Unsupervised-Classification
SimMIM
| Unsupervised-Classification | SimMIM | |
|---|---|---|
| 2 | 1 | |
| 1,366 | 935 | |
| - | 2.1% | |
| 1.4 | 0.0 | |
| over 1 year ago | over 2 years ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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Unsupervised-Classification
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Middle ground dataset between CIFAR and ImageNet [D]
The subsets we used are from here: https://github.com/wvangansbeke/Unsupervised-Classification/tree/master/data/imagenet_subsets
- Any reference or idea about how to train unsupervised CNN model ?
SimMIM
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My DL model mis-segment wrong objects with similar colors
It seems the segmentation task is complex. One thing that I can suggest that definitely worked for me is Self-supervised learning prior to training. The task of the sel-supervision is very important. In your case I suggest Masked Image Modeling because it does not rely heavily on shapes and colors but can leverage them if they exist. Check out SimMIM by Microsoft. https://arxiv.org/abs/2111.09886 . https://github.com/microsoft/simmim
What are some alternatives?
simclr - SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners
SparK - [ICLR'23 Spotlightš„] The first successful BERT/MAE-style pretraining on any convolutional network; Pytorch impl. of "Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling"
self-supervised - Whitening for Self-Supervised Representation Learning | Official repository
PaddleClas - A treasure chest for visual classification and recognition powered by PaddlePaddle
Unsupervised-Semantic-Segmentation - Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals. [ICCV 2021]
Swin-Transformer - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
DiffCSE - Code for the NAACL 2022 long paper "DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings"
mmdetection - OpenMMLab Detection Toolbox and Benchmark
contrastive-reconstruction - Tensorflow-keras implementation for Contrastive Reconstruction (ConRec) : a self-supervised learning algorithm that obtains image representations by jointly optimizing a contrastive and a self-reconstruction loss.
mmpretrain - OpenMMLab Pre-training Toolbox and Benchmark
Transformer-SSL - This is an official implementation for "Self-Supervised Learning with Swin Transformers".
mmselfsup - OpenMMLab Self-Supervised Learning Toolbox and Benchmark