# megvii-research/FQ-ViT

<https://repo.masters-masters.com/en/repos/megvii-research/FQ-ViT>

[Home](https://repo.masters-masters.com/en) › [megvii-research](https://repo.masters-masters.com/en/owners/megvii-research) › [FQ-ViT](https://repo.masters-masters.com/en/repos/megvii-research/FQ-ViT)

megvii-research/FQ-ViT has 363 stars on GitHub. Its RepoMasters card: Alpha tier. \[IJCAI 2022\] FQ-ViT: Post-Training Quantization for Fully Quantized Vision…

| Field | Value |
| --- | --- |
| Owner | [megvii-research](https://repo.masters-masters.com/en/owners/megvii-research) |
| Stars | 363 |
| Forks | 57 |
| Language | [Python](https://repo.masters-masters.com/en/languages/python) |
| License | Apache-2.0 |
| Created | 2021 |
| Topics | vision-transformer, quantization, post-training-quantization, pytorch, imagenet |
| Tier | [Alpha (250 to 699 ★)](https://repo.masters-masters.com/en/ranks/b_movie) |
| View on GitHub | [megvii-research/FQ-ViT](https://github.com/megvii-research/FQ-ViT) |

## Repository

\[IJCAI 2022\] FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

The tier for repos with 250 to 699 ★. Published odds per card pulled: 20%. Rarity: Uncommon.

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Data: GitHub · Illustration drawn by RepoMasters from the repo’s ID and its language color.