introduction_cn.ipynb

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   "source": [
    "## 1. PP-HGNet 模型简介\n",
    "PP-HGNet(High Performance GPU Net) 是百度飞桨视觉团队自研的更适用于 GPU 平台的高性能骨干网络，该网络在 VOVNet 的基础上使用了可学习的下采样层（LDS Layer），融合了 ResNet_vd、PPHGNet 等模型的优点，该模型在 GPU 平台上与其他 SOTA 模型在相同的速度下有着更高的精度。在同等速度下，该模型高于 ResNet34-D 模型 3.8 个百分点，高于 ResNet50-D 模型 2.4 个百分点，在使用百度自研 SSLD 蒸馏策略后，超越 ResNet50-D 模型 4.7 个百分点。与此同时，在相同精度下，其推理速度也远超主流 VisionTransformer 的推理速度。\n",
    "\n",
    "PP-HGNet 系列模型基于 PaddleClas 开发、训练，更多关于 PP-HGNet 系列模型的更多信息，可以从 [PaddleClas-PPHGNet](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.5/docs/zh_CN/models/ImageNet1k/PP-HGNet.md) 获取。"
   ]
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   "source": [
    "## 2. 模型效果\n",
    "\n",
    "PP-HGNet 系列模型在 ImageNet1k 数据集上的评测指标如下所示：\n",
    "\n",
    "| Model | Top-1 Acc(\\%) | Top-5 Acc(\\%) | Latency(ms) |\n",
    "|:--: |:--: |:--: |:--: | :--: |:--: |\n",
    "| PPHGNet_tiny      | 79.83 | 95.04 | 1.77 |\n",
    "| PPHGNet_tiny_ssld  | 81.95 | 96.12 | 1.77 |\n",
    "| PPHGNet_small     | 81.51| 95.82 | 2.52  |\n",
    "| PPHGNet_small_ssld | 83.82| 96.81 | 2.52  |\n",
    "| PPHGNet_base_ssld | 85.00| 97.35 | 5.97   |\n",
    "\n",
    "其中 `_ssld` 表示使用 `SSLD 蒸馏`后的模型。关于 `SSLD蒸馏` 的内容，详情 [SSLD 蒸馏](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.5/docs/zh_CN/training/advanced/knowledge_distillation.md)。\n",
    "\n",
    "PP-HGNet 与其他模型的比较如下，其中测试机器为 NVIDIA® Tesla® V100，开启 TensorRT 引擎，精度类型为 FP32。在相同速度下，PP-HGNet 精度均超越了其他 SOTA CNN 模型，在与 SwinTransformer 模型的比较中，在更高精度的同时，速度快 2 倍以上。\n",
    "\n",
    "| Model | Top-1 Acc(\\%) | Top-5 Acc(\\%) | Latency(ms) |\n",
    "|:--: |:--: |:--: |:--: |\n",
    "| ResNet34                 | 74.57      | 92.14       | 1.97        |\n",
    "| ResNet34_vd              | 75.98      | 92.98       | 2.00        |\n",
    "| EfficientNetB0           | 77.38      | 93.31       | 1.96        |\n",
    "| <b>PPHGNet_tiny<b>       | <b>79.83<b> | <b>95.04<b> | <b>1.77<b> |\n",
    "| <b>PPHGNet_tiny_ssld<b>  | <b>81.95<b> | <b>96.12<b> | <b>1.77<b> |\n",
    "| ResNet50                 | 76.50      | 93.00       | 2.54        |\n",
    "| ResNet50_vd              | 79.12      | 94.44       | 2.60        |\n",
    "| ResNet50_rsb             | 80.40      |         |     2.54        |\n",
    "| EfficientNetB1           | 79.15      | 94.41       | 2.88        |\n",
    "| SwinTransformer_tiny     | 81.2      | 95.5       | 6.59        |\n",
    "| <b>PPHGNet_small<b>      | <b>81.51<b>| <b>95.82<b> | <b>2.52<b>  |\n",
    "| <b>PPHGNet_small_ssld<b> | <b>83.82<b>| <b>96.81<b> | <b>2.52<b>  |\n",
    "| Res2Net200_vd_26w_4s_ssld| 85.13      | 97.42       | 11.45       |\n",
    "| ResNeXt101_32x48d_wsl    | 85.37      | 97.69       | 55.07       |\n",
    "| SwinTransformer_base     | 85.2       | 97.5        | 13.53       |  \n",
    "| <b>PPHGNet_base_ssld<b> | <b>85.00<b>| <b>97.35<b> | <b>5.97<b>   |"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## 3. 模型快速使用\n",
    "\n",
    "### 3.1 模型推理：\n",
    "* 安装相关 Python 包\n",
    "\n",
    "（不在Jupyter Notebook上运行时需要将\"!\"去掉。）\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果您的机器安装了 CUDA9、CUDA10 或 CUDA11，请运行以下命令安装 paddle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install paddlepaddle-gpu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "您的机器是CPU，请运行以下命令安装 paddle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install paddlepaddle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "安装 paddleclas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "tags": []
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   "source": [
    "!pip install paddleclas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 快速体验\n",
    "\n",
    "恭喜！ 您已经成功安装了 PaddleClas，接下来快速体验图像分类效果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "!wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.5/docs/images/inference_deployment/whl_demo.jpg\n",
    "!paddleclas --model_name=PPHGNet_small  --infer_imgs=\"./whl_demo.jpg\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上述命令的运行结果如下所示：\n",
    "\n",
    "class_ids: [8, 7, 86, 82, 81], scores: [0.71479, 0.08682, 0.00806, 0.0023, 0.00121], label_names: ['hen', 'cock', 'partridge', 'ruffed grouse, partridge, Bonasa umbellus', 'ptarmigan'], filename: docs/images/inference_deployment/whl_demo.jpg\n",
    "Predict complete!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 模型训练\n",
    "* PP-HGNet 系列模型基于 PaddleClas 实现，模型训练的具体信息请参考[模型训练、评估和预测](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.5/docs/zh_CN/models/ImageNet1k/PP-HGNet.md#3-%E6%A8%A1%E5%9E%8B%E8%AE%AD%E7%BB%83%E8%AF%84%E4%BC%B0%E5%92%8C%E9%A2%84%E6%B5%8B)。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 模型原理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PP-HGNet 作者针对 GPU 设备，对目前 GPU 友好的网络做了分析和归纳，尽可能多的使用 3x3 标准卷积（计算密度最高）。在此将 VOVNet 作为基准模型，将主要的有利于 GPU 推理的改进点进行融合。从而得到一个有利于 GPU 推理的骨干网络，同样速度下，精度大幅超越其他 CNN 或者 VisionTransformer 模型。\n",
    "\n",
    "PP-HGNet 骨干网络的整体结构如下：\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"https://user-images.githubusercontent.com/12560511/200782647-93cec23d-67fe-4585-bfcf-899dc65c7845.png\"  width = \"80%\"  />\n",
    "</div>\n",
    "\n",
    "其中，PP-HGNet是由多个HG-Block组成，HG-Block的细节如下：\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"https://user-images.githubusercontent.com/12560511/201665345-1182b902-29f2-43a4-bc9b-c3473769b025.png\"  width = \"80%\"  />\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 注意事项\n",
    "PP-HGNet 系列模型的推理延时评测基于 NVIDIA® Tesla® V100 硬件平台进行，并且开启 TensorRT 引擎，精度类型为 FP32。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. 相关论文以及引用信息"
   ]
  }
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