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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.
PULC: Practical Ultra Light-weight image Classification solutions
🔥️ Release PP-ShiTuV2, recall1 is improved by nearly 8 points, covering 20+ recognition scenarios, with index management tool and Android Demo for better experience.
2022.6.15 Release Practical Ultra Light-weight image Classification solutions. 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 of the CVPR2022 oral paper MixFormer.
2021.09.17 Add PP-LCNet series model developed by PaddleClas, these models show strong competitiveness on Intel CPUs. For the introduction of PP-LCNet, please refer to paper or PP-LCNet model introduction. The metrics and pretrained model are available here.
2021.06.29 Add Swin-transformer) series model，Highest top1 acc on ImageNet1k dataset reaches 87.2%, training, evaluation and inference are all supported. Pretrained models can be downloaded here.
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 Simple Semi-supervised Label Distillation algorithms, and support plenty of image classification and image recognition algorithms.Based on th algorithms above, PaddleClas release PP-ShiTu image recognition system and Practical Ultra Light-weight image Classification solutions.
Quick experience of PP-ShiTu image recognition system：Link
PP-ShiTuV2 Android Demo
Quick experience of Practical Ultra Light-weight image Classification models：Link
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. 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.
PaddleClas is released under the Apache 2.0 license Apache 2.0 license
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