mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-14 12:55:06 +08:00
Merge branch 'develop' of https://github.com/PaddlePaddle/FastDeploy into huawei
This commit is contained in:
yunyaoXYY
@@ -23,19 +23,18 @@
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## 下载预训练ONNX模型
|
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|
||||
为了方便开发者的测试，下面提供了ScaledYOLOv4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）
|
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| 模型 | 大小 | 精度 |
|
||||
|:---------------------------------------------------------------- |:----- |:----- |
|
||||
| [ScaledYOLOv4-P5-896](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p5-896.onnx) | 271MB | 51.2% |
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| [ScaledYOLOv4-P5+BoF-896](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p5_-896.onnx) | 271MB | 51.7% |
|
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| [ScaledYOLOv4-P6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p6-1280.onnx) | 487MB | 53.9% |
|
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| [ScaledYOLOv4-P6+BoF-1280](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p6_-1280.onnx) | 487MB | 54.4% |
|
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| [ScaledYOLOv4-P7-1536](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p7-1536.onnx) | 1.1GB | 55.0% |
|
||||
| [ScaledYOLOv4-P5](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p5.onnx) | 271MB | - |
|
||||
| [ScaledYOLOv4-P5+BoF](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p5_.onnx) | 271MB | -|
|
||||
| [ScaledYOLOv4-P6](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p6.onnx) | 487MB | - |
|
||||
| [ScaledYOLOv4-P6+BoF](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p6_.onnx) | 487MB | - |
|
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| [ScaledYOLOv4-P7](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p7.onnx) | 1.1GB | - |
|
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|
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| 模型 | 大小 | 精度 | 备注 |
|
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|:---------------------------------------------------------------- |:----- |:----- |:----- |
|
||||
| [ScaledYOLOv4-P5-896](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p5-896.onnx) | 271MB | 51.2% | 此模型文件来源于[ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)，GPL-3.0 License |
|
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| [ScaledYOLOv4-P5+BoF-896](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p5_-896.onnx) | 271MB | 51.7% | 此模型文件来源于[ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)，GPL-3.0 License |
|
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| [ScaledYOLOv4-P6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p6-1280.onnx) | 487MB | 53.9% | 此模型文件来源于[ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)，GPL-3.0 License |
|
||||
| [ScaledYOLOv4-P6+BoF-1280](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p6_-1280.onnx) | 487MB | 54.4% | 此模型文件来源于[ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)，GPL-3.0 License |
|
||||
| [ScaledYOLOv4-P7-1536](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p7-1536.onnx) | 1.1GB | 55.0% | 此模型文件来源于[ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)，GPL-3.0 License |
|
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| [ScaledYOLOv4-P5](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p5.onnx) | 271MB | - | 此模型文件来源于[ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)，GPL-3.0 License |
|
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| [ScaledYOLOv4-P5+BoF](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p5_.onnx) | 271MB | -| 此模型文件来源于[ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)，GPL-3.0 License |
|
||||
| [ScaledYOLOv4-P6](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p6.onnx) | 487MB | - | 此模型文件来源于[ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)，GPL-3.0 License |
|
||||
| [ScaledYOLOv4-P6+BoF](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p6_.onnx) | 487MB | - | 此模型文件来源于[ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)，GPL-3.0 License |
|
