Add support CO-DETR (MMDetection)

docs/CODETR.md

@@ -0,0 +1,187 @@
+# CO-DETR (MMDetection) usage
+
+* [Convert model](#convert-model)
+* [Compile the lib](#compile-the-lib)
+* [Edit the config_infer_primary_codetr file](#edit-the-config_infer_primary_codetr-file)
+* [Edit the deepstream_app_config file](#edit-the-deepstream_app_config-file)
+* [Testing the model](#testing-the-model)
+
+##
+
+### Convert model
+
+#### 1. Download the CO-DETR (MMDetection) repo and install the requirements
+
+```
+git clone https://github.com/open-mmlab/mmdetection.git
+cd mmdetection
+pip3 install openmim
+mim install mmengine
+mim install mmdeploy
+mim install "mmcv>=2.0.0rc4,<2.2.0"
+pip3 install -v -e .
+pip3 install onnx onnxslim onnxruntime
+```
+
+**NOTE**: It is recommended to use Python virtualenv.
+
+#### 2. Copy conversor
+
+Copy the `export_codetr.py` file from `DeepStream-Yolo/utils` directory to the `mmdetection` folder.
+
+#### 3. Download the model
+
+Download the `pth` file from [CO-DETR (MMDetection)](https://github.com/open-mmlab/mmdetection/tree/main/projects/CO-DETR) releases (example for Co-DINO R50 DETR*)
+
+```
+wget https://download.openmmlab.com/mmdetection/v3.0/codetr/co_dino_5scale_r50_1x_coco-7481f903.pth
+```
+
+**NOTE**: You can use your custom model.
+
+#### 4. Convert model
+
+Generate the ONNX model file (example for Co-DINO R50 DETR)
+
+```
+python3 export_codetr.py -w co_dino_5scale_r50_1x_coco-7481f903.pth -c projects/CO-DETR/configs/codino/co_dino_5scale_r50_8xb2_1x_coco.py --dynamic
+```
+
+**NOTE**: To change the inference size (defaut: 640)
+
+```
+-s SIZE
+--size SIZE
+-s HEIGHT WIDTH
+--size HEIGHT WIDTH
+```
+
+Example for 1280
+
+```
+-s 1280
+```
+
+or
+
+```
+-s 1280 1280
+```
+
+**NOTE**: To simplify the ONNX model (DeepStream >= 6.0)
+
+```
+--simplify
+```
+
+**NOTE**: To use dynamic batch-size (DeepStream >= 6.1)
+
+```
+--dynamic
+```
+
+**NOTE**: To use static batch-size (example for batch-size = 4)
+
+```
+--batch 4
+```
+
+**NOTE**: If you are using the DeepStream 5.1, remove the `--dynamic` arg and use opset 12 or lower. The default opset is 11.
+
+```
+--opset 12
+```
+
+#### 5. Copy generated files
+
+Copy the generated ONNX model file and labels.txt file (if generated) to the `DeepStream-Yolo` folder.
+
+##
+
+### Compile the lib
+
+1. Open the `DeepStream-Yolo` folder and compile the lib
+
+2. Set the `CUDA_VER` according to your DeepStream version
+
+```
+export CUDA_VER=XY.Z
+```
+
+* x86 platform
+
+  ```
+  DeepStream 7.1 = 12.6
+  DeepStream 7.0 / 6.4 = 12.2
+  DeepStream 6.3 = 12.1
+  DeepStream 6.2 = 11.8
+  DeepStream 6.1.1 = 11.7
+  DeepStream 6.1 = 11.6
+  DeepStream 6.0.1 / 6.0 = 11.4
+  DeepStream 5.1 = 11.1
+  ```
+
+* Jetson platform
+
+  ```
+  DeepStream 7.1 = 12.6
+  DeepStream 7.0 / 6.4 = 12.2
+  DeepStream 6.3 / 6.2 / 6.1.1 / 6.1 = 11.4
+  DeepStream 6.0.1 / 6.0 / 5.1 = 10.2
+  ```
+
+3. Make the lib
+
+```
+make -C nvdsinfer_custom_impl_Yolo clean && make -C nvdsinfer_custom_impl_Yolo
+```
+
+##
+
+### Edit the config_infer_primary_codetr file
+
+Edit the `config_infer_primary_codetr.txt` file according to your model (example for Co-DINO R50 DETR with 80 classes)
+
+```
+[property]
+...
+onnx-file=co_dino_5scale_r50_1x_coco-7481f903.pth.onnx
+...
+num-detected-classes=80
+...
+parse-bbox-func-name=NvDsInferParseYolo
+...
+```
+
+**NOTE**: The **CO-DETR (MMDetection)** resizes the input with left/top padding. To get better accuracy, use
+
+```
+[property]
+...
+maintain-aspect-ratio=1
+symmetric-padding=0
+...
+```
+
+##
+
+### Edit the deepstream_app_config file
+
+```
+...
+[primary-gie]
+...
+config-file=config_infer_primary_codetr.txt
+```
+
+##
+
+### Testing the model
+
+```
+deepstream-app -c deepstream_app_config.txt
+```
+
+**NOTE**: The TensorRT engine file may take a very long time to generate (sometimes more than 10 minutes).
+
+**NOTE**: For more information about custom models configuration (`batch-size`, `network-mode`, etc), please check the [`docs/customModels.md`](customModels.md) file.

docs/PPYOLOE.md

@@ -14,7 +14,7 @@
 
 #### 1. Download the PaddleDetection repo and install the requirements
 
-https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/INSTALL.md
+https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.8/docs/tutorials/INSTALL.md
 
 **NOTE**: It is recommended to use Python virtualenv.
 
@@ -24,7 +24,7 @@ Copy the `export_ppyoloe.py` file from `DeepStream-Yolo/utils` directory to the
 
 #### 3. Download the model
 
-Download the `pdparams` file from [PP-YOLOE](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe) releases (example for PP-YOLOE+_s)
+Download the `pdparams` file from [PP-YOLOE](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.8/configs/ppyoloe) releases (example for PP-YOLOE+_s)
 
 ```
 wget https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_80e_coco.pdparams

docs/RTDETR_Paddle.md

@@ -14,7 +14,7 @@
 
 #### 1. Download the PaddleDetection repo and install the requirements
 
-https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/INSTALL.md
+https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.8/docs/tutorials/INSTALL.md
 
 ```
 git clone https://github.com/lyuwenyu/RT-DETR.git