diff --git a/deploy/pipeline/README.md b/deploy/pipeline/README.md index 15acf2fa9ac20538380903e15012c222e3ffc9b9..cbb2d86fbe0a985545f0c466886611a9c4eca729 100644 --- a/deploy/pipeline/README.md +++ b/deploy/pipeline/README.md @@ -62,7 +62,7 @@ | 打电话识别 | 单人ms | [目标检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)
[基于人体id的图像分类](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPHGNet_tiny_calling_halfbody.zip) | 目标检测：182M
基于人体id的图像分类：45M | -点击模型方案中的模型即可下载指定模型，下载后解压存放至`./output_inference`目录中 +点击模型方案中的模型即可下载指定模型，下载后解压存放至`./output_inference`目录中 @@ -71,20 +71,14 @@
端到端模型效果（点击展开） -| 任务 | 端到端速度（ms） | 模型方案 | 模型体积 | -| --------- | --------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------- | -| 行人检测（高精度） | 25.1ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | 182M | -| 行人检测（轻量级） | 16.2ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) | 27M | -| 行人跟踪（高精度） | 31.8ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | 182M | -| 行人跟踪（轻量级） | 21.0ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) | 27M | -| 属性识别（高精度） | 单人8.5ms | [目标检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)
[属性识别](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.zip) | 目标检测：182M
属性识别：86M | -| 属性识别（轻量级） | 单人7.1ms | [目标检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)
[属性识别](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.zip) | 目标检测：182M
属性识别：86M | -| 摔倒识别 | 单人10ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)
[关键点检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip)
[基于关键点行为识别](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) | 多目标跟踪：182M
关键点检测：101M
基于关键点行为识别：21.8M | -| 闯入识别 | 31.8ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | 182M | -| 打架识别 | 19.7ms | [视频分类](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | 90M | -| 抽烟识别 | 单人15.1ms | [目标检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)
[基于人体id的目标检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/ppyoloe_crn_s_80e_smoking_visdrone.zip) | 目标检测：182M
基于人体id的目标检测：27M | -| 打电话识别 | 单人ms | [目标检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)
[基于人体id的图像分类](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPHGNet_tiny_calling_halfbody.zip) | 目标检测：182M
基于人体id的图像分类：45M | - +| 任务 | 端到端速度（ms）| 模型方案 | 模型体积 | +| :---------: | :-------: | :------: |:------: | +| 车辆检测（高精度） | 25.7ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | 182M | +| 车辆检测（轻量级） | 13.2ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_ppvehicle.zip) | 27M | +| 车辆跟踪（高精度） | 40ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | 182M | +| 车辆跟踪（轻量级） | 25ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_ppvehicle.zip) | 27M | +| 车牌识别 | 4.68ms | [车牌检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_det_infer.tar.gz)
[车牌字符识别](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_rec_infer.tar.gz) | 车牌检测：3.9M