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| [ScaledYOLOv4-P7](https://bj.bcebos.com/paddlehub/fastdeploy/scaled_yolov4-p7.onnx) | 1.1GB | - | 此模型文件来源于[ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)，GPL-3.0 License |
|
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|
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|
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## 详细部署文档
|
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|
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@@ -22,19 +22,18 @@
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## 下载预训练ONNX模型
|
||||
|
||||
为了方便开发者的测试，下面提供了YOLOR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 |
|
||||
|:---------------------------------------------------------------- |:----- |:----- |
|
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| [YOLOR-P6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-p6-paper-541-1280-1280.onnx) | 143MB | 54.1% |
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| [YOLOR-W6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-w6-paper-555-1280-1280.onnx) | 305MB | 55.5% |
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| [YOLOR-E6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-e6-paper-564-1280-1280.onnx ) | 443MB | 56.4% |
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| [YOLOR-D6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-d6-paper-570-1280-1280.onnx) | 580MB | 57.0% |
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| [YOLOR-D6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-d6-paper-573-1280-1280.onnx) | 580MB | 57.3% |
|
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| [YOLOR-P6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-p6-paper-541-640-640.onnx) | 143MB | - |
|
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| [YOLOR-W6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-w6-paper-555-640-640.onnx) | 305MB | - |
|
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| [YOLOR-E6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-e6-paper-564-640-640.onnx ) | 443MB | - |
|
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| [YOLOR-D6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-d6-paper-570-640-640.onnx) | 580MB | - |
|
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| [YOLOR-D6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-d6-paper-573-640-640.onnx) | 580MB | - |
|
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|
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| 模型 | 大小 | 精度 | 备注 |
|
||||
|:---------------------------------------------------------------- |:----- |:----- |:----- |
|
||||
| [YOLOR-P6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-p6-paper-541-1280-1280.onnx) | 143MB | 54.1% | 此模型文件来源于[YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
|
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| [YOLOR-W6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-w6-paper-555-1280-1280.onnx) | 305MB | 55.5% | 此模型文件来源于[YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
|
||||
| [YOLOR-E6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-e6-paper-564-1280-1280.onnx ) | 443MB | 56.4% | 此模型文件来源于[YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
|
||||
| [YOLOR-D6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-d6-paper-570-1280-1280.onnx) | 580MB | 57.0% | 此模型文件来源于[YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
|
||||
| [YOLOR-D6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-d6-paper-573-1280-1280.onnx) | 580MB | 57.3% | 此模型文件来源于[YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
|
||||
| [YOLOR-P6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-p6-paper-541-640-640.onnx) | 143MB | - | 此模型文件来源于[YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
|
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| [YOLOR-W6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-w6-paper-555-640-640.onnx) | 305MB | - | 此模型文件来源于[YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
|
||||
| [YOLOR-E6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-e6-paper-564-640-640.onnx ) | 443MB | - | 此模型文件来源于[YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
