From adc7c7085d5d9c3d1f0f03345d0955ec68832bb0 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Thu, 15 Sep 2022 20:14:17 +0800 Subject: [PATCH] fix small typos in docs, cherry pick from develop --- docs/en/PPShiTu/PPShiTuV2_introduction.md | 2 +- .../feature_extraction_en.md | 6 +++--- docs/zh_CN/PPShiTu/PPShiTuV2_introduction.md | 10 +++++----- .../image_recognition_pipeline/feature_extraction.md | 8 ++++---- docs/zh_CN/inference_deployment/python_deploy.md | 2 +- 5 files changed, 14 insertions(+), 14 deletions(-) diff --git a/docs/en/PPShiTu/PPShiTuV2_introduction.md b/docs/en/PPShiTu/PPShiTuV2_introduction.md index bae44aea..e420bd71 100644 --- a/docs/en/PPShiTu/PPShiTuV2_introduction.md +++ b/docs/en/PPShiTu/PPShiTuV2_introduction.md @@ -38,7 +38,7 @@ The following table lists the relevant metric obtained by PP-ShiTuV2 with compar | :--------- | :------------------------------------------------ | :------- | | | | recall@1 | | PP-ShiTuV1 | 64(30+34)MB | 66.8% | -| PP-ShiTuV2 | 49(30+19) | 73.8% | +| PP-ShiTuV2 | 49(30+19)MB | 73.8% | **Note:** - For the introduction of recall and mAP metric, please refer to [Retrieval Metric](../algorithm_introduction/reid.md). diff --git a/docs/en/image_recognition_pipeline/feature_extraction_en.md b/docs/en/image_recognition_pipeline/feature_extraction_en.md index 26216543..856d4753 100644 --- a/docs/en/image_recognition_pipeline/feature_extraction_en.md +++ b/docs/en/image_recognition_pipeline/feature_extraction_en.md @@ -176,14 +176,14 @@ Model training mainly includes the starting training and restoring training from **Notice:** The online evaluation method is used by default in the configuration file. If you want to speed up the training, you can turn off the online evaluation function, just add `-o Global.eval_during_train=False` after the above scripts. -After training, the final model files `latest.pdparams`, `best_model.pdarams` and the training log file `train.log` will be generated in the output directory. Among them, `best_model` saves the best model under the current evaluation index, and `latest` is used to save the latest generated model, which is convenient to resume training from the checkpoint when training task is interrupted. Training can be resumed from a checkpoint by adding `-o Global.checkpoint="path_to_resume_checkpoint"` to the end of the above training scripts, as shown below. +After training, the final model files `latest.pdparams`, `best_model.pdarams` and the training log file `train.log` will be generated in the output directory. Among them, `best_model` saves the best model under the current evaluation index, and `latest` is used to save the latest generated model, which is convenient to resume training from the checkpoint when training task is interrupted. Training can be resumed from a checkpoint by adding `-o Global.checkpoints="path_to_resume_checkpoint"` to