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

  • 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