3,275 machine learning datasets
3,275 dataset results
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We construct the ForgeryNet dataset, an extremely large face forgery dataset with unified annotations in image- and video-level data across four tasks: 1) Image Forgery Classification, including two-way (real / fake), three-way (real / fake with identity-replaced forgery approaches / fake with identity-remained forgery approaches), and n-way (real and 15 respective forgery approaches) classification. 2) Spatial Forgery Localization, which segments the manipulated area of fake images compared to their corresponding source real images. 3) Video Forgery Classification, which re-defines the video-level forgery classification with manipulated frames in random positions. This task is important because attackers in real world are free to manipulate any target frame. and 4) Temporal Forgery Localization, to localize the temporal segments which are manipulated. ForgeryNet is by far the largest publicly available deep face forgery dataset in terms of data-scale (2.9 million images, 221,247 video
The GMVD dataset consists of synthetic scenes captured using the GTA-V and Unity graphics engines. The dataset covers a variety of scenes, along with different conditions including day time variations (morning, afternoon, evening, night) and weather variations (sunny, cloudy, rainy, snowy). The purpose of the dataset is twofold. The first is to benchmark the generalization capabilities of Multi-View Detection algorithms. The second purpose is to serve as a synthetic training source from which the trained models can be directly applied on real-world data.
DurLAR is a high-fidelity 128-channel 3D LiDAR dataset with panoramic ambient (near infrared) and reflectivity imagery for multi-modal autonomous driving applications. Compared to existing autonomous driving task datasets, DurLAR has the following novel features:
FS2K is a high-quality Facial Sketch Synthesis (FSS). It consists of 2,104 image-sketch pairs spanning three types of sketch styles, image backgrounds, lighting conditions, skin colors, and facial attributes. FS2K differs from previous FSS datasets in difficulty, diversity, and scalability, and should thus facilitate the progress of FSS research.
The IUPUI-CSRC Pedestrian Situated Intent (PSI) benchmark dataset has two innovative labels besides comprehensive computer vision annotations. The first novel label is the dynamic intent changes for the pedestrians to cross in front of the ego-vehicle, achieved from 24 drivers with diverse backgrounds. The second one is the text-based explanations of the driver reasoning process when estimating pedestrian intents and predicting their behaviors during the interaction period.
MuMiN is a misinformation graph dataset containing rich social media data (tweets, replies, users, images, articles, hashtags), spanning 21 million tweets belonging to 26 thousand Twitter threads, each of which have been semantically linked to 13 thousand fact-checked claims across dozens of topics, events and domains, in 41 different languages, spanning more than a decade.
ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches
The semantic line (SEL) dataset contains 1,750 outdoor images in total, which are split into 1,575 training and 175 testing images. Each semantic line is annotated by the coordinates of the two end-points on an image boundary. If an image has a single dominant line, it is set as the ground truth primary semantic line. If an image has multiple semantic lines, the line with the best rank by human annotators is set as the ground-truth primary line, and the others as additional ground-truth semantic lines. In SEL, 61% of the images contain multiple semantic lines.
A database of over 1.4 billion 3x3 convolution filters extracted from hundreds of diverse CNN models with relevant meta information.
CLEVR-X is a dataset that extends the CLEVR dataset with natural language explanations in the context of VQA. It consists of 3.6 million natural language explanations for 850k question-image pairs.
A large scale Chinese multi-modal dialogue corpus (120.84K dialogues and 198.82 K images). MMCHAT contains image-grounded dialogues collected from real conversations on social media. We manually annotate 100K dialogues from MMCHAT with the dialogue quality and whether the dialogues are related to the given image. We provide the rule-filtered raw dialogues that are used to create MMChat (Rule Filtered Raw MMChat). It contains 4.257 M dialogue sessions and 4.874 M images We provide a version of MMChat that is filtered based on LCCC (LCCC Filtered MMChat). This version contain much cleaner dialogues (492.6 K dialogue sessions and 1.066 M images)
HowMany-Qa is a object counting dataset. It is taken from the counting-specific union of VQA 2.0 (Goyal et al., 2017) and Visual Genome QA (Krishna et al., 2016).
PGDP5K is a dataset consisting of 5000 diagram samples composed of 16 shapes, covering 5 positional relations, 22 symbol types and 6 text types, labeled with more fine-grained annotations at primitive level, including primitive classes, locations and relationships, where 1,813 non-duplicated images are selected from the Geometry3K dataset and other 3,187 images are collected from three popular textbooks across grades 6-12 on mathematics curriculum websites by taking screenshots from PDF books.
This data collection consists of images acquired during chemoradiotherapy of 20 locally-advanced, non-small cell lung cancer patients. The images include four-dimensional (4D) fan beam (4D-FBCT) and 4D cone beam CT (4D-CBCT). All patients underwent concurrent radiochemotherapy to a total dose of 64.8-70 Gy using daily 1.8 or 2 Gy fractions. scription of the dataset.
Contains 8k flickr Images with captions. Visit this page to explore the data.
This dataset is collected via the WinoGAViL game to collect challenging vision-and-language associations. Inspired by the popular card game Codenames, a “spymaster” gives a textual cue related to several visual candidates, and another player has to identify them.
The current OOD benchmark VQA-CP v2 only considers one type of shortcut (from question type to answer) and thus still cannot guarantee that the modelrelies on the intended solution rather than a solution specific to this shortcut. To overcome this limitation, VQA-VS proposes a new dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets. In addition, VQA-VS overcomes three troubling practices in the use of VQA-CP v2, e.g., selecting models using OOD test sets, and further standardize OOD evaluation procedure. VQA-VS provides a more rigorous and comprehensive testbed for shortcut learning in VQA.
The Industrial Metal Objects dataset is a diverse dataset of industrial metal objects. These objects are symmetric, textureless and highly reflective, leading to challenging conditions not captured in existing datasets. The dataset contains both real-world and synthetic multi-view RGB images with 6D object pose labels.
The dataset uses VGG-Sound which consists of 10s clips collected from YouTube for 309 sound classes. A subset of ‘temporally sparse’ classes is selected using the following procedure: 5–15 videos are randomly picked from each of the 309 VGGSound classes, and manually annotated as to whether audio-visual cues are only sparsely available. As a result, 12 classes are selected (∼4 %) or 6.5k and 0.6k videos in the train and test sets, respectively. The classes include 'dog barking', 'chopping wood', 'lion roaring', 'skateboarding' etc.