the end of the above training scripts, as shown below. - Single machine and single card checkpoint recovery training ```shell export CUDA_VISIBLE_DEVICES=0 python3.7 tools/train.py \ -c ./ppcls/configs/GeneralRecognitionV2/GeneralRecognitionV2_PPLCNetV2_base.yaml \ - -o Global.checkpoint="output/RecModel/latest" + -o Global.checkpoints="output/RecModel/latest" ``` - Single-machine multi-card checkpoint recovery training ```shell @@ -191,7 +191,7 @@ After training, the final model files `latest.pdparams`, `best_model.pdarams` an python3.7 -m paddle.distributed.launch --gpus="0,1,2,3" \ tools/train.py \ -c ./ppcls/configs/GeneralRecognitionV2/GeneralRecognitionV2_PPLCNetV2_base.yaml \ - -o Global.checkpoint="output/RecModel/latest" + -o Global.checkpoints="output/RecModel/latest" ``` diff --git a/docs/zh_CN/PPShiTu/PPShiTuV2_introduction.md b/docs/zh_CN/PPShiTu/PPShiTuV2_introduction.md index bac4f8ac..7e819efa 100644 --- a/docs/zh_CN/PPShiTu/PPShiTuV2_introduction.md +++ b/docs/zh_CN/PPShiTu/PPShiTuV2_introduction.md @@ -40,7 +40,7 @@ PP-ShiTuV2 是基于 PP-ShiTuV1 改进的一个实用轻量级通用图像识别 | :--------- | :---------------------- | :------------------ | | | | recall@1 | | PP-ShiTuV1 | 64(30+34)MB | 66.8% | -| PP-ShiTuV2 | 49(30+19) | 73.8% | +| PP-ShiTuV2 | 49(30+19)MB | 73.8% | **注：** - recall及mAP指标的介绍可以参考 [常用指标](../algorithm_introduction/reid.md#22-常用指标)。 @@ -70,10 +70,10 @@ PP-ShiTuV2 是基于 PP-ShiTuV1 改进的一个实用轻量级通用图像识别 ```shell # 如果您的机器安装的是 CUDA9 或 CUDA10，请运行以下命令安装 python3.7 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple - + # 如果您的机器是CPU，请运行以下命令安装 python3.7 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple - + # 安装 faiss 库 python3.7 -m pip install faiss-cpu==1.7.1post2 ``` @@ -82,7 +82,7 @@ PP-ShiTuV2 是基于 PP-ShiTuV1 改进的一个实用轻量级通用图像识别 ```shell # 进入到PaddleClas根目录下 cd PaddleClas - + # 安装paddleclas python3.7 setup.py install ``` @@ -92,7 +92,7 @@ PP-ShiTuV2 是基于 PP-ShiTuV1 改进的一个实用轻量级通用图像识别 ```shell # 下载并解压demo数据 wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v2.0.tar && tar -xf drink_dataset_v2.0.tar - + # 执行识别命令 paddleclas \ --model_name=PP-ShiTuV2 \ diff --git a/docs/zh_CN/image_recognition_pipeline/feature_extraction.md b/docs/zh_CN/image_recognition_pipeline/feature_extraction.md index f037dc25..a2107fa6 100644 --- a/docs/zh_CN/image_recognition_pipeline/feature_extraction.md +++ b/docs/zh_CN/image_recognition_pipeline/feature_extraction.md @@ -38,7 +38,7 @@ - **Backbone**: 用于提取输入图像初步特征的骨干网络，一般由配置文件中的 [Backbone](../../../ppcls/configs/GeneralRecognitionV2/GeneralRecognitionV2_PPLCNetV2_base.yaml#L33-L37) 以及 [BackboneStopLayer](../../../ppcls/configs/GeneralRecognitionV2/GeneralRecognitionV2_PPLCNetV2_base.yaml#L38-L39) 字段共同指定。 - **Neck**: 用以特征增强及特征维度变换。可以是一个简单的 FC Layer，用来做特征维度变换；也可以是较复杂的 FPN 结构，用以做特征增强，一般由配置文件中的 [Neck](../../../ppcls/configs/GeneralRecognitionV2/GeneralRecognitionV2_PPLCNetV2_base.yaml#L40-L51) 字段指定。 -- **Head**: 用来将 `Neck` 的输出 feature 转化为 logits，让模型在训练阶段能以分类任务的形式进行训练。除了常用的 FC Layer 外，还可以替换为 [CosMargin](../../../ppcls/arch/gears/cosmargin.py), [ArcMargin](../../../ppcls/arch/gears/arcmargin.py), [CircleMargin](../../../ppcls/arch/gears/circlemargin.py) 等模块，一般由配置文件中的 [Head](`../../../ppcls/configs/GeneralRecognitionV2/GeneralRecognitionV2_PPLCNetV2_base.yaml#L52-L60) 字段指定。 +- **Head**: 用来将 `Neck` 的输出 feature 转化为 logits，让模型在训练阶段能以分类任务的形式进行训练。除了常用的 FC Layer 外，还可以替换为 [CosMargin](../../../ppcls/arch/gears/cosmargin.py), [ArcMargin](../../../ppcls/arch/gears/arcmargin.py), [CircleMargin](../../../ppcls/arch/gears/circlemargin.py) 等模块，一般由配置文件中的 [Head](../../../ppcls/configs/GeneralRecognitionV2/GeneralRecognitionV2_PPLCNetV2_base.yaml#L52) 字段指定。 - **Loss**: 指定所使用的 Loss 函数。我们将 Loss 设计为组合 loss 的形式，可以方便地将 Classification Loss 和 Metric learning Loss 组合在一起，一般由配置文件中的 [Loss](../../../ppcls/configs/GeneralRecognitionV2/GeneralRecognitionV2_PPLCNetV2_base.yaml#L63-L77) 字段指定。 @@ -185,14 +185,14 @@ Loss 部分选用 [Cross entropy loss](../../../ppcls/loss/celoss.py) 和 [Tripl **注意：** 配置文件中默认采用`在线评估`的方式，如果你想加快训练速度，可以关闭`在线评估`功能，只需要在上述命令的后面，增加 `-o Global.eval_during_train=False`。 -训练完毕后，在 output 目录下会生成最终模型文件 `latest.pdparams`，`best_model.pdarams` 和训练日志文件 `train.log`。其中，`best_model` 保存了当前评测指标下的最佳模型，`latest` 用来保存最新生成的模型, 方便在任务中断的情况下从断点位置恢复训练。通过在上述训练命令的末尾加上`-o Global.checkpoint="path_to_resume_checkpoint"`即可从断点恢复训练，示例如下。 +训练完毕后，在 output 目录下会生成最终模型文件 `latest.pdparams`，`best_model.pdarams` 和训练日志文件 `train.log`。其中，`best_model` 保存了当前评测指标下的最佳模型，`latest` 用来保存最新生成的模型, 方便在任务中断的情况下从断点位置恢复训练。通过在上述训练命令的末尾加上`-o Global.checkpoints="path_to_resume_checkpoint"`即可从断点恢复训练，示例如下。 - 单机单卡断点恢复训练 ```shell export CUDA_VISIBLE_DEVICES=0 python3.7 tools/train.py \ -c ./ppcls/configs/GeneralRecognitionV2/GeneralRecognitionV2_PPLCNetV2_base.yaml \ - -o Global.checkpoint="output/RecModel/latest" + -o Global.checkpoints="output/RecModel/latest" ``` - 单机多卡断点恢复训练 ```shell @@ -200,7 +200,7 @@ Loss 部分选用 [Cross entropy loss](../../../ppcls/loss/celoss.py) 和 [Tripl python3.7 -m paddle.distributed.launch --gpus="0,1,2,3" \ tools/train.py \ -c ./ppcls/configs/GeneralRecognitionV2/GeneralRecognitionV2_PPLCNetV2_base.yaml \ - -o Global.checkpoint="output/RecModel/latest" + -o Global.checkpoints="output/RecModel/latest" ``` diff --git a/docs/zh_CN/inference_deployment/python_deploy.md b/docs/zh_CN/inference_deployment/python_deploy.md index 06b3b670..e432326b 100644 --- a/docs/zh_CN/inference_deployment/python_deploy.md +++ b/docs/zh_CN/inference_deployment/python_deploy.md @@ -103,7 +103,7 @@ python3.7 python/predict_rec.py -c configs/inference_rec.yaml 上述预测命令可以得到一个 512 维的特征向量，直接输出在在命令行中。 -在配置文件 `configs/inference_det.yaml` 中有以下字段用于配置预测参数： +在配置文件 `configs/inference_rec.yaml` 中有以下字段用于配置预测参数： * `Global.infer_imgs`：待预测的图片文件路径； * `Global.use_gpu`： 是否使用 GPU 预测，默认为 `True`。 -- GitLab