mmrazor
mmselfsup
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| mmrazor | mmselfsup | |
|---|---|---|
| 4 | 5 | |
| 1,365 | 3,084 | |
| 3.8% | 1.8% | |
| 2.8 | 5.3 | |
| 17 days ago | 10 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
mmrazor
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMRazor: OpenMMLab model compression toolbox and benchmark.
- Still worrying about model compression? MMRazor may work for you.
- Still worrying about model compression? MMRazor is all you need
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[P] 4.5 times faster Hugging Face transformer inference by modifying some Python AST
https://github.com/open-mmlab/mmrazor ,it may work for you~
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?
transformer-deploy - Efficient, scalable and enterprise-grade CPU/GPU inference server for 🤗 Hugging Face transformer models 🚀
Unsupervised-Semantic-Segmentation - Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals. [ICCV 2021]
neural-compressor - SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
mmaction2 - OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark
calibrated-backprojection-network - PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)
Pointnet_Pointnet2_pytorch - PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
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.
PaddleViT - :robot: PaddleViT: State-of-the-art Visual Transformer and MLP Models for PaddlePaddle 2.0+
barlowtwins - Implementation of Barlow Twins paper
ttach - Image Test Time Augmentation with PyTorch!
Revisiting-Contrastive-SSL - Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations. [NeurIPS 2021]