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6 changes: 3 additions & 3 deletions README.md
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87. [ftp://ftp.limsi.fr/pub/quenot/opflow/testdata/piv/](ftp://ftp.limsi.fr/pub/quenot/opflow/testdata/piv/) - Real and synthetic image sequences used for testing a Particle Image Velocimetry application. These images may be used for the test of optical flow and image matching algorithms. (Formats: pgm (raw))
88. [LIMSI-CNRS/CHM/IMM/vision](http://www.limsi.fr/Recherche/IMM/PageIMM.html)
89. [LIMSI-CNRS](http://www.limsi.fr/)
90. [Photometric 3D Surface Texture Database](http://www.taurusstudio.net/research/pmtexdb/index.htm) - This is the first 3D texture database which provides both full real surface rotations and registered photometric stereo data (30 textures, 1680 images). (Formats: TIFF)
90. [Photometric 3D Surface Texture Database](http://www.taurusstudio.net/research/pmtexdb/index.htm) - This is the first 3D texture database which provides both full real surface rotations and registered photometric stereo data (30 textures, 1,680 images). (Formats: TIFF)
91. [SEQUENCES FOR OPTICAL FLOW ANALYSIS (SOFA)](http://www.cee.hw.ac.uk/~mtc/sofa) - 9 synthetic sequences designed for testing motion analysis applications, including full ground truth of motion and camera parameters. (Formats: gif)
92. [Computer Vision Group](http://www.cee.hw.ac.uk/~mtc/research.html)
94. [Sequences for Flow Based Reconstruction](http://www.nada.kth.se/~zucch/CAMERA/PUB/seq.html) - synthetic sequence for testing structure from motion algorithms (Formats: pgm)
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138. [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) - MNIST like fashion product dataset consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.
139. [Large-scale Fashion (DeepFashion) Database](http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html) - Contains over 800,000 diverse fashion images. Each image in this dataset is labeled with 50 categories, 1,000 descriptive attributes, bounding box and clothing landmarks
140. [FakeNewsCorpus](https://github.com/several27/FakeNewsCorpus) - Contains about 10 million news articles classified using [opensources.co](http://opensources.co) types
141. [LLVIP](https://github.com/bupt-ai-cz/LLVIP) - 15488 visible-infrared paired images (30976 images) for low-light vision research, [Project_Page](https://bupt-ai-cz.github.io/LLVIP/)
141. [LLVIP](https://github.com/bupt-ai-cz/LLVIP) - 15,488 visible-infrared paired images (30,976 images) for low-light vision research, [Project_Page](https://bupt-ai-cz.github.io/LLVIP/)
142. [MSDA](https://github.com/bupt-ai-cz/Meta-SelfLearning) - Over over 5 million images from 5 different domains for multi-source ocr/text recognition DA research, [Project_Page](https://bupt-ai-cz.github.io/Meta-SelfLearning/)
143. [SANAD: Single-Label Arabic News Articles Dataset for Automatic Text Categorization](https://data.mendeley.com/datasets/57zpx667y9/2) - SANAD Dataset is a large collection of Arabic news articles that can be used in different Arabic NLP tasks such as Text Classification and Word Embedding. The articles were collected using Python scripts written specifically for three popular news websites: AlKhaleej, AlArabiya and Akhbarona.
144. [Referit3D](https://referit3d.github.io) - Two large-scale and complementary visio-linguistic datasets (aka Nr3D and Sr3D) for identifying fine-grained 3D objects in ScanNet scenes. Nr3D contains 41.5K natural, free-form utterances, and Sr3d contains 83.5K template-based utterances.
144. [Referit3D](https://referit3d.github.io) - Two large-scale and complementary visio-linguistic datasets (aka Nr3D and Sr3D) for identifying fine-grained 3D objects in ScanNet scenes. Nr3D contains 41.5K natural, free-form utterances, and Sr3d contains 83,5K template-based utterances.
145. [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) - Stanford released ~100,000 English QA pairs and ~50,000 unanswerable questions
146. [FQuAD](https://fquad.illuin.tech/) - ~25,000 French QA pairs released by Illuin Technology
147. [GermanQuAD and GermanDPR](https://www.deepset.ai/germanquad) - deepset released ~14,000 German QA pairs
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