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+## 1. Inference Benchmark
+
+### 1.1 Environment
+
+* Segmentation accuracy (mIoU): We tested the models on PP-HumanSeg-14K dataset using the best input shape, without tricks like multi-scale and flip.
+* Inference latency on Snapdragon 855 ARM CPU: We tested the models on xiaomi9 (Snapdragon 855 CPU) using PaddleLite, with single thread, large kernel and best input shape.
+* Inference latency on Intel CPU: We tested the models on Intel(R) Xeon(R) Gold 6271C CPU using Paddle Inference.
+
+### 1.2 Datasets
+
+* PP-HumanSeg14k[(paper)](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/contrib/PP-HumanSeg/paper.md), proposed by PaddleSeg team, is used for testing.
+
+### 1.3 Benchmark
+
+Inference speed is tested on Snapdragon 855 ARM CPU for each SOTA model.
+
+Model Name | Input Shape | mIou(%) | Speed(FPS)
+---|---|---|---
+PortraitNet | 224x224 | 95.20 |31.93
+SINet | 224x224 | 93.76 | 78.12
+PP-HumanSegV2 | 224x224 | 95.21 | 52.27
+PP-HumanSegV2 | 192x192 | 94.87 | 81.96
+
+Inference speed is tested on Arm CPU for model solutions.
+
+Model Name | Input Shape | mIoU(%) | Inference Speed on Arm CPU(FPS)
+---|---|---|---
+PP-HumanSegV1 | 398x224 | 93.60 | 33.69
+PP-HumanSegV2 | 398x224 | 97.50 | 35.23
+PP-HumanSegV2 | 256x144 | 96.63 | 63.05
+
+Inference speed is tested on Intel CPU for model solutions.
+
+Model Name | Input Shape | mIoU(%) | Inference Speed on Intel CPU(FPS)
+---|---|---|---
+PP-HumanSegV1 | 398x224 | 93.60 | 36.02
+PP-HumanSegV2 | 398x224 | 97.50 | 60.75
+PP-HumanSegV2 | 256x144 | 96.63 | 70.67
+
+
+## 2. Reference
+Ref: https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/contrib/PP-HumanSeg
diff --git a/modelcenter/PP-HumanSegV2/download_en.md b/modelcenter/PP-HumanSegV2/download_en.md
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+# Model List
+
+## 1 Portrait Segmentation Models
+
+Self-developed portrait segmentation models are released for real-time segmentation applications such as mobile phone video calls and web video conferences. These models can be directly integrated into products at zero cost.
+
+| Model Name | Best Input Shape | mIou(%) | Inference Time on Phone(ms) | Modle Size(MB) | Config File | Links |
+| --- | --- | --- | ---| --- | --- | --- |
+| PP-HumanSegV1-Lite | 398x224 | 93.60 | 29.68 | 2.3 | [cfg](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/contrib/PP-HumanSeg/configs/portrait_pp_humansegv1_lite.yml) | [Checkpoint](https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/portrait_pp_humansegv1_lite_398x224_pretrained.zip) \| [Inference Model (Argmax)](https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/portrait_pp_humansegv1_lite_398x224_inference_model.zip) \| [Inference Model (Softmax)](https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/portrait_pp_humansegv1_lite_398x224_inference_model_with_softmax.zip) |
+| PP-HumanSegV2-Lite | 256x144 | 96.63 | 15.86 | 5.4 | [cfg](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/contrib/PP-HumanSeg/configs/portrait_pp_humansegv2_lite.yml) | [Checkpoint](https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/portrait_pp_humansegv2_lite_256x144_smaller/portrait_pp_humansegv2_lite_256x144_pretrained.zip) \| [Inference Model (Argmax)](https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/portrait_pp_humansegv2_lite_256x144_smaller/portrait_pp_humansegv2_lite_256x144_inference_model.zip) \| [Inference Model (Softmax)](https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/portrait_pp_humansegv2_lite_256x144_smaller/portrait_pp_humansegv2_lite_256x144_inference_model_with_softmax.zip) |
+
+Note:
+
+* Segmentation accuracy (mIoU): We tested the models on [PP-HumanSeg-14K](https://github.com/PaddlePaddle/models/pull/5583/paper.md) dataset using the best input shape, without tricks like multi-scale and flip.