|
||||
| [YOLOR-D6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-d6-paper-570-640-640.onnx) | 580MB | - | 此模型文件来源于[YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
|
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| [YOLOR-D6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-d6-paper-573-640-640.onnx) | 580MB | - | 此模型文件来源于[YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
|
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|
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|
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## 详细部署文档
|
||||
|
||||
@@ -8,13 +8,13 @@
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||||
## 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](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5n.onnx) | 7.6MB | 28.0% |
|
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| [YOLOv5s](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx) | 28MB | 37.4% |
|
||||
| [YOLOv5m](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5m.onnx) | 82MB | 45.4% |
|
||||
| [YOLOv5l](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5l.onnx) | 178MB | 49.0% |
|
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| [YOLOv5x](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5x.onnx) | 332MB | 50.7% |
|
||||
| 模型 | 大小 | 精度 | 备注 |
|
||||
|:---------------------------------------------------------------- |:----- |:----- |:---- |
|
||||
| [YOLOv5n](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5n.onnx) | 7.6MB | 28.0% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5)，GPL-3.0 License |
|
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| [YOLOv5s](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx) | 28MB | 37.4% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5)，GPL-3.0 License |
|
||||
| [YOLOv5m](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5m.onnx) | 82MB | 45.4% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5)，GPL-3.0 License |
|
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| [YOLOv5l](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5l.onnx) | 178MB | 49.0% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5)，GPL-3.0 License |
|
||||
| [YOLOv5x](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5x.onnx) | 332MB | 50.7% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5)，GPL-3.0 License |
|
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|
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|
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## 详细部署文档
|
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|
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@@ -52,12 +52,12 @@
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## 下载预训练ONNX模型
|
||||
|
||||
为了方便开发者的测试，下面提供了YOLOv5Lite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 |
|
||||
|:---------------------------------------------------------------- |:----- |:----- |
|
||||
| [YOLOv5Lite-e](https://bj.bcebos.com/paddlehub/fastdeploy/v5Lite-e-sim-320.onnx) | 3.1MB | 35.1% |
|
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| [YOLOv5Lite-s](https://bj.bcebos.com/paddlehub/fastdeploy/v5Lite-s-sim-416.onnx) | 6.3MB | 42.0% |
|
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| [YOLOv5Lite-c](https://bj.bcebos.com/paddlehub/fastdeploy/v5Lite-c-sim-512.onnx) | 18MB | 50.9% |
|
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| [YOLOv5Lite-g](https://bj.bcebos.com/paddlehub/fastdeploy/v5Lite-g-sim-640.onnx) | 21MB | 57.6% |
|
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| 模型 | 大小 | 精度 | 备注 |
|
||||
|:---------------------------------------------------------------- |:----- |:----- |:----- |
|
||||
| [YOLOv5Lite-e](https://bj.bcebos.com/paddlehub/fastdeploy/v5Lite-e-sim-320.onnx) | 3.1MB | 35.1% | 此模型文件来源于[YOLOv5-Lite](https://github.com/ppogg/YOLOv5-Lite)，GPL-3.0 License ｜
|
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| [YOLOv5Lite-s](https://bj.bcebos.com/paddlehub/fastdeploy/v5Lite-s-sim-416.onnx) | 6.3MB | 42.0% | 此模型文件来源于[YOLOv5-Lite](https://github.com/ppogg/YOLOv5-Lite)，GPL-3.0 License |
|
