3,275 machine learning datasets
3,275 dataset results
The COCO-MLT is created from MS COCO-2017, containing 1,909 images from 80 classes. The maximum of training number per class is 1,128 and the minimum is 6. We use the test set of COCO2017 with 5,000 for evaluation. The ratio of head, medium, and tail classes is 22:33:25 in COCO-MLT.
We construct the long-tailed version of VOC from its 2012 train-val set. It contains 1,142 images from 20 classes, with a maximum of 775 images per class and a minimum of 4 images per class. The ratio of head, medium, and tail classes after splitting is 6:6:8. We evaluate the performance on VOC2007 test set with 4952 images.
The ACNE04 dataset includes 3756 Chinese face images with Acne. The ACNE04 dataset includes the annotations of local lesion numbers and global acne severity based on Hayashi Criterion.
Large Multimodal Models (LMMs) such as GPT-4V and LLaVA have shown remarkable capabilities in visual reasoning with common image styles. However, their robustness against diverse style shifts, crucial for practical applications, remains largely unexplored. In this paper, we propose a new benchmark, BenchLMM, to assess the robustness of LMMs against three different styles: artistic image style, imaging sensor style, and application style, where each style has five sub-styles. Utilizing BenchLMM, we comprehensively evaluate state-of-the-art LMMs and reveal: 1) LMMs generally suffer performance degradation when working with other styles; 2) An LMM performs better than another model in common style does not guarantee its superior performance in other styles; 3) LMMs' reasoning capability can be enhanced by prompting LMMs to predict the style first, based on which we propose a versatile and training-free method for improving LMMs; 4) An intelligent LMM is expected to interpret the causes of
Math-Vision (Math-V) dataset is a meticulously curated collection of 3,040 high-quality mathematical problems with visual contexts sourced from real math competitions. Spanning 16 distinct mathematical disciplines and graded across 5 levels of difficulty, our dataset provides a comprehensive and diverse set of challenges for evaluating the mathematical reasoning abilities of LMMs.
We introduce the MultiModal Needle-in-a-haystack (MMNeedle) benchmark, specifically designed to assess the long-context capabilities of MLLMs. Besides multi-image input, we employ image stitching to further increase the input context length, and develop a protocol to automatically generate labels for sub-image level retrieval. Essentially, MMNeedle evaluates MLLMs by stress-testing their capability to locate a target sub-image (needle) within a set of images (haystack) based on textual instructions and descriptions of image contents. This setup necessitates an advanced understanding of extensive visual contexts and effective information retrieval within long-context image inputs.
WildScenes is a bi-modal benchmark dataset consisting of multiple large-scale, sequential traversals in natural environments, including semantic annotations in high-resolution 2D images and dense 3D LiDAR point clouds, and accurate 6-DoF pose information. The data is (1) trajectory-centric with accurate localization and globally aligned point clouds, (2) calibrated and synchronized to support bi-modal training and inference, and (3) containing different natural environments over 6 months to support research on domain adaptation. We introduce benchmarks on 2D and 3D semantic segmentation and evaluate a variety of recent deep-learning techniques to demonstrate the challenges in semantic segmentation in natural environments. We propose train-val-test splits for standard benchmarks as well as domain adaptation benchmarks and utilize an automated split generation technique to ensure the balance of class label distributions. The WildScenes benchmark webpage is https://csiro-robotics.github.i
100 tasks from LIBERO-100 suite. Note that the datasets are split under the folder names of LIBERO-90 and LIBERO-10. The 10 contains selected task that require long-horizon task completion.
An important factor in advancing autonomous driving systems is simulation. Yet, there is rather small progress for transferability between the virtual and real world. We revisit this problem for 3D object detection on LiDAR point clouds and propose a dataset generation pipeline based on the CARLA simulator. Utilizing domain randomization strategies and careful modeling, we are able to train an object detector on the synthetic data and demonstrate strong generalization capabilities to the KITTI dataset.
These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines.
WiderPerson contains a total of 13,382 images with 399,786 annotations, i.e., 29.87 annotations per image, which means this dataset contains dense pedestrians with various kinds of occlusions. Hence, pedestrians in the proposed dataset are extremely challenging due to large variations in the scenario and occlusion, which is suitable to evaluate pedestrian detectors in the wild.
TUM monoVO is a dataset for evaluating the tracking accuracy of monocular Visual Odometry (VO) and SLAM methods. It contains 50 real-world sequences comprising over 100 minutes of video, recorded across different environments – ranging from narrow indoor corridors to wide outdoor scenes. All sequences contain mostly exploring camera motion, starting and ending at the same position: this allows to evaluate tracking accuracy via the accumulated drift from start to end, without requiring ground-truth for the full sequence. In contrast to existing datasets, all sequences are photometrically calibrated: the dataset creators provide the exposure times for each frame as reported by the sensor, the camera response function and the lens attenuation factors (vignetting).
The ISIC 2018 dataset was published by the International Skin Imaging Collaboration (ISIC) as a large-scale dataset of dermoscopy images. The Task 3 dataset is the challenge on lesion classification. It includes 2594 images. The task is to classify the dermoscopic images into one of the following categories: melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis / Bowen’s disease, benign keratosis, dermatofibroma, and vascular lesion.
BCN_20000 is a dataset composed of 19,424 dermoscopic images of skin lesions captured from 2010 to 2016 in the facilities of the Hospital Clínic in Barcelona. The dataset can be used for lesion recognition tasks such as lesion segmentation, lesion detection and lesion classification.
The Million Song Dataset is a freely-available collection of audio features and metadata for a million contemporary popular music tracks.
FoodX-251 is a dataset of 251 fine-grained classes with 118k training, 12k validation and 28k test images. Human verified labels are made available for the training and test images. The classes are fine-grained and visually similar, for example, different types of cakes, sandwiches, puddings, soups, and pastas.
The GTA Indoor Motion dataset (GTA-IM) that emphasizes human-scene interactions in the indoor environments. It consists of HD RGB-D image sequences of 3D human motion from a realistic game engine. The dataset has clean 3D human pose and camera pose annotations, and large diversity in human appearances, indoor environments, camera views, and human activities.
A large-scale Landmark guided face Parsing dataset (LaPa) for face parsing. It consists of more than 22,000 facial images with abundant variations in expression, pose and occlusion, and each image of LaPa is provided with a 11-category pixel-level label map and 106-point landmarks.
A data-set which consists of over one million images of physical 3D objects with seven factors of variation, such as object color, shape, size and position.
A large-scale collection of visually-grounded, task-oriented dialogues in English designed to investigate shared dialogue history accumulating during conversation.