mmselfsup
mmpretrain
| mmselfsup | mmpretrain | |
|---|---|---|
| 5 | 2 | |
| 3,212 | 3,527 | |
| 1.3% | 1.4% | |
| 5.3 | 2.9 | |
| over 1 year ago | 3 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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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
mmpretrain
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMClassification: OpenMMLab image classification toolbox and benchmark.
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how to recognize digits from this pics(i have many of them) so kindly suggest generic that can work for other similar images. I have searched alot for the source code on github but not found the correct solution. most of these solutions were incorrect while other were incomplete. Kindly help me :(
MMClassification or TIMM would be good starting points for training a classification model.
What are some alternatives?
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Revisiting-Contrastive-SSL - Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations. [NeurIPS 2021]
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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.
pytorch-image-models - The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
barlowtwins - Implementation of Barlow Twins paper
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