SimMIM
mmselfsup
| SimMIM | mmselfsup | |
|---|---|---|
| 1 | 5 | |
| 860 | 3,089 | |
| 0.0% | 0.8% | |
| 0.0 | 5.3 | |
| over 1 year ago | 11 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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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
mmselfsup
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
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Does anyone know how a loss curve like this can happen? Details in comments
For some reason, the loss goes up shaply right at the start and slowly goes back down. I am self-supervised pretraining an image modeling with DenseCL using mmselfsup (https://github.com/open-mmlab/mmselfsup). This shape happened on the Coco-2017 dataset and my custom dataset. As you can see, it happens consistently for different runs. How could the loss increase so sharply and is it indicative of an issue with the training? The loss peaks before the first epoch is finished. Unfortunately, the library does not support validation.
- Defect Detection using RPI
- [D] State-of-the-Art for Self-Supervised (Pre-)Training of CNN architectures (e.g. ResNet)?
- Rebirth! OpenSelfSup is upgraded to MMSelfSup
What are some alternatives?
Unsupervised-Classification - SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]
Unsupervised-Semantic-Segmentation - Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals. [ICCV 2021]
mmdetection - OpenMMLab Detection Toolbox and Benchmark
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
PaddleClas - A treasure chest for visual classification and recognition powered by PaddlePaddle
calibrated-backprojection-network - PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)
SparK - [ICLR'23 Spotlight🔥] The first successful BERT/MAE-style pretraining on any convolutional network; Pytorch impl. of "Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling"
mmagic - OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox. Unlock the magic 🪄: Generative-AI (AIGC), easy-to-use APIs, awsome model zoo, diffusion models, for text-to-image generation, image/video restoration/enhancement, etc.
Swin-Transformer - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
barlowtwins - Implementation of Barlow Twins paper
Revisiting-Contrastive-SSL - Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations. [NeurIPS 2021]
animessl - Train vision models with vissl + illustrated images