27 lines
1.3 KiB
Markdown
27 lines
1.3 KiB
Markdown
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# 量化测试结果
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目前 mmdeploy 支持 ncnn 量化
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## ncnn 量化
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### 分类任务
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|model|dataset|fp32 top-1 (%)|int8 top-1 (%)|
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|[ResNet-18](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb16_cifar10.py)|Cifar10|94.82|94.83|
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|[ResNeXt-32x4d-50](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext50-32x4d_8xb32_in1k.py)|ImageNet-1k|77.90|78.20*|
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|[MobileNet V2](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py)|ImageNet-1k|71.86|71.43*|
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|[HRNet-W18*](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py)|ImageNet-1k|76.75|76.25*|
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备注：
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* 因为 imagenet-1k 数据量很大、ncnn 未正式发布 Vulkan int8 版本，考虑到 CPU 运行时间，仅用部分测试集（4000/50000）
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* 量化后精度会有差异，分类模型涨点 1% 以内是正常情况
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### OCR 检测任务
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|model|dataset|fp32 hmean|int8 hmean|
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|[PANet](https://github.com/open-mmlab/mmocr/blob/main/configs/textdet/panet/panet_r18_fpem_ffm_600e_icdar2015.py)|ICDAR2015|0.795|0.792 @thr=0.9|
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备注：[mmocr](https://github.com/open-mmlab/mmocr) 使用 `shapely` 计算 IoU，实现方法会导致轻微的精度差异
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