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Add support to YOLOv5 v4.0 and v5.0

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Marcos Luciano
2022-04-08 17:20:02 -03:00
parent 7bcc9b62fa
commit 18ae611929
1 changed files with 5 additions and 3 deletions
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@@ -20,7 +20,7 @@ NVIDIA DeepStream SDK 6.0.1 configuration for YOLO models
* Support for INT8 calibration
* Support for non square models
* Support for reorg, implicit and channel layers (YOLOR)
* YOLOv5 6.0 / 6.1 native support
* YOLOv5 4.0, 5.0, 6.0 and 6.1 native support
* YOLOR native support
* Models benchmarks (**outdated**)
* **GPU YOLO Decoder (moved from CPU to GPU to get better performance)** [#138](https://github.com/marcoslucianops/DeepStream-Yolo/issues/138)
@@ -75,7 +75,7 @@ NVIDIA DeepStream SDK 6.0.1 configuration for YOLO models
### Tested models
* [Darknet YOLO](https://github.com/AlexeyAB/darknet)
* [YOLOv5 6.0 / 6.1](https://github.com/ultralytics/yolov5)
* [YOLOv5 4.0, 5.0, 6.0 and 6.1](https://github.com/ultralytics/yolov5)
* [YOLOR](https://github.com/WongKinYiu/yolor)
* [MobileNet-YOLO](https://github.com/dog-qiuqiu/MobileNet-Yolo)
* [YOLO-Fastest](https://github.com/dog-qiuqiu/Yolo-Fastest)
@@ -378,11 +378,13 @@ config-file=config_infer_primary_yoloV2.txt
### YOLOv5 usage
**NOTE**: Make sure to change the YOLOv5 repo version to your model version before conversion.
#### 1. Copy gen_wts_yoloV5.py from DeepStream-Yolo/utils to [ultralytics/yolov5](https://github.com/ultralytics/yolov5) folder
#### 2. Open the ultralytics/yolov5 folder
#### 3. Download pt file from [ultralytics/yolov5](https://github.com/ultralytics/yolov5/releases/tag/v6.1) website (example for YOLOv5n)
#### 3. Download pt file from [ultralytics/yolov5](https://github.com/ultralytics/yolov5/releases/) website (example for YOLOv5n 6.1)
```
wget https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n.pt