19,997 machine learning datasets
19,997 dataset results
Rethinking Abdominal Organ Segmentation (RAOS) in the clinical scenario: A robustness evaluation benchmark with challenging cases.
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This is the paper βDF-RAP: A Robust Adversarial Perturbation for Defending against Deepfakes in Real-world Social Network Scenarios" OSN-transmission CelebA sampling dataset collected by manual upload and download. This dataset includes 30,000 facial images of size 256Γ256 transmitted through online social networks (OSN) and their corresponding original images. Among them, Facebook, Twitter, WeChat and Weibo were selected as the transmission OSN, with 7500 images each.
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
Images for Classification, Segmentation, Object Detection, Upsampling, and Edge LLM Feature Noise-Augmented Dataset for Semantic Communication
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Dataset for testing the ability of Vision Language Models (LVM) to recognize and match 3D objects of the exact same 3D shapes but with different orientation/materials/textures/ environments and light conditions.
MΒ²ConceptBase is a concept-centric multimodal knowledge base designed to bridge the gap between visual and linguistic semantics. It features 951K images and 152K concepts, with each concept linked to an average of 6.27 images and a detailed textual description.
Dataset Overview The dataset used for training and testing consists of vibration signals for six pump conditions:
Dataset Description This dataset consists of Electroencephalography (EEG) data recorded from 15 healthy subjects using a 64-channel EEG headset during spoken and imagined speech interaction with a simulated robot.
We collect a dataset of 805 clean videos that show the action of pouring water in a container. Our dataset spans over 50 unique containers made of 5 different materials, 4 different shapes and with hot and cold water.
The Construction Industry Steel Ordering Lists (CISOL) dataset comprises table-centric, real-world documents from the construction industry, annotated to facilitate the testing and training of deep learning models for table detection (TD) and table structure recognition (TSR).
A large-scale gloss-free sign language translation dataset with 1,985 hours of videos, approximately 86 times larger than the previous CSL-Daily dataset.
We have uploaded a sample dataset for training and testing Back-Projection Diffusion. Trained model parameters for the dataset are also provided in tmp.zip.
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
MP-IDB comprises four species of Malaria parasites: Falciparum, Malariae, Ovale, Vivax. For each species, there are four distinct stages of life, described in the filenames as follows:
In this project, we tried to make malaria detection easily possible at a low cost. We present M5-malaria Dataset which is the first-ever dataset that is across microscopes and across magnifications. Malaria, a fatal but curable disease claims hundreds of thousands of lives every year. Early and correct diagnosis is vital to avoid health complexities, however, it depends upon the availability of costly microscopes and trained experts to analyze blood-smear slides. Deep learning-based methods have the potential to not only decrease the burden of experts but also improve diagnostic accuracy on low-cost microscopes. However, this is hampered by the absence of a reasonable size dataset. One of the most challenging aspects is the reluctance of the experts to annotate the dataset at low magnification on low-cost microscopes. We present a dataset to further the research on malaria microscopy over the low-cost microscopes at low magnification. Our large-scale dataset consists of images of blood