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<img src="https://user-images.githubusercontent.com/58739961/187154444-fce76639-ac8d-429b-9354-c6fac64b7ef8.jpg" width="600"/> <img src="https://user-images.githubusercontent.com/58739961/187154444-fce76639-ac8d-429b-9354-c6fac64b7ef8.jpg" height="100"/>
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<b><font size="5">OpenMMLab website</font></b> <b><font size="5">OpenMMLab website</font></b>
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## Introduction ## Introduction
MMEngine is a fundational library for training deep learning models based on PyTorch. It can runs on Linux, Windows, and MacOS. MMEngine is a foundational library for training deep learning models based on PyTorch. It provides a solid engineering foundation and frees developers from writing redundant codes on workflows. It serves as the training engine of all OpenMMLab codebases, which support hundreds of algorithms on various research areas. Importantly, MMEngine is also generic to be applied to non-OpenMMLab projects.
Major features: Major features:
1. A general and powerful runner: 1. **Universal and powerful runner**.
- Users can train different models with several lines of code, e.g., training ImageNet in 80 lines (in comparison with PyTorch example that need more than 400 lines). - Fewer code, e.g., train ImageNet with 1/5 lines of code compared with PyTorch example.
- Can train models in popular libraries like TIMM, TorchVision, and Detectron2. - Compatible with popular libraries like OpenMMLab, TorchVision, timm and Detectron2.
2. An open framework with unified interfaces: 2. **Open architecture with unified interfaces**.
- Users can do one thing to all OpenMMLab 2.x projects with the same code. For example, MMRazor 1.x can compress models in all OpenMMLab 2.x projects with 40% of the code reduced from MMRazor 0.x. - Handle different algorithm tasks with unified API, e.g., implement a method and apply it to all compatible models.
- Simplify the support of up/down-streams. Currently, MMEngine can run on Nvidia CUDA, Mac MPS, AMD, MLU, and other devices. - Support different devices and hardwares with unified API, including CPU, GPU, IPU, Apple silicon, etc.
3. A `legoified` training process: 3. **Customizable training process**.
- Dynamical training, optimization, and data augmentation strategies like Early stopping - Define the training process like playing with Legos. Rich components and strategies are available.
- Arbitrary forms of model weight averaging including Exponential Momentum Average (EMA) and Stochastic Weight Averaging (SWA) - Complete control of training with different level of APIs.
- Visualize and log whatever you want
- Fine-grained optimization strategies of each parameter groups
- Flexible control of mixed precision training
## Installation ## Installation
Before installing MMEngine, please make sure that PyTorch has been successfully installed following the [official guide](https://pytorch.org/get-started/locally/). Before installing MMEngine, please ensure that PyTorch has been successfully installed following the [official guide](https://pytorch.org/get-started/locally/).
Install MMEngine Install MMEngine