1. MRI (Brain Tumor Segmentation - BraTS 2020) Kaggle Link: https://www.kaggle.com/datasets/awsaf49/brats20-dataset-training-validation BRATS 2020 Dataset Contains multi-modal MRI scans (T1, T1ce, T2, FLAIR) with tumor segmentation masks. 2. Ultrasound (Breast Ultrasound Images) Kaggle Link: https://www.kaggle.com/datasets/aryashah2k/breast-ultrasound-images-dataset Breast Ultrasound Dataset Includes breast ultrasound images (normal, benign, malignant) with lesion annotations. 3.
The proposed method is rigorously evaluated against several state-of-the-art algorithms, including ISAC, ITD3, IPPO, and IDDPG, to ensure a comprehensive performance analysis. The experimental data, which is publicly available [here], provides detailed insights into the training and evaluation processes of each algorithm.
This dataset comprises 33,800 images of underwater signals captured in aquatic environments. Each signal is presented against three types of backgrounds: pool, marine, and plain white. Additionally, the dataset includes three water tones: clear, blue, and green. A total of 12 different signals are included, each available in all six possible background-tone combinations.
This dataset is designed to advance research in Visual Question Answering (VQA), specifically addressing challenges related to language priors and compositional reasoning. It incorporates question labels categorizing queries based on their susceptibility to either issue, allowing for targeted evaluation of VQA models. The dataset consists of 33,051 training images and 14,165 validation images, along with 571,244 training questions and 245,087 validation questions. Among the training questions, 313,664 focus on compositional reasoning, while 257,580 pertain to language prior.
The dataset consists of satellite optical images, landslide boundary shapefiles, and digital elevation models. It includes 770 landslide samples, comprising rockfalls, rockslides, and some debris landslides, along with 2003 negative samples covering various backgrounds. These samples were cropped from TripleSat satellite images taken between May and August 2018, with an image resolution of 0.8 meters.
A dataset has been created by recoloring three existing datasets: NeRF Synthetic, LLFF, and Mip 360. The recoloring was performed to provide ground truth for validating recoloring applications. NeRF Synthetic was recolored using Blender, while LLFF and Mip 360 were processed in Photoshop. For each scene in the datasets, 11 images were recolored, ensuring consistency across the datasets.
This paper presents an innovative Internet of Things (IoT) system that integrates gas sensors and a custom Convolutional Neural Network (CNN) to classify the freshness and species of beef and mutton in real time. The CNN, trained on 9,928 images, achieved 99% accuracy, outperforming models like ResNet-50, SVM, and KNN. The system uses three gas sensors (MQ135, MQ4, MQ136) to detect gases such as ammonia, methane, and hydrogen sulfide, which indicate meat spoilage.
Grasping and manipulating objects is an important human skill. Since hand-object contact is fundamental to grasping, capturing it can lead to important insights. However, observing contact through external sensors is challenging because of occlusion and the complexity of the human hand. We present ContactDB, a novel dataset of contact maps for household objects that captures the rich hand-object contact that occurs during grasping, enabled by use of a thermal camera. Participants in our study grasped 3D printed objects with a post-grasp functional intent.
Grasping is natural for humans. However, it involves complex hand configurations and soft tissue deformation that can result in complicated regions of contact between the hand and the object. Understanding and modeling this contact can potentially improve hand models, AR/VR experiences, and robotic grasping. Yet, we currently lack datasets of hand-object contact paired with other data modalities, which is crucial for developing and evaluating contact modeling techniques. We introduce ContactPose, the first dataset of hand-object contact paired with hand pose, object pose, and RGB-D images.
This is an optical flow dataset which covers multiple independent moving object samples under various adverse weather. This is an optical flow dataset which covers multiple independent moving object samples under various adverse weather. This is an optical flow dataset which covers multiple independent moving object samples under various adverse weather.
