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