Back to prasannapulakurthi/foreground-background-augmentation
Projects sharing features with Foreground Background Augmentation
24 open-source projects similar to prasannapulakurthi/foreground-background-augmentation, ranked by shared indexed features. Tags may describe platforms or build tools rather than the same primary purpose. Check each project’s use case, license, and deployment requirements before treating it as a replacement.
- prasannapulakurthi/spm
PrasannaPulakurthi/SPM
Shuffle PatchMix (SPM) for Source-Free Domain Adaptation (ICIP 2025); patch-shuffle augmentation confidence-margin pseudo-labels. New SOTA on PACS (+7.3%), strong results on DomainNet-126 and VisDA-C.
Python - vcl-iisc/conmix
vcl-iisc/CoNMix
Source free Single and Multi target Unsupervised Domain Adaptation
Python - kaiyangzhou/deep-person-reid
KaiyangZhou/deep-person-reid
4,849View on GitHubThis project is a PyTorch person re-identification framework designed for training and evaluating models that identify individuals across different camera views. It provides a complete model training pipeline, a deep learning feature extractor for converting images into numeric vectors, and a suite of computer vision benchmarking tools to measure identity retrieval accuracy. The framework includes a specialized transfer learning toolkit that supports layer freezing, staged learning rate optimization, and differential learning rates for fine-tuning pretrained models. It distinguishes itself th
Pythoncomputer-visioncross-domaindeep-learning - thuml/transfer-learning-library
thuml/Transfer-Learning-Library
3,917View on GitHubThis project is a comprehensive library for transfer learning and domain adaptation in computer vision. It serves as a framework for aligning feature distributions between source and target datasets, a toolkit for domain generalization, and a library for semi-supervised learning using small labeled datasets and large unlabeled sets. The library provides specialized capabilities for unsupervised domain adaptation, including the use of adversarial networks, discrepancy-based architectures, and image-to-image translation to reduce distribution mismatch. It also includes tools for domain generali
Python
