19,997 machine learning datasets
19,997 dataset results
The Peripheral Blood Cell} (PBC) dataset consists of 17,092 images. These images are further organized into the following eight groups: neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (including promyelocytes, myelocytes, and metamyelocytes), erythroblasts, and platelets or thrombocytes. Each image is 360 x 363 pixels in size and is in JPG format, annotated by expert clinical pathologists. This dataset focuses on images of peripheral blood cells. For our newly introduced dataset, we have selected five types of white blood cells from this dataset.
T(BCD) dataset consisted of a total of 364 blood smear images with annotations. The dataset is labeled three groups: “RBC”, “WBC”, and “Platelets”. There are 364 images with 416 x 416 pixels resolution in the BCD dataset. This dataset was published on https://www.kaggle.com/datasets/adhoppin/blood-celldetection-datatset.
(WBC) dataset which consisted of 14514 WBC images across five classes 301 basophils, 795 monocytes, 1066 eosinophils, 8891 neutrophils, and 3461 lymphocytes at resolutions of 575 x 575. The data set mainly focuses on the classification of white blood cells. We selected each type of white blood cells and introduced them into our new data set.
We introduce a new synthetic test set named IS3 for interactive sound source localization. By leveraging diffusion models, we generate images containing multiple sounding objects. Any combination of sounding objects can appear in the same scene. Additionally, this dataset offers unusual scenes and unique combinations that are rarely found in nature, such as ‘a donkey playing a saxophone’ or ‘a sea lion on the snow’. This dataset provides both segmentation maps and bounding box information with class categories. IS3 includes 3240 images, resulting in 6480 unique audio-visual instances (with 2 objects per image) across 118 categories. This dataset can be used in below tasks: 1) Sound Source Localization 2) Audio-Visual Segmentation 3) Semantic Segmentation
🎯 DART-Math
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CAMO-FS Dataset comes with the paper entitled The Art of Camouflage: Few-shot Learning for Animal Detection and Segmentation.
100 samples each of synthetic speech generated by 9 moderns TTS systems. They all use the same subset of speaker-text pairs for conditioning.
Visual Haystacks (VHs) is a "visual-centric" Needle-In-A-Haystack (NIAH) benchmark specifically designed to evaluate the capabilities of Large Multimodal Models (LMMs) in visual retrieval and reasoning over sets of unrelated images. Unlike conventional NIAH challenges that center on text-related retrieval and understanding with limited anecdotal examples, VHs contains a much larger number of examples and focuses on "simple visual tasks", providing a more accurate reflection of LMMs' capabilities when dealing with extensive visual context.
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includes train, test and validation set
Procedural videos show step-by-step demonstrations of tasks like recipe preparation. Understanding such videos is challenging, involving the precise localization of steps and the generation of textual instructions. Manually annotating steps and writing instructions is costly, which limits the size of current datasets and hinders effective learning. Leveraging large but noisy video-transcript datasets for pre-training can boost performance, but demands significant computational resources. Furthermore, transcripts contain irrelevant content and exhibit style variation compared to instructions written by human annotators. To mitigate both issues, we propose a technique, Sieve-&-Swap, to automatically curate a smaller dataset: (i) Sieve filters irrelevant transcripts and (ii) Swap enhances the quality of the text instruction by automatically replacing the transcripts with human-written instructions from a text-only recipe dataset. The curated dataset, three orders of magnitude smaller than
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This dataset consists of books digitised by the British Library in partnership with Microsoft. The dataset includes ~25 million pages of out of copyright texts. The majority of the texts were published in the 18th and 19th Century, but the collection also consists of a smaller number of books from earlier periods. Items within this collection cover a wide range of subject areas, including geography, philosophy, history, poetry and literature and are published in various languages.
The progress of Large Language Models (LLMs) has largely been driven by the availability of large-scale unlabeled text data for unsupervised learning. This work focuses on modeling both content and the corresponding receiver behavior in the same space. Although existing datasets have trillions of content tokens (text, images, audio, and videos), they lack information on receiver effects. To address this, the paper utilizes YouTube, a large publicly available source of content-behavior data, which includes:
LAMDBA is a long term ad memorability dataset, featuring data from 1749 participants and 2205 ads across 276 brands.
UltraLAMBDAis a large-scale dataset of ads sourced from brand videos on platforms such as YouTube and Facebook Ads, as well as from CommonCrawl. The memorability scores for the ads are assigned by our model Henry.
This data comprises processed weather, soil, yield, and cultivation area for corn yield prediction in Sub-Sahara Africa, with emphasis on Nigeria. The data was collected to design a corn yield prediction model to help smallholder farmers make smart farming decisions. However, the data can serve several other purposes through analysis and interpretation.
Forecasting future world events is a challenging but valuable task. Forecasts of climate, geopolitical conflict, pandemics and economic indicators help shape policy and decision making. In these domains, the judgment of expert humans contributes to the best forecasts. Given advances in language modeling, can these forecasts be automated? To this end, we introduce Autocast, a dataset containing thousands of forecasting questions and an accompanying news corpus. Questions are taken from forecasting tournaments, ensuring high quality, real-world importance, and diversity. The news corpus is organized by date, allowing us to precisely simulate the conditions under which humans made past forecasts (avoiding leakage from the future). We test language models on our forecasting task and find that performance is far below a human expert baseline. However, performance improves with increased model size and incorporation of relevant information from the news corpus. In sum, Autocast poses a novel
This 2d indoor dataset collection consists of 9 individual datasets. Each dataset contains odometry and (range and bearing) measurement data from 5 robots, as well as accurate groundtruth data for all robot poses and (15) landmark positions. The dataset is intended for studying the problems of cooperative localization (with only a team robots), cooperative localization with a known map, and cooperative simultaneous localization and mapping (SLAM).