mmselfsup
mmflow
mmselfsup | mmflow | |
---|---|---|
5 | 5 | |
3,089 | 895 | |
0.8% | 1.7% | |
5.3 | 0.0 | |
11 months ago | 8 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
mmflow
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMFlow: OpenMMLab optical flow toolbox and benchmark.
- A new member of OpenMMLab -MMFlow
- MMFlow: OpenMMLab Optical Flow Toolbox and Benchmark
What are some alternatives?
Unsupervised-Semantic-Segmentation - Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals. [ICCV 2021]
mmaction2 - OpenMMLab's Next Generation Video Understanding 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.
mmfewshot - OpenMMLab FewShot Learning Toolbox and Benchmark
calibrated-backprojection-network - PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)
mmrotate - OpenMMLab Rotated Object Detection Toolbox and Benchmark
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.
mmdeploy - OpenMMLab Model Deployment Framework
barlowtwins - Implementation of Barlow Twins paper
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
Revisiting-Contrastive-SSL - Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations. [NeurIPS 2021]
text2cinemagraph - Text2Cinemagraph: Text-Guided Synthesis of Eulerian Cinemagraphs [SIGGRAPH ASIA 2023]