured with the use of hardware (HW) synchronization, to the best of our knowledge, HUMAN4D is the first and only public resource that provides volumetric depth maps with high synchronization precision due to the use of intra- and inter-sensor HW-SYNC. Moreover, a spatio-temporally aligned scanned and rigged 3D character complements HUMAN4D to enable joint research on time-varying and high-quality dynamic meshes. We provide evaluation baselines by benchmarking HUMAN4D with state-of-the-art human pose estimation and 3D compression methods. For the former, we apply 2D and 3D pose estimation algorithms both on single- and multi-view data cues. For the latter, we benchmark open-source 3D codecs on volumetric data respecting online volumetric video encoding and steady bit-rates. Furthermore, qualitative and quantitative visual comparison between mesh-based volumetric data reconstructed in different qualities showcases the available options with respect to 4D representations. HUMAN4D is introduced to the computer vision and graphics research communities to enable joint research on spatio-temporally aligned pose, volumetric, mRGBD and audio data cues.The dataset and its code are available online.

Part 3 of the synthetic facial data rendered from male fbx models. The total dataset contains around 24k facial images generated from 14 identity and the corresponding raw facial depth and head pose.

Models for egocentric 3D and 4D reconstruction, including few-shot interpolation and extrapolation settings, can benefit from having images from exocentric viewpoints as supervision signals. No existing dataset provides the necessary mixture of complex, dynamic, and multi-view data. To facilitate the development of 3D and 4D reconstruction methods in the autonomous driving context, we propose a Synthetic Ego--Exo Dynamic 4D (SEED4D) dataset. SEED4D encompasses two large-scale multi-view synthetic urban scene datasets. Our static (3D) dataset encompasses 212k inward- and outward-facing vehicle images from 2k scenes, while our dynamic (4D) dataset contains 16.8M images from 10k trajectories, each sampled at 100 points in time with egocentric images, exocentric images, and LiDAR data. This Kaggle repository contains a subset of those data.

The Synthetic Underwater Environment Images Dataset comprises 11,400 images with ground truth, generated using Unreal Engine. The dataset encompasses a wide range of underwater settings to simulate different underwater conditions, making each image distinct from the others. Additionally, the dataset incorporates a diverse array of marine life forms, including fish, algae, coral reefs, and sand particles, meticulously integrated into the images to enhance realism. The scenes were generated at 30 frames per second with a resolution of 1920×1080p. The settings for a DSLR camera were used with an aspect ratio of 16:9. The dataset was generated at three different levels of intensity for light and fog: low, medium, and high. None of the images were preprocessed after capture. The dataset has two distinct scenes, each scene having 19 zipped files: one for the ground truth and the remaining for different underwater settings, making 38 zipped files in total, each containing 300 images for both scenes. Among these, S1_GT and S2_GT refer to the folders containing the ground truth images of the two scenes. The remaining zipped folders are named as Sx_Cx_Lx_Fx, where Sx indicates the scene number (S1 for scene one and S2 for scene two). Cx indicates the color of the water (C1 for blue water and C2 for green water). Lx and Fx indicate the light and fog intensity, respectively. 1 denotes low intensity, 2 for medium intensity, and 3 for the highest intensity. For illustration, S2_C2_L3_F1 means that the image is generated from the second scene with green water, high light intensity, and low fog intensity.

Explore the CIFAKE dataset with 60,000 real and 60,000 AI-generated images for machine learning and computer vision research.

The global image recognition solution market is experiencing robust growth, projected to reach $663.4 million in 2025. While the provided CAGR is missing, considering the rapid advancements in AI and computer vision technologies, and the increasing adoption across diverse sectors like government, SMEs, and large enterprises, a conservative estimate of a 15% CAGR for the forecast period (2025-2033) is reasonable. This implies significant market expansion, driven by factors such as the increasing availability of high-quality data, improvements in deep learning algorithms, and the rising demand for automated image analysis across various applications. The market segmentation reveals a strong presence across different service models (SaaS, PaaS, IaaS) indicating a diverse range of deployment options catering to various organizational needs and technological capabilities. The geographical distribution reveals a strong market presence in North America and Europe, driven by early adoption and technological advancements, with significant growth potential in Asia-Pacific regions fueled by rapid digitalization and increasing investments in AI infrastructure. The leading players in this dynamic market landscape—including established tech giants like Amazon Web Services and Google alongside specialized companies like Clarifai and Scandit—are constantly innovating to improve accuracy, speed, and scalability of image recognition solutions. The competitive landscape is characterized by a combination of established players and emerging startups focusing on niche applications and specialized functionalities. Factors such as increasing data privacy concerns and the need for robust cybersecurity measures present certain restraints; however, continuous innovation and regulatory compliance efforts are likely to mitigate these challenges, paving the way for continued market growth and expansion into new sectors. The interplay of technological advancements, increasing demand, and competitive activity ensures a positive outlook for the image recognition solution market in the coming years.