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| [YOLOv5Lite-c](https://bj.bcebos.com/paddlehub/fastdeploy/v5Lite-c-sim-512.onnx) | 18MB | 50.9% | 此模型文件来源于[YOLOv5-Lite](https://github.com/ppogg/YOLOv5-Lite)，GPL-3.0 License |
|
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| [YOLOv5Lite-g](https://bj.bcebos.com/paddlehub/fastdeploy/v5Lite-g-sim-640.onnx) | 21MB | 57.6% | 此模型文件来源于[YOLOv5-Lite](https://github.com/ppogg/YOLOv5-Lite)，GPL-3.0 License |
|
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|
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|
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## 详细部署文档
|
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|
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@@ -11,13 +11,12 @@
|
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## 下载预训练ONNX模型
|
||||
|
||||
为了方便开发者的测试，下面提供了YOLOv6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 |
|
||||
|:---------------------------------------------------------------- |:----- |:----- |
|
||||
| [YOLOv6s](https://bj.bcebos.com/paddlehub/fastdeploy/yolov6s.onnx) | 66MB | 43.1% |
|
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| [YOLOv6s_640](https://bj.bcebos.com/paddlehub/fastdeploy/yolov6s-640x640.onnx) | 66MB | 43.1% |
|
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| [YOLOv6t](https://bj.bcebos.com/paddlehub/fastdeploy/yolov6t.onnx) | 58MB | 41.3% |
|
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| [YOLOv6n](https://bj.bcebos.com/paddlehub/fastdeploy/yolov6n.onnx) | 17MB | 35.0% |
|
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|
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| 模型 | 大小 | 精度 | 备注 |
|
||||
|:---------------------------------------------------------------- |:----- |:----- |:----- |
|
||||
| [YOLOv6s](https://bj.bcebos.com/paddlehub/fastdeploy/yolov6s.onnx) | 66MB | 43.1% | 此模型文件来源于[YOLOv6](https://github.com/meituan/YOLOv6)，GPL-3.0 License |
|
||||
| [YOLOv6s_640](https://bj.bcebos.com/paddlehub/fastdeploy/yolov6s-640x640.onnx) | 66MB | 43.1% | 此模型文件来源于[YOLOv6](https://github.com/meituan/YOLOv6)，GPL-3.0 License |
|
||||
| [YOLOv6t](https://bj.bcebos.com/paddlehub/fastdeploy/yolov6t.onnx) | 58MB | 41.3% | 此模型文件来源于[YOLOv6](https://github.com/meituan/YOLOv6)，GPL-3.0 License |
|
||||
| [YOLOv6n](https://bj.bcebos.com/paddlehub/fastdeploy/yolov6n.onnx) | 17MB | 35.0% | 此模型文件来源于[YOLOv6](https://github.com/meituan/YOLOv6)，GPL-3.0 License |
|
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|
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|
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## 详细部署文档
|
||||
|
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@@ -27,16 +27,14 @@ python models/export.py --grid --dynamic --end2end --weights PATH/TO/yolov7.pt
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## 下载预训练ONNX模型
|
||||
|
||||
为了方便开发者的测试，下面提供了YOLOv7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 |
|
||||
|:---------------------------------------------------------------- |:----- |:----- |
|
||||
| [YOLOv7](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx) | 141MB | 51.4% |
|
||||
| [YOLOv7x](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7x.onnx) | 273MB | 53.1% |
|
||||
| [YOLOv7-w6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-w6.onnx) | 269MB | 54.9% |
|
||||
| [YOLOv7-e6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6.onnx) | 372MB | 56.0% |
|
||||
| [YOLOv7-d6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-d6.onnx) | 511MB | 56.6% |
|
||||
| [YOLOv7-e6e](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6e.onnx) | 579MB | 56.8% |
|
||||
|
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|
||||
| 模型 | 大小 | 精度 | 备注 |
|
||||
|:---------------------------------------------------------------- |:----- |:----- | :----- |
|
||||
| [YOLOv7](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx) | 141MB | 51.4% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
|
||||
| [YOLOv7x](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7x.onnx) | 273MB | 53.1% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
|
||||
| [YOLOv7-w6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-w6.onnx) | 269MB | 54.9% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
