# HaloTrouvaille/YOLO-Multi-Backbones-Attention

<https://repo.masters-masters.com/en/repos/HaloTrouvaille/YOLO-Multi-Backbones-Attention>

[Home](https://repo.masters-masters.com/en) › [HaloTrouvaille](https://repo.masters-masters.com/en/owners/HaloTrouvaille) › [YOLO-Multi-Backbones-Attention](https://repo.masters-masters.com/en/repos/HaloTrouvaille/YOLO-Multi-Backbones-Attention)

HaloTrouvaille/YOLO-Multi-Backbones-Attention has 503 stars on GitHub. Its RepoMasters card: Alpha tier. Model Compression—YOLOv3 with multi lightweight…

| Field | Value |
| --- | --- |
| Owner | [HaloTrouvaille](https://repo.masters-masters.com/en/owners/HaloTrouvaille) |
| Stars | 503 |
| Forks | 115 |
| Language | [Python](https://repo.masters-masters.com/en/languages/python) |
| License | None |
| Created | 2019 |
| Topics | huawei-ghostnet, object-detection-datasets, attention-mechanism, yolo, se, cbam, eca, shufflenet |
| Tier | [Alpha (250 to 699 ★)](https://repo.masters-masters.com/en/ranks/b_movie) |
| View on GitHub | [HaloTrouvaille/YOLO-Multi-Backbones-Attention](https://github.com/HaloTrouvaille/YOLO-Multi-Backbones-Attention) |

## Repository

Model Compression—YOLOv3 with multi lightweight backbones(ShuffleNetV2 HuaWei GhostNet), attention, prune and quantization

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

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