- Released a new series of PP-PicoDet models, with greatly improved accuracy and optimized CPU prediction speed. **(2022.03.20)**
- Released a new series of PP-PicoDet models, it was used TAL/Task-aligned-Head and optimized PAN, which improved the accuracy and optimized CPU prediction speed. Moreover the training speed is greatly improved. **(2022.03.20)**
### Legacy Model
- Please refer to: [PicoDet 2021.10版本](./legacy_model/)
## Introduction
## Introduction
We developed a series of lightweight models, named `PP-PicoDet`. Because of the excellent performance, our models are very suitable for deployment on mobile or CPU. For more details, please refer to our [report on arXiv](https://arxiv.org/abs/2111.00902).
We developed a series of lightweight models, named `PP-PicoDet`. Because of the excellent performance, our models are very suitable for deployment on mobile or CPU. For more details, please refer to our [report on arXiv](https://arxiv.org/abs/2111.00902).
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@@ -25,15 +30,15 @@ We developed a series of lightweight models, named `PP-PicoDet`. Because of the
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@@ -25,15 +30,15 @@ We developed a series of lightweight models, named `PP-PicoDet`. Because of the
- PicoDet used 4 or 8 GPUs for training and all checkpoints are trained with default settings and hyperparameters.
- PicoDet used 4 or 8 GPUs for training and all checkpoints are trained with default settings and hyperparameters.
</details>
</details>
## Cite PP-PicoDet
```
@misc{yu2021pppicodet,
title={PP-PicoDet: A Better Real-Time Object Detector on Mobile Devices},
author={Guanghua Yu and Qinyao Chang and Wenyu Lv and Chang Xu and Cheng Cui and Wei Ji and Qingqing Dang and Kaipeng Deng and Guanzhong Wang and Yuning Du and Baohua Lai and Qiwen Liu and Xiaoguang Hu and Dianhai Yu and Yanjun Ma},
