This project is a computer vision benchmark and image classification dataset used to measure and compare the accuracy of machine learning models. It provides a standardized collection of labeled fashion product images and training data formatted to be compatible with the MNIST dataset structure. The dataset consists of fixed-dimension grayscale images and label-based category mappings, stored in a binary format. It includes pre-split training and testing sets and a static distribution to ensure consistent cross-model benchmarking. The repository supports image classification benchmarking and
Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018
CVPR2018: Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatio-temporal Patterns
Code for the ECCV 2018 paper "Pairwise Confusion for Fine-Grained Visual Classification"
Four landmark detection algorithms, implemented in PyTorch.
The main features of d-x-y/san are: Computer Vision Research.
Open-source alternatives to d-x-y/san include: zalandoresearch/fashion-mnist — This project is a computer vision benchmark and image classification dataset used to measure and compare the accuracy… agrimgupta92/sgan — Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018. ahangchen/tfusion — CVPR2018: Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatio-temporal Patterns. aimerykong/recurrent-pixel-embedding-for-instance-grouping — CVPR2018 - pixel embedding & grouping for structured prediction, e.g., instance segmentation. akanazawa/cmr — Angjoo Kanazawa \ , Shubham Tulsiani \ , Alexei A. Efros, Jitendra Malik. abhimanyudubey/confusion — Code for the ECCV 2018 paper "Pairwise Confusion for Fine-Grained Visual Classification".