|
||||
| [YOLOv7-e6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6.onnx) | 372MB | 56.0% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
|
||||
| [YOLOv7-d6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-d6.onnx) | 511MB | 56.6% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
|
||||
| [YOLOv7-e6e](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6e.onnx) | 579MB | 56.8% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
|
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|
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|
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## 详细部署文档
|
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|
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@@ -24,14 +24,14 @@ python models/export.py --grid --dynamic --end2end --weights PATH/TO/yolov7.pt
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|
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To facilitate testing for developers, we provide below the models exported by YOLOv7, which developers can download and use directly. (The accuracy of the models in the table is sourced from the official library)
|
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|
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| Model | Size | Accuracy |
|
||||
| ------------------------------------------------------------------------ | ----- | -------- |
|
||||
| [YOLOv7](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx) | 141MB | 51.4% |
|
||||
| [YOLOv7x](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7x.onnx) | 273MB | 53.1% |
|
||||
| [YOLOv7-w6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-w6.onnx) | 269MB | 54.9% |
|
||||
| [YOLOv7-e6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6.onnx) | 372MB | 56.0% |
|
||||
| [YOLOv7-d6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-d6.onnx) | 511MB | 56.6% |
|
||||
| [YOLOv7-e6e](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6e.onnx) | 579MB | 56.8% |
|
||||
| Model | Size | Accuracy | Note |
|
||||
| ------------------------------------------------------------------------ | ----- | -------- | -------- |
|
||||
| [YOLOv7](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx) | 141MB | 51.4% | This model file comes from [YOLOv7](https://github.com/WongKinYiu/yolov7), GPL-3.0 License |
|
||||
| [YOLOv7x](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7x.onnx) | 273MB | 53.1% | This model file comes from [YOLOv7](https://github.com/WongKinYiu/yolov7), GPL-3.0 License |
|
||||
| [YOLOv7-w6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-w6.onnx) | 269MB | 54.9% | This model file comes from [YOLOv7](https://github.com/WongKinYiu/yolov7), GPL-3.0 License |
|
||||
| [YOLOv7-e6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6.onnx) | 372MB | 56.0% | This model file comes from [YOLOv7](https://github.com/WongKinYiu/yolov7), GPL-3.0 License |
|
||||
| [YOLOv7-d6](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-d6.onnx) | 511MB | 56.6% | This model file comes from [YOLOv7](https://github.com/WongKinYiu/yolov7), GPL-3.0 License |
|
||||
| [YOLOv7-e6e](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6e.onnx) | 579MB | 56.8% | This model file comes from [YOLOv7](https://github.com/WongKinYiu/yolov7), GPL-3.0 License |
|
||||
|
||||
## Detailed Deployment Tutorials
|
||||
|
||||
|
||||
@@ -20,14 +20,14 @@ python export.py --weights yolov7.pt --grid --end2end --simplify --topk-all 100
|
||||
## 下载预训练ONNX模型
|
||||
|
||||
为了方便开发者的测试，下面提供了YOLOv7End2EndORT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 |
|
||||
|:---------------------------------------------------------------- |:----- |:----- |
|
||||
| [yolov7-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-end2end-ort-nms.onnx) | 141MB | 51.4% |
|
||||
| [yolov7x-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7x-end2end-ort-nms.onnx) | 273MB | 53.1% |
|
||||
| [yolov7-w6-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-w6-end2end-ort-nms.onnx) | 269MB | 54.9% |
|
||||
| [yolov7-e6-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6-end2end-ort-nms.onnx) | 372MB | 56.0% |
|
||||