车牌字符识别： 12M | +| 车辆属性 | 7.31ms | [车辆属性](https://bj.bcebos.com/v1/paddledet/models/pipeline/vehicle_attribute_model.zip) | 7.2M | 点击模型方案中的模型即可下载指定模型，下载后解压存放至`./output_inference`目录中 diff --git a/deploy/pipeline/docs/tutorials/PPVehicle_QUICK_STARTED.md b/deploy/pipeline/docs/tutorials/PPVehicle_QUICK_STARTED.md index ec0a8b89f57bbc02ea802265f5e4085acfb3cc98..1d45ff521c5848ab1182e9bc620cc9be3ac8b258 100644 --- a/deploy/pipeline/docs/tutorials/PPVehicle_QUICK_STARTED.md +++ b/deploy/pipeline/docs/tutorials/PPVehicle_QUICK_STARTED.md @@ -46,11 +46,13 @@ PP-Vehicle提供了目标检测、属性识别、行为识别、ReID预训练模 | 任务 | 端到端速度（ms）| 模型方案 | 模型体积 | | :---------: | :-------: | :------: |:------: | -| 车辆检测（高精度） | 25.1ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | 182M | -| 车辆检测（轻量级） | 16.2ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_ppvehicle.zip) | 27M | -| 车辆跟踪（高精度） | 31.8ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | 182M | -| 车辆跟踪（轻量级） | 21.0ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_ppvehicle.zip) | 27M | -| 闯入识别 | 31.8ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | 多目标跟踪：182M | +| 车辆检测（高精度） | 25.7ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | 182M | +| 车辆检测（轻量级） | 13.2ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_ppvehicle.zip) | 27M | +| 车辆跟踪（高精度） | 40ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | 182M | +| 车辆跟踪（轻量级） | 25ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_ppvehicle.zip) | 27M | +| 车牌识别 | 4.68ms | [车牌检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_det_infer.tar.gz)
[车牌字符识别](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_rec_infer.tar.gz) | 车牌检测：3.9M
车牌字符识别： 12M | +| 车辆属性 | 7.31ms | [车辆属性](https://bj.bcebos.com/v1/paddledet/models/pipeline/vehicle_attribute_model.zip) | 7.2M | + 下载模型后，解压至`./output_inference`文件夹。 @@ -97,7 +99,7 @@ VEHICLE_ATTR: **注意：** - 如果用户需要实现不同任务，可以在配置文件对应enable选项设置为True。 -- 如果用户仅需要修改模型文件路径，可以在命令行中加入 `--model_dir det=ppyoloe/` 即可，也可以手动修改配置文件中的相应模型路径，详细说明参考下方参数说明文档。 +- 如果用户仅需要修改模型文件路径，可以在命令行中--config后面紧跟着 `-o MOT.model_dir=ppyoloe/` 进行修改即可，也可以手动修改配置文件中的相应模型路径，详细说明参考下方参数说明文档。 ## 预测部署 @@ -111,7 +113,7 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppv # 车辆跟踪，指定配置文件路径，模型路径和测试视频，在配置文件```deploy/pipeline/config/infer_cfg_ppvehicle.yml```中的MOT部分enable设置为```True``` # 命令行中指定的模型路径优先级高于配置文件 -python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml --video_file=test_video.mp4 --device=gpu --model_dir det=ppyoloe/ [--run_mode trt_fp16] +python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml --video_file=test_video.mp4 --device=gpu [--run_mode trt_fp16] # 车辆属性识别，指定配置文件路径和测试视频，在配置文件```deploy/pipeline/config/infer_cfg_ppvehicle.yml```中的ATTR部分enable设置为```True``` python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml --video_file=test_video.mp4 --device=gpu [--run_mode trt_fp16] @@ -130,7 +132,6 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppv |-------|-------|----------| | --config | Yes | 配置文件路径 | | -o | Option | 覆盖配置文件中对应的配置 | -| --model_dir | Option | 各任务模型路径，优先级高于配置文件, 例如`--model_dir det=better_det/ attr=better_attr/`| | --image_file | Option | 需要预测的图片 | | --image_dir | Option | 要预测的图片文件夹路径 | | --video_file | Option | 需要预测的视频，或者rtsp流地址 | diff --git a/deploy/pipeline/docs/tutorials/pphuman_action.md b/deploy/pipeline/docs/tutorials/pphuman_action.md index cf0c5af501b465de810d37813032bcb316e6fde5..ee49282eb8ca0ab221dfa3bb6400da51de395716 100644 --- a/deploy/pipeline/docs/tutorials/pphuman_action.md +++ b/deploy/pipeline/docs/tutorials/pphuman_action.md @@ -60,12 +60,12 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph 3. 若修改模型路径，有以下两种方式： - ```./deploy/pipeline/config/infer_cfg_pphuman.yml```下可以配置不同模型路径，关键点模型和摔倒行为识别模型分别对应`KPT`和`SKELETON_ACTION`字段，修改对应字段下的路径为实际期望的路径即可。 - - 命令行中增加`--model_dir`修改模型路径： + - 命令行中--config后面紧跟着增加`-o KPT.model_dir=xxx SKELETON_ACTION.model_dir=xxx `修改模型路径： ```python python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \ + -o KPT.model_dir=./dark_hrnet_w32_256x192 SKELETON_ACTION.model_dir=./STGCN\ --video_file=test_video.mp4 \ - --device=gpu \ - --model_dir kpt=./dark_hrnet_w32_256x192 action=./STGCN + --device=gpu ``` 4. 启动命令中的完整参数说明，请参考[参数说明](./PPHuman_QUICK_STARTED.md)。 diff --git a/deploy/pipeline/docs/tutorials/pphuman_action_en.md b/deploy/pipeline/docs/tutorials/pphuman_action_en.md index 943a5c5678997816f66941e785f4ac95256acf83..3eab1d447736c26a354ca8527e391fe9fc1500ac 100644 --- a/deploy/pipeline/docs/tutorials/pphuman_action_en.md +++ b/deploy/pipeline/docs/tutorials/pphuman_action_en.md @@ -72,13 +72,13 @@ SKELETON_ACTION: # Config for skeleton-based action recognition model 3. There are two ways to modify the model path: - In ```./deploy/pipeline/config/infer_cfg_pphuman.yml```, you can configurate different model paths，which is proper only if you match keypoint models and action recognition models with the fields of `KPT` and `SKELETON_ACTION` respectively, and modify the corresponding path of each field into the expected path. - - Add `--model_dir` in the command line to revise the model path： + - Add `-o KPT.model_dir=xxx SKELETON_ACTION.model_dir=xxx ` in the command line following the --config to change the model path： ```python - python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \ - --video_file=test_video.mp4 \ - --device=gpu \ - --model_dir kpt=./dark_hrnet_w32_256x192 action=./STGCN +python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \ + -o KPT.model_dir=./dark_hrnet_w32_256x192 SKELETON_ACTION.model_dir=./STGCN\ + --video_file=test_video.mp4 \ + --device=gpu ``` 4. For detailed parameter description, please refer to [Parameter Description](./PPHuman_QUICK_STARTED.md) diff --git a/deploy/pipeline/docs/tutorials/pphuman_attribute.md b/deploy/pipeline/docs/tutorials/pphuman_attribute.md index 16109606c0cbfbac9794512648bf34a7150d0cb7..5e5f1dc61f204e7106af26516d4311637acd32b5 100644 --- a/deploy/pipeline/docs/tutorials/pphuman_attribute.md +++ b/deploy/pipeline/docs/tutorials/pphuman_attribute.md @@ -59,12 +59,12 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph 4. 若修改模型路径，有以下两种方式： - 方法一：```./deploy/pipeline/config/infer_cfg_pphuman.yml```下可以配置不同模型路径，属性识别模型修改ATTR字段下配置 - - 方法二：命令行中增加`--model_dir`修改模型路径： + - 方法二：命令行中--config后面紧跟着增加`-o ATTR.model_dir`修改模型路径： ```python -python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \ +python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml + -o ATTR.model_dir=output_inference/PPLCNet_x1_0_person_attribute_945_infer/\ --video_file=test_video.mp4 \ - --device=gpu \ - --model_dir attr=output_inference/PPLCNet_x1_0_person_attribute_945_infer/ + --device=gpu ``` 测试效果如下： diff --git a/deploy/pipeline/docs/tutorials/pphuman_attribute_en.md b/deploy/pipeline/docs/tutorials/pphuman_attribute_en.md index 70c55de0edea1a2c3b0574efd7835c10d94f74e6..fb402f757302160055e63a9f5c289ecdd4aab8de 100644 --- a/deploy/pipeline/docs/tutorials/pphuman_attribute_en.md +++ b/deploy/pipeline/docs/tutorials/pphuman_attribute_en.md @@ -55,12 +55,12 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph 4. If you