PaddleViT
FastestDet
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| PaddleViT | FastestDet | |
|---|---|---|
| 2 | 1 | |
| 1,169 | 713 | |
| - | - | |
| 0.0 | 0.0 | |
| over 1 year ago | about 1 year ago | |
| Python | Python | |
| Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
PaddleViT
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Researchers From China Propose A Pale-Shaped Self-Attention (PS-Attention) And A General Vision Transformer Backbone, Called Pale Transformer
Code for https://arxiv.org/abs/2112.14000 found: https://github.com/BR-IDL/PaddleViT
Github: https://github.com/BR-IDL/PaddleViT
FastestDet
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FastestDet: a new ultra real-time anchor free target detection algorithm designed for ARM CPU, with only 250K parameters,
The time consumption in the table is measured by ncnn. The test platform is RK3568 ARM CPU. Compared with Yolo-fastest, the time consumption of fastestdet single core is reduced by 50%, and the index of map0.5 is 3.4% higher than Yolo-fastest. In fact, due to the increase of input resolution, the calculation amount of FastestDet is nearly twice that of Yolo-fastest. However, thanks to the minimalist network structure and the reduction of memory access, the actual test time on multiple platforms is greatly reduced, especially on single core or weak performance platforms, and the speed is increased by 50%+
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