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The dataset is labeled with semantic labels for 32 semantic classes. over 700 images Images Object recognition and classification 2008 [60] [61] [62] Gabriel J. Brostow, Jamie Shotton, Julien Fauqueur, Roberto Cipolla RailSem19 RailSem19 is a dataset for understanding scenes for vision systems on railways. The dataset is labeled semanticly and ...
A dataset adopting the FEVER methodology that consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence sentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim totalling in 7,675 claim ...
Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC.Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
Extended MNIST (EMNIST) is a newer dataset developed and released by NIST to be the (final) successor to MNIST. [ 15 ] [ 16 ] MNIST included images only of handwritten digits. EMNIST includes all the images from NIST Special Database 19 (SD 19), which is a large database of 814,255 handwritten uppercase and lower case letters and digits.
In 2021, ImageNet-1k was updated by annotating faces appearing in the 997 non-person categories. They found training models on the dataset with these faces blurred caused minimal loss in performance. [31] ImageNetV2 was a new dataset containing three test sets with 10,000 each, constructed by the same methodology as the original ImageNet. [32]
The purpose of the FID score is to measure the diversity of images created by a generative model with images in a reference dataset. The reference dataset could be ImageNet or COCO-2014. [3] [8] Using a large dataset as a reference is important as the reference image set should represent the full diversity of images which the model attempts to ...
T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. [1] [2] Like the original Transformer model, [3] T5 models are encoder-decoder Transformers, where the encoder processes the input text, and the decoder generates the output text.
Input–output planning was never adopted because the material balance system had become entrenched in the Soviet economy, and input–output planning was shunned for ideological reasons. As a result, the benefits of consistent and detailed planning through input–output analysis were never realized in the Soviet-type economies .