want to change the model path, there are two methods： - The first: In ```./deploy/pipeline/config/infer_cfg_pphuman.yml``` you can configurate different model paths. In attribute recognition models, you can modify the configuration in the field of ATTR. - - The second: Add `--model_dir` in the command line to change the model path： + - The second: Add `-o ATTR.model_dir` in the command line following the --config to change the model path： ```python python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \ + -o ATTR.model_dir=output_inference/PPLCNet_x1_0_person_attribute_945_infer/\ --video_file=test_video.mp4 \ - --device=gpu \ - --model_dir attr=output_inference/PPLCNet_x1_0_person_attribute_945_infer/ + --device=gpu ``` The test result is： diff --git a/deploy/pipeline/docs/tutorials/pphuman_mot.md b/deploy/pipeline/docs/tutorials/pphuman_mot.md index 13a6c3f05eb170c65e38de1002413be2bef23f17..6380ffbbd7f68d6feb286a1b4f4f33eebcf473a5 100644 --- a/deploy/pipeline/docs/tutorials/pphuman_mot.md +++ b/deploy/pipeline/docs/tutorials/pphuman_mot.md @@ -39,15 +39,15 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph 4. 若修改模型路径，有以下两种方式： - ```./deploy/pipeline/config/infer_cfg_pphuman.yml```下可以配置不同模型路径，检测和跟踪模型分别对应`DET`和`MOT`字段，修改对应字段下的路径为实际期望的路径即可。 - - 命令行中增加`--model_dir`修改模型路径： + - 命令行中--config后面紧跟着增加`-o MOT.model_dir`修改模型路径： ```python python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \ + -o MOT.model_dir=ppyoloe/\ --video_file=test_video.mp4 \ --device=gpu \ --region_type=horizontal \ --do_entrance_counting \ - --draw_center_traj \ - --model_dir det=ppyoloe/ + --draw_center_traj ``` **注意:** diff --git a/deploy/pipeline/docs/tutorials/pphuman_mot_en.md b/deploy/pipeline/docs/tutorials/pphuman_mot_en.md index c66ad61721280c4d05e42820207794f893f9cdec..06e7ab0149012813b9b4488d2c136981ba578633 100644 --- a/deploy/pipeline/docs/tutorials/pphuman_mot_en.md +++ b/deploy/pipeline/docs/tutorials/pphuman_mot_en.md @@ -39,16 +39,16 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph 4. There are two ways to modify the model path: - In `./deploy/pipeline/config/infer_cfg_pphuman.yml`, you can configurate different model paths，which is proper only if you match keypoint models and action recognition models with the fields of `DET` and `MOT` respectively, and modify the corresponding path of each field into the expected path. - - Add `--model_dir` in the command line to revise the model path: + - Add `-o MOT.model_dir` in the command line following the --config to change the model path： ```python python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \ + -o MOT.model_dir=ppyoloe/\ --video_file=test_video.mp4 \ --device=gpu \ --region_type=horizontal \ --do_entrance_counting \ - --draw_center_traj \ - --model_dir det=ppyoloe/ + --draw_center_traj ``` **Note:** diff --git a/deploy/pipeline/docs/tutorials/pphuman_mtmct.md b/deploy/pipeline/docs/tutorials/pphuman_mtmct.md index 894c18ee0d5541f082f1f70b0250549519768020..e39d53fbd02fb51671928de9ff4e65acfa83cad6 100644 --- a/deploy/pipeline/docs/tutorials/pphuman_mtmct.md +++ b/deploy/pipeline/docs/tutorials/pphuman_mtmct.md @@ -18,10 +18,9 @@ python3 deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pp ```python python3 deploy/pipeline/pipeline.py - --config