61 lines
2.8 KiB
Markdown
61 lines
2.8 KiB
Markdown
# Version Updates
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------
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## Catalogue
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- [1. v2.3](#1)
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- [2. v2.2](#2)
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<a name='1'></a>
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## 1. v2.3
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- Model Update
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- Add pre-training weights for lightweight models, including detection models and feature models
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- Release PP-LCNet series of models, which are self-developed ones designed to run on CPU
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- Enable SwinTransformer, Twins, and Deit to support direct training from scrach to achieve thesis accuracy.
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- Basic framework capabilities
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- Add DeepHash module, which supports feature model to directly export binary features
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- Add PKSampler, which tackles the problem that feature models cannot be trained by multiple machines and cards
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- Support PaddleSlim: support quantization, pruning training, and offline quantization of classification models and feature models
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- Enable legendary models to support intermediate model output
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- Support multi-label classification training
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- Inference Deployment
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- Replace the original feature retrieval library with Faiss to improve platform adaptability
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- Support PaddleServing: support the deployment of classification models and image recognition process
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- Versions of the Recommendation Library
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- python: 3.7
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- PaddlePaddle: 2.1.3
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- PaddleSlim: 2.2.0
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- PaddleServing: 0.6.1
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<a name='2'></a>
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## 2. v2.2
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- Model Updates
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- Add models including LeViT, Twins, TNT, DLA, HardNet, RedNet, and SwinTransfomer
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- Basic framework capabilities
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- Divide the classification models into two categories
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- legendary models: introduce TheseusLayer base class, add the interface to modify the network function, and support the networking data truncation and output
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- model zoo: other common classification models
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- Add the support of Metric Learning algorithm
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- Add a variety of related loss algorithms, and the basic network module gears (allow the combination with backbone and loss) for convenient use
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- Support both the general classification and metric learning-related training
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- Support static graph training
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- Classification training with dali acceleration supported
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- Support fp16 training
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- Application Updates
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- Add specific application cases and related models of product recognition, vehicle recognition (vehicle fine-grained classification, vehicle ReID), logo recognition, animation character recognition
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- Add a complete pipeline for image recognition, including detection module, feature extraction module, and vector search module
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- Inference Deployment
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- Add Mobius, Baidu's self-developed vector search module, to support the inference deployment of the image recognition system
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- Image recognition, build feature library that allows batch_size>1
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- Documents Update
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- Add image recognition related documents
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- Fix bugs in previous documents
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- Versions of the Recommendation Library
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- python: 3.7
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- PaddlePaddle: 2.1.2
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