Code and Datasets

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Codebases

MMDetection

MMDetection is an open source object detection toolbox that supports popular and contemporary detection frameworks, e.g., Faster RCNN, Mask RCNN, and RetinaNet. Easy-to-extend and highly efficient.

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MMDetection3D

MMDetection3D supports multi-modality/single-modality 3D detectors out of box. It directly supports popular indoor and outdoor 3D detection datasets, including ScanNet, SUNRGB-D, Waymo, nuScenes, Lyft, and KITTI.

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MMEditing

MMEditing supports popular and contemporary methods for image inpainting, matting, and super-resolution. Check out popular super-resolution methods such as SRCNN, EDVR, and ESRGAN.

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OpenSelfSup

OpenSelfSup supports popular and contemporary self-supervised learning methods such as MoCo, MoCo v2, SimCLR, ODC, and BYOL.

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Zero-DCE

Zero-Reference Deep Curve Estimation (Zero-DCE) formulates light enhancement as a task of image-specific curve estimation with a deep network. The method generalizes well to diverse lighting conditions.

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TSIT

TSIT provides a simple and versatile framework for image-to-image translation. It facilitates tasks such as style transfer and semantic image systhesis.

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OpenMMLab

As an open source project for academic research and industrial applications, OpenMMLab covers a wide range of libraries to facilitate research on various computer vision topics, e.g., classification, detection, segmentation and super-resolution. Join the OpenMMLab developer community to contribute, learn, and get your questions answered.
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Featured

Datasets

DeeperForensics

DeeperForensics is a large-scale face forgery detection dataset with 60, 000 videos constituted by a total of 17.6 million frames. Extensive perturbations are applied to obtain a more challenging benchmark of larger scale and higher diversity. All source videos in DeeperForensics are carefully collected, and fake videos are generated by a newly proposed end-to-end face swapping framework.

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ForgeryNet

The dataset contains 2.9 million images and 221,247 videos for the research of anti-deepfake. Manipulations are achieved using seven image-level approaches and eight video-level approaches. For the research on forgery detection.

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ATD-12K

ATD-12K is a large-scale dataset that facilitates the training and evaluation of animation video interpolation methods. It contains 10,000 animation frame triplets and a test set of 2,000 triplets, collected from a variety of animation movies.

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MessyTable

A challenging dataset that features a large number of scenes with messy tables captured from multiple camera views. Each scene in this dataset is highly complex, containing multiple object instances that could be identical, stacked and occluded by other instances. The key challenge is to associate all instances given the RGB image of all views. Over 50K images with 1.2M bounding box annotations.

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MEAD

Multi-view Emotional Audio-visual Dataset (MEAD) is a talking-face video dataset featuring 60 actors and actresses talking with eight different emotions at three different intensity levels. High-quality audio-visual clips are captured at seven different view angles in a strictly-controlled environment.

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Webly-Reference Super-Resolution

Webly-Reference SR dataset is a test dataset for evaluating reference-based super-resolution approaches. The dataset covers diverse categories including outdoor scenes, indoor scenes, buildings, famous landmarks, animals and plants.

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Under-Display Camera Images

Synthetic and real images for the research on under-display camera restoration. UDC systems introduce a new class of complex image degradation problems, combining flare, haze, blur, and noise.

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Multi-View Partial (MVP) Point Cloud Dataset

The dataset contains over 100,000 high-quality scans, obtained by rendering partial 3D shapes from 26 uniformly distributed camera poses for each 3D CAD model. For research on point cloud completion.

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