PaddleClas is an image classification and image recognition toolset for industry and academia, helping users train better computer vision models and apply them in real scenarios.
- 🔥️ Release [PP-ShiTuV2](./docs/en/PPShiTu/PPShiTuV2_introduction.md), recall1 is improved by nearly 8 points, covering 20+ recognition scenarios, with [index management tool](./deploy/shitu_index_manager) and [Android Demo](./docs/en/quick_start/quick_start_recognition_en.md) for better experience.
- 2022.6.15 Release [**P**ractical **U**ltra **L**ight-weight image **C**lassification solutions](./docs/en/PULC/PULC_quickstart_en.md). PULC models inference within 3ms on CPU devices, with accuracy on par with SwinTransformer. We also release 9 practical classification models covering pedestrian, vehicle and OCR scenario.
- 2022.4.21 Added the related [code](https://github.com/PaddlePaddle/PaddleClas/pull/1820/files) of the CVPR2022 oral paper [MixFormer](https://arxiv.org/pdf/2204.02557.pdf).
For the introduction of PP-LCNet, please refer to [paper](https://arxiv.org/pdf/2109.15099.pdf) or [PP-LCNet model introduction](docs/en/models/PP-LCNet_en.md). The metrics and pretrained model are available [here](docs/en/algorithm_introduction/ImageNet_models_en.md).
- 2021.06.29 Add [Swin-transformer](docs/en/models/SwinTransformer_en.md)) series model，Highest top1 acc on ImageNet1k dataset reaches 87.2%, training, evaluation and inference are all supported. Pretrained models can be downloaded [here](docs/en/algorithm_introduction/ImageNet_models_en.md#16).
- 2021.06.16 PaddleClas release/2.2. Add metric learning and vector search modules. Add product recognition, animation character recognition, vehicle recognition and logo recognition. Added 30 pretrained models of LeViT, Twins, TNT, DLA, HarDNet, and RedNet, and the accuracy is roughly the same as that of the paper.
PaddleClas release PP-HGNet、PP-LCNetv2、 PP-LCNet and **S**imple **S**emi-supervised **L**abel **D**istillation algorithms, and support plenty of image classification and image recognition algorithms.Based on th algorithms above, PaddleClas release PP-ShiTu image recognition system and [**P**ractical **U**ltra **L**ight-weight image **C**lassification solutions](docs/en/PULC/PULC_quickstart_en.md).
* You can also scan the QR code below to join the PaddleClas QQ group and WeChat group (add and replay "C") to get more efficient answers to your questions and to communicate with developers from all walks of life. We look forward to hearing from you.
PULC solutions consists of PP-LCNet light-weight backbone, SSLD pretrained models, Ensemble of Data Augmentation strategy and SKL-UGI knowledge distillation.
PULC models inference within 3ms on CPU devices, with accuracy comparable with SwinTransformer. We also release 9 practical models covering pedestrian, vehicle and OCR.
PP-ShiTuV2 is a practical lightweight general image recognition system, which is mainly composed of three modules: mainbody detection model, feature extraction model and vector search tool. The system adopts a variety of strategies including backbone network, loss function, data augmentations, optimal hyperparameters, pre-training model, model pruning and quantization. Compared to V1, PP-ShiTuV2, Recall1 is improved by nearly 8 points. For more details, please refer to [PP-ShiTuV2 introduction](./docs/en/PPShiTu/PPShiTuV2_introduction.md).
For a new unknown category, there is no need to retrain the model, just prepare images of new category, extract features and update retrieval database and the category can be recognised.