deploy/pipeline/config/infer_cfg_pphuman.yml + --config deploy/pipeline/config/infer_cfg_pphuman.yml -o REID.model_dir=reid_best/ --video_dir=[your_video_file_directory] --device=gpu - --model_dir reid=reid_best/ ``` ## 方案说明 diff --git a/deploy/pipeline/docs/tutorials/pphuman_mtmct_en.md b/deploy/pipeline/docs/tutorials/pphuman_mtmct_en.md index 0321d2a52d511a00b0c095a3878c3a959e646292..f67c1b67e0a80fadcd5dd03ab24e54c526e6a987 100644 --- a/deploy/pipeline/docs/tutorials/pphuman_mtmct_en.md +++ b/deploy/pipeline/docs/tutorials/pphuman_mtmct_en.md @@ -18,10 +18,9 @@ python3 deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pp ```python python3 deploy/pipeline/pipeline.py - --config deploy/pipeline/config/infer_cfg_pphuman.yml + --config deploy/pipeline/config/infer_cfg_pphuman.yml -o REID.model_dir=reid_best/ --video_dir=[your_video_file_directory] --device=gpu - --model_dir reid=reid_best/ ``` ## Intorduction to the Solution diff --git a/deploy/pipeline/pipe_utils.py b/deploy/pipeline/pipe_utils.py index 8cc863aa51c4e2df713fee181d9ea5c36257b50b..e17f8aeab784a673d09fda91bf8b3ffefab5c7ec 100644 --- a/deploy/pipeline/pipe_utils.py +++ b/deploy/pipeline/pipe_utils.py @@ -88,7 +88,8 @@ class PipeTimer(Times): for k, v in self.module_time.items(): v_time = round(v.value(), 4) if v_time > 0 and k in ['det', 'mot', 'video_action']: - print("{} time(ms): {}".format(k, v_time * 1000)) + print("{} time(ms): {}; per frame average time(ms): {}".format( + k, v_time * 1000, v_time * 1000 / self.img_num)) elif v_time > 0: print("{} time(ms): {}; per trackid average time(ms): {}". format(k, v_time * 1000, v_time * 1000 / self.track_num)) diff --git a/deploy/pipeline/pipeline.py b/deploy/pipeline/pipeline.py index da2489682edd165d392a440e8660987b5fff2877..3f0b8ce4ac4e9b6ee8f978dfe19987d875a5363a 100644 --- a/deploy/pipeline/pipeline.py +++ b/deploy/pipeline/pipeline.py @@ -465,6 +465,7 @@ class PipePredictor(object): self.cfg['crop_thresh']) if i > self.warmup_frame: self.pipe_timer.module_time['det'].end() + self.pipe_timer.track_num += len(det_res['boxes']) self.pipeline_res.update(det_res, 'det') if self.with_human_attr: diff --git a/deploy/pipeline/ppvehicle/vehicle_plateutils.py b/deploy/pipeline/ppvehicle/vehicle_plateutils.py index 4a7b792da37de9a1f9e394452cdd11a91b0a9b47..8a93c945cb95ea4b456b1be19572a717ca61150d 100644 --- a/deploy/pipeline/ppvehicle/vehicle_plateutils.py +++ b/deploy/pipeline/ppvehicle/vehicle_plateutils.py @@ -139,6 +139,7 @@ def create_predictor(args, cfg, mode): min_input_shape = {"x": [1, 3, imgH, 10]} max_input_shape = {"x": [batch_size, 3, imgH, 2304]} opt_input_shape = {"x": [batch_size, 3, imgH, 320]} + config.exp_disable_tensorrt_ops(["transpose2"]) elif mode == "cls": min_input_shape = {"x": [1, 3, 48, 10]} max_input_shape = {"x": [batch_size, 3, 48, 1024]} diff --git a/docs/images/pphumanv2.png b/docs/images/pphumanv2.png index 829dd60865b18b211e97e6a1c405dc2ee3d24c4b..87b896ed0e5f973497aaedb7cf2042d004e080c3 100644 Binary files a/docs/images/pphumanv2.png and b/docs/images/pphumanv2.png differ diff --git a/docs/images/ppvehicle.png b/docs/images/ppvehicle.png new file mode 100644 index 0000000000000000000000000000000000000000..87176c313e7af184225e0f09001be53955b98f7a Binary files /dev/null and b/docs/images/ppvehicle.png differ