+* Inference latency on Snapdragon 855 ARM CPU: We tested the models on xiaomi9 (Snapdragon 855 CPU) using [PaddleLite](https://www.paddlepaddle.org.cn/lite), with single thread, large kernel and best input shape.
+* For the best input shape, the ratio of width and height is 16:9, which is the same as the camera of mobile phone and laptop.
+* The checkpoint is the pretrained weight, which can be used for finetune together with the config file.
+* Inference model is used for deployment.
+* Inference Model (Argmax): The last operation of inference model is argmax, so the output is a single-channel image (int64), where the portrait area is 1, and the background area is 0.
+* Inference Model (Softmax): The last operation of inference model is softmax, so the output is a single-channel image(float32), where each pixel represents the probability of portrait area.
+Usage:
+
+* Portrait segmentation model can be directly integrated into products at zero cost. Best input shape is suggested.
+* For mobile phone, screen can be horizontal or vertical. Before fed into model, the image should be rotated to keep the portrait always vertical.
+Note:
+
+* Segmentation accuracy (mIoU): After training the models on large human segmentation dataset, we test these models on small Supervisely Person dataset ([url](https://paddleseg.bj.bcebos.com/humanseg/data/mini_supervisely.zip)).
+* Inference latency on Snapdragon 855 ARM CPU: We tested the models on xiaomi9 (Snapdragon 855 CPU) using [PaddleLite](https://www.paddlepaddle.org.cn/lite), with single thread, large kernel and best input shape.
+* Inference latency on server: We tested the models on Nvidia V100 GPU using Paddle Inference, with TensorRT and best input shape.
+* The checkpoint is the pretrained weight, which can be used for finetune together with the config file.
+* Inference model is used for deployment.
+* Inference Model (Argmax): The last operation of inference model is argmax, so the output is a single-channel image (int64), where the portrait area is 1, and the background area is 0.
+* Inference Model (Softmax): The last operation of inference model is softmax, so the output is a single-channel image(float32), where each pixel represents the probability of portrait area.
+Usage:
+
+* Since the images for general human segmentation are various, you should evaluate the released models according to the actual scene.
+* If the segmentation accuracy is not satisfied, you are suggested to collect more data, annotate them and finetune the model.
+\n",
+ "
![](\"https://user-images.githubusercontent.com/48357642/200734740-72e98c73-5b41-47c4-b208-7fd9c10d8b8a.jpeg\")
\n",
+ "
\n",
+ "
![](\"https://user-images.githubusercontent.com/48357642/200735017-eb8a2b22-7ef9-4e4f-acc2-1ea538672f75.jpeg\")
\n",
+ "
![](\"https://user-images.githubusercontent.com/52520497/188776878-130f4f6a-6379-4fb0-87e4-9a7ee4707c1d.jpg\")
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f6aeb78d-63ff-4e01-aa50-da37522e0b08",
+ "metadata": {},
+ "source": [
+ "### 3.2 Model Training\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4da9e355-e99a-4821-9fa2-1571a7b84f97",
+ "metadata": {},
+ "source": [
+ "* Preparation\n",
+ "\n",
+ "With reference to the above, install PaddleSeg, download the dataset, and then download the pretrained weights."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5945d193-bae0-4c5f-bd43-59e205b0484a",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "!python src/download_pretrained_models.py"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d8ba1500-8475-40e0-a257-9ddad436c14d",
+ "metadata": {},
+ "source": [
+ "* Training\n",
+ "\n",
+ "The config files are saved in `./configs` as follows. We have set the path of pretrained weight in all config files.\n",
+ "```\n",
+ "configs\n",
+ "├── human_pp_humansegv1_lite.yml\n",
+ "├── human_pp_humansegv2_lite.yml\n",
