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New features and fixes

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This commit is contained in:
Marcos Luciano
2023-06-05 14:48:23 -03:00
parent 3f14b0d95d
commit 66a6754b77
57 changed files with 2137 additions and 1534 deletions
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docs/YOLOX.md
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@@ -46,6 +46,24 @@ Generate the ONNX model file (example for YOLOX-s)
python3 export_yolox.py -w yolox_s.pth -c exps/default/yolox_s.py --simplify --dynamic
```
**NOTE**: To simplify the ONNX model
```
--simplify
```
**NOTE**: To use dynamic batch-size
```
--dynamic
```
**NOTE**: To use implicit batch-size (example for batch-size = 4)
```
--batch 4
```
**NOTE**: If you are using DeepStream 5.1, use opset 12 or lower. The default opset is 11.
```
@@ -89,7 +107,7 @@ Open the `DeepStream-Yolo` folder and compile the lib
* DeepStream 5.1 on x86 platform
```
CUDA_VER=11.1 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
CUDA_VER=11.1 make -C nvdsinfer_custom_impl_Yolo
```
* DeepStream 6.2 / 6.1.1 / 6.1 on Jetson platform
@@ -98,18 +116,12 @@ Open the `DeepStream-Yolo` folder and compile the lib
CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
```
* DeepStream 6.0.1 / 6.0 on Jetson platform
* DeepStream 6.0.1 / 6.0 / 5.1 on Jetson platform
```
CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
```
* DeepStream 5.1 on Jetson platform
```
CUDA_VER=10.2 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
```
##
### Edit the config_infer_primary_yolox file
@@ -120,7 +132,6 @@ Edit the `config_infer_primary_yolox.txt` file according to your model (example
[property]
...
onnx-file=yolox_s.onnx
model-engine-file=yolox_s.onnx_b1_gpu0_fp32.engine
...
num-detected-classes=80
...
@@ -133,14 +144,18 @@ parse-bbox-func-name=NvDsInferParseYolo
**NOTE**: The **YOLOX and YOLOX legacy** resize the input with left/top padding. To get better accuracy, use
```
...
maintain-aspect-ratio=1
symmetric-padding=0
...
```
**NOTE**: The **YOLOX** uses no normalization on the image preprocess. It is important to change the `net-scale-factor` according to the trained values.
```
...
net-scale-factor=1
...
```
**NOTE**: The **YOLOX legacy** uses normalization on the image preprocess. It is important to change the `net-scale-factor` and `offsets` according to the trained values.
@@ -148,8 +163,18 @@ net-scale-factor=1
Default: `mean = 0.485, 0.456, 0.406` and `std = 0.229, 0.224, 0.225`
```
...
net-scale-factor=0.0173520735727919486
offsets=123.675;116.28;103.53
...
```
**NOTE**: By default, the dynamic batch-size is set. To use implicit batch-size, uncomment the line
```
...
force-implicit-batch-dim=1
...
```
##