Files
FastDeploy/examples/vision/detection/yolov5
yunyaoXYY 58d63f3e90 [Other] Add detection, segmentation and OCR examples for Ascend deploy. (#983)
* Add Huawei Ascend NPU deploy through PaddleLite CANN

* Add NNAdapter interface for paddlelite

* Modify Huawei Ascend Cmake

* Update way for compiling Huawei Ascend NPU deployment

* remove UseLiteBackend in UseCANN

* Support compile python whlee

* Change names of nnadapter API

* Add nnadapter pybind and remove useless API

* Support Python deployment on Huawei Ascend NPU

* Add models suppor for ascend

* Add PPOCR rec reszie for ascend

* fix conflict for ascend

* Rename CANN to Ascend

* Rename CANN to Ascend

* Improve ascend

* fix ascend bug

* improve ascend docs

* improve ascend docs

* improve ascend docs

* Improve Ascend

* Improve Ascend

* Move ascend python demo

* Imporve ascend

* Improve ascend

* Improve ascend

* Improve ascend

* Improve ascend

* Imporve ascend

* Imporve ascend

* Improve ascend

* acc eval script

* acc eval

* remove acc_eval from branch huawei

* Add detection and segmentation examples for Ascend deployment

* Add detection and segmentation examples for Ascend deployment

* Add PPOCR example for ascend deploy

* Imporve paddle lite compiliation

* Add FlyCV doc

* Add FlyCV doc

* Add FlyCV doc

* Imporve Ascend docs

* Imporve Ascend docs
2023-01-04 10:01:23 +08:00
..
2022-12-28 10:46:55 +08:00

YOLOv5准备部署模型

  • YOLOv5 v7.0部署模型实现来自YOLOv5,和基于COCO的预训练模型
    • 1官方库提供的*.onnx可直接进行部署
    • 2开发者基于自己数据训练的YOLOv5 v7.0模型,可使用YOLOv5中的export.py导出ONNX文件后完成部署。

下载预训练ONNX模型

为了方便开发者的测试下面提供了YOLOv5导出的各系列模型开发者可直接下载使用。下表中模型的精度来源于源官方库

模型 大小 精度
YOLOv5n 7.6MB 28.0%
YOLOv5s 28MB 37.4%
YOLOv5m 82MB 45.4%
YOLOv5l 178MB 49.0%
YOLOv5x 332MB 50.7%

详细部署文档

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