+ "├── human_pp_humansegv1_mobile.yml\n",
+ "├── human_pp_humansegv2_mobile.yml\n",
+ "├── human_pp_humansegv1_server.yml\n",
+ "├── portrait_pp_humansegv1_lite.yml\n",
+ "├── portrait_pp_humansegv2_lite.yml\n",
+ "```\n",
+ "\n",
+ "Run the following command to start finetuning. You should change the details, such as learning rate, according to the actual situation. Please refer to [url](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/docs/train/train_cn.md) for more details."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "14461b15-5c88-453a-b877-739ddf0062ce",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "!export CUDA_VISIBLE_DEVICES=0 # Set GPU on Linux\n",
+ "# set CUDA_VISIBLE_DEVICES=0 # Set GPU on Windows\n",
+ "!python ../../train.py \\\n",
+ " --config configs/human_pp_humansegv2_lite.yml \\\n",
+ " --save_dir output/human_pp_humansegv2_lite \\\n",
+ " --save_interval 100 --do_eval --use_vdl"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6c44f772-2104-4379-bd66-d6f9b8d72414",
+ "metadata": {},
+ "source": [
+ "* Evaluation\n",
+ "\n",
+ "Load trained model and start evaluation. Please refer to [url](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/docs/evaluation/evaluate/evaluate_cn.md) for more details."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9ff7c87f-62b4-4377-a7fd-260b8278e6c4",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "!python ../../val.py \\\n",
+ " --config configs/human_pp_humansegv2_lite.yml \\\n",
+ " --model_path pretrained_models/human_pp_humansegv2_lite_192x192_pretrained/model.pdparams"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e346d381-15df-421f-8473-9e96e39bbd0c",
+ "metadata": {},
+ "source": [
+ "* Prediction\n",
+ "\n",
+ "Load trained model and start prediction. The result are saved in `./data/images_result/added_prediction` and `./data/images_result/pseudo_color_prediction` "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b5b53557-9dbe-472d-8406-c60c2c58927d",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "!python ../../predict.py \\\n",
+ " --config configs/human_pp_humansegv2_lite.yml \\\n",
+ " --model_path pretrained_models/human_pp_humansegv2_lite_192x192_pretrained/model.pdparams \\\n",
+ " --image_path data/images/human.jpg \\\n",
+ " --save_dir ./data/images_result"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d98d8455-6e84-493a-b823-428aab76e932",
+ "metadata": {},
+ "source": [
+ "* Export\n",
+ "\n",
+ "Load trained model and export it. Please refer to [url](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/docs/model_export_cn.md) for more details."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8fe76c54-aafe-43e0-a0cc-b8feb49bb770",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "!python ../../export.py \\\n",
+ " --config configs/human_pp_humansegv2_lite.yml \\\n",
+ " --model_path pretrained_models/human_pp_humansegv2_lite_192x192_pretrained/model.pdparams \\\n",
+ " --save_dir output/human_pp_humansegv2_lite \\\n",
+ " --without_argmax \\\n",
+ " --with_softmax"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "629a50ee-3e33-42a5-b505-aacc4ccd220f",
+ "metadata": {},
+ "source": [
+ "When set --without_argmax --with_softmax, the last operation of inference model will be changed from argmax to softmax, which outputs an image of type float32, representing the probability of foreground, and sometimes it can be useful to make the edges smoother and more natural."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6a6fd1d6-a7a5-4388-b225-86b62ab72d67",
+ "metadata": {},
+ "source": [
+ "## 4. Model Principles\n",
+ "\n",
+ "The model structure.\n",
+ "\n",
+ "
![](\"https://user-images.githubusercontent.com/48357642/200757494-1e63215e-4cd1-4c39-8dd9-a0e37f8719f2.png\")
\n",
+ "
\n",
+ "
![](\"https://user-images.githubusercontent.com/48357642/200758284-a8d5e6f9-1a66-414b-804c-57c6b6fcd698.png\")
\n",
+ "