| [yolov7-d6-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-d6-end2end-ort-nms.onnx) | 511MB | 56.6% |
|
||||
| [yolov7-e6e-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6e-end2end-ort-nms.onnx) | 579MB | 56.8% |
|
||||
| 模型 | 大小 | 精度 | 备注 |
|
||||
|:---------------------------------------------------------------- |:----- |:----- |:----- |
|
||||
| [yolov7-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-end2end-ort-nms.onnx) | 141MB | 51.4% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
|
||||
| [yolov7x-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7x-end2end-ort-nms.onnx) | 273MB | 53.1% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
|
||||
| [yolov7-w6-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-w6-end2end-ort-nms.onnx) | 269MB | 54.9% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
|
||||
| [yolov7-e6-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6-end2end-ort-nms.onnx) | 372MB | 56.0% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
|
||||
| [yolov7-d6-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-d6-end2end-ort-nms.onnx) | 511MB | 56.6% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
|
||||
| [yolov7-e6e-end2end-ort-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6e-end2end-ort-nms.onnx) | 579MB | 56.8% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
|
||||
|
||||
|
||||
## 详细部署文档
|
||||
|
||||
@@ -22,14 +22,14 @@ python export.py --weights yolov7.pt --grid --end2end --simplify --topk-all 100
|
||||
## 下载预训练ONNX模型
|
||||
|
||||
为了方便开发者的测试，下面提供了YOLOv7End2EndTRT 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 |
|
||||
|:---------------------------------------------------------------- |:----- |:----- |
|
||||
| [yolov7-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-end2end-trt-nms.onnx) | 141MB | 51.4% |
|
||||
| [yolov7x-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7x-end2end-trt-nms.onnx) | 273MB | 53.1% |
|
||||
| [yolov7-w6-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-w6-end2end-trt-nms.onnx) | 269MB | 54.9% |
|
||||
| [yolov7-e6-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6-end2end-trt-nms.onnx) | 372MB | 56.0% |
|
||||
| [yolov7-d6-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-d6-end2end-trt-nms.onnx) | 511MB | 56.6% |
|
||||
| [yolov7-e6e-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6e-end2end-trt-nms.onnx) | 579MB | 56.8% |
|
||||
| 模型 | 大小 | 精度 | 备注 |
|
||||
|:---------------------------------------------------------------- |:----- |:----- |:----- |
|
||||
| [yolov7-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-end2end-trt-nms.onnx) | 141MB | 51.4% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
|
||||
| [yolov7x-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7x-end2end-trt-nms.onnx) | 273MB | 53.1% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
|
||||
| [yolov7-w6-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-w6-end2end-trt-nms.onnx) | 269MB | 54.9% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
|
||||
| [yolov7-e6-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6-end2end-trt-nms.onnx) | 372MB | 56.0% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
|
||||
| [yolov7-d6-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-d6-end2end-trt-nms.onnx) | 511MB | 56.6% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
|
||||
| [yolov7-e6e-end2end-trt-nms](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-e6e-end2end-trt-nms.onnx) | 579MB | 56.8% | 此模型文件来源于[YOLOv7](https://github.com/WongKinYiu/yolov7)，GPL-3.0 License |
|
||||
|
||||
|
||||
## 详细部署文档
|
||||
|
||||
@@ -16,10 +16,10 @@
|
||||
|
||||
| 模型 | 参数大小 | 精度 | 备注 |
|
||||
|:---------------------------------------------------------------- |:----- |:----- | :------ |
|
||||
| [rvm_mobilenetv3_fp32.onnx](https://bj.bcebos.com/paddlehub/fastdeploy/rvm_mobilenetv3_fp32.onnx) | 15MB | - |
|
||||
| [rvm_resnet50_fp32.onnx](https://bj.bcebos.com/paddlehub/fastdeploy/rvm_resnet50_fp32.onnx) | 103MB | - |
|
||||
| [rvm_mobilenetv3_trt.onnx](https://bj.bcebos.com/paddlehub/fastdeploy/rvm_mobilenetv3_trt.onnx) | 15MB | - |
|
||||
| [rvm_resnet50_trt.onnx](https://bj.bcebos.com/paddlehub/fastdeploy/rvm_resnet50_trt.onnx) | 103MB | - |
|
||||
| [rvm_mobilenetv3_fp32.onnx](https://bj.bcebos.com/paddlehub/fastdeploy/rvm_mobilenetv3_fp32.onnx) | 15MB ||exported from [RobustVideoMatting](https://github.com/PeterL1n/RobustVideoMatting/commit/81a1093)，GPL-3.0 License |
|
||||
| [rvm_resnet50_fp32.onnx](https://bj.bcebos.com/paddlehub/fastdeploy/rvm_resnet50_fp32.onnx) | 103MB | |exported from [RobustVideoMatting](https://github.com/PeterL1n/RobustVideoMatting/commit/81a1093)，GPL-3.0 License |
|
||||
| [rvm_mobilenetv3_trt.onnx](https://bj.bcebos.com/paddlehub/fastdeploy/rvm_mobilenetv3_trt.onnx) | 15MB | |exported from [RobustVideoMatting](https://github.com/PeterL1n/RobustVideoMatting/commit/81a1093)，GPL-3.0 License |
|
||||
| [rvm_resnet50_trt.onnx](https://bj.bcebos.com/paddlehub/fastdeploy/rvm_resnet50_trt.onnx) | 103MB | | exported from [RobustVideoMatting](https://github.com/PeterL1n/RobustVideoMatting/commit/81a1093)，GPL-3.0 License |
|
||||
|
||||
**Note**：
|
||||
- 如果要使用 TensorRT 进行推理，需要下载后缀为 trt 的 onnx 模型文件
|
||||
|
||||
@@ -41,8 +41,12 @@ RUN apt-get update \
|
||||
RUN apt-get update \
|
||||
&& apt-get install -y --no-install-recommends libre2-5 libb64-0d python3 python3-pip libarchive-dev ffmpeg libsm6 libxext6 \
|
||||
&& python3 -m pip install -U pip \
|
||||
&& python3 -m pip install paddlenlp fast-tokenizer-python \
|
||||
&& python3 -m pip install paddlepaddle-gpu==2.4.1.post112 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html
|
||||
&& python3 -m pip install paddlenlp fast-tokenizer-python
|
||||
|
||||
# unset proxy
|
||||
ENV http_proxy=
|
||||
ENV https_proxy=
|
||||
python3 -m pip install paddlepaddle-gpu==2.4.1.post112 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html
|
||||
|
||||
COPY python/dist/*.whl /opt/fastdeploy/
|
||||
RUN python3 -m pip install /opt/fastdeploy/*.whl \
|
||||
@@ -53,6 +57,3 @@ COPY build/fastdeploy_install /opt/fastdeploy/
|
||||
|
||||
ENV LD_LIBRARY_PATH="/opt/TensorRT-8.4.1.5/lib/:/opt/fastdeploy/lib:/opt/fastdeploy/third_libs/install/onnxruntime/lib:/opt/fastdeploy/third_libs/install/paddle2onnx/lib:/opt/fastdeploy/third_libs/install/tensorrt/lib:/opt/fastdeploy/third_libs/install/paddle_inference/paddle/lib:/opt/fastdeploy/third_libs/install/paddle_inference/third_party/install/mkldnn/lib:/opt/fastdeploy/third_libs/install/paddle_inference/third_party/install/mklml/lib:/opt/fastdeploy/third_libs/install/openvino/runtime/lib:$LD_LIBRARY_PATH"
|
||||
ENV PATH="/opt/tritonserver/bin:$PATH"
|
||||
# unset proxy
|
||||
ENV http_proxy=
|
||||
ENV https_proxy=
|
||||
|
||||
@@ -12,7 +12,41 @@
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
WITH_GPU=${1:-ON}
|
||||
|
||||
ARGS=`getopt -a -o w:n:h:hs -l WITH_GPU:,docker_name:,http_proxy:,https_proxy: -- "$@"`
|
||||
|
||||
eval set -- "${ARGS}"
|
||||
echo "parse start"
|
||||
|
||||
while true
|
||||
do
|
||||
case "$1" in
|
||||
-w|--WITH_GPU)
|
||||
WITH_GPU="$2"
|
||||
shift;;
|
||||
-n|--docker_name)
|
||||
docker_name="$2"
|
||||
shift;;
|
||||
-h|--http_proxy)
|
||||
http_proxy="$2"
|
||||
shift;;
|
||||
-hs|--https_proxy)
|
||||
https_proxy="$2"
|
||||
shift;;
|
||||
--)
|
||||
shift
|
||||
break;;
|
||||
esac
|
||||
shift
|
||||
done
|
||||
|
||||
if [ -z $WITH_GPU ];then
|
||||
WITH_GPU="ON"
|
||||
fi
|
||||
|
||||
if [ -z $docker_name ];then
|
||||
docker_name="build_fd"
|
||||
fi
|
||||
|
||||
if [ $WITH_GPU == "ON" ]; then
|
||||
|
||||
@@ -30,7 +64,7 @@ if [ ! -d "./TensorRT-8.4.1.5/" ]; then
|
||||
rm -rf TensorRT-8.4.1.5.Linux.x86_64-gnu.cuda-11.6.cudnn8.4.tar.gz
|
||||
fi
|
||||
|
||||
nvidia-docker run -i --rm --name build_fd \
|
||||
nvidia-docker run -i --rm --name ${docker_name} \
|
||||
-v`pwd`/..:/workspace/fastdeploy \
|
||||
-e "http_proxy=${http_proxy}" \
|
||||
-e "https_proxy=${https_proxy}" \
|
||||
@@ -68,7 +102,7 @@ else
|
||||
|
||||
echo "start build FD CPU library"
|
||||
|
||||
docker run -i --rm --name build_fd \
|
||||
docker run -i --rm --name ${docker_name} \
|
||||
-v`pwd`/..:/workspace/fastdeploy \
|
||||
-e "http_proxy=${http_proxy}" \
|
||||
-e "https_proxy=${https_proxy}" \
|
||||
|
||||
Reference in New Issue
Block a user