From 5ea613dc4fa4e4e73bb17e1b6001fb476ecb6a1e Mon Sep 17 00:00:00 2001 From: tink2123 Date: Thu, 25 Mar 2021 11:02:58 +0800 Subject: [PATCH] use pretrained_model for eval --- doc/doc_ch/detection.md | 4 ++-- doc/doc_ch/recognition.md | 6 +++--- doc/doc_en/detection_en.md | 4 ++-- doc/doc_en/recognition_en.md | 6 +++--- 4 files changed, 10 insertions(+), 10 deletions(-) diff --git a/doc/doc_ch/detection.md b/doc/doc_ch/detection.md index a8dee65a..671fda99 100644 --- a/doc/doc_ch/detection.md +++ b/doc/doc_ch/detection.md @@ -108,9 +108,9 @@ PaddleOCR计算三个OCR检测相关的指标，分别是：Precision、Recall 运行如下代码，根据配置文件`det_db_mv3.yml`中`save_res_path`指定的测试集检测结果文件，计算评估指标。 评估时设置后处理参数`box_thresh=0.5`，`unclip_ratio=1.5`，使用不同数据集、不同模型训练，可调整这两个参数进行优化 -训练中模型参数默认保存在`Global.save_model_dir`目录下。在评估指标时，需要设置`Global.checkpoints`指向保存的参数文件。 +训练中模型参数默认保存在`Global.save_model_dir`目录下。在评估指标时，需要设置`Global.pretrained_model`指向保存的参数文件。 ```shell -python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="{path/to/weights}/best_accuracy" PostProcess.box_thresh=0.5 PostProcess.unclip_ratio=1.5 +python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model="{path/to/weights}/best_accuracy" PostProcess.box_thresh=0.5 PostProcess.unclip_ratio=1.5 ``` diff --git a/doc/doc_ch/recognition.md b/doc/doc_ch/recognition.md index 91d64907..4fccf469 100644 --- a/doc/doc_ch/recognition.md +++ b/doc/doc_ch/recognition.md @@ -420,8 +420,8 @@ Eval: 评估数据集可以通过 `configs/rec/rec_icdar15_train.yml` 修改Eval中的 `label_file_path` 设置。 ``` -# GPU 评估， Global.checkpoints 为待测权重 -python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy +# GPU 评估， Global.pretrained_model 为待测权重 +python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.pretrained_model={path/to/weights}/best_accuracy ``` @@ -432,7 +432,7 @@ python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec 使用 PaddleOCR 训练好的模型，可以通过以下脚本进行快速预测。 -默认预测图片存储在 `infer_img` 里，通过 `-o Global.checkpoints` 指定权重： +默认预测图片存储在 `infer_img` 里，通过 `-o Global.pretrained_model` 指定权重： ``` # 预测英文结果 diff --git a/doc/doc_en/detection_en.md b/doc/doc_en/detection_en.md index 3ee9092c..897f5b3b 100644 --- a/doc/doc_en/detection_en.md +++ b/doc/doc_en/detection_en.md @@ -101,9 +101,9 @@ Run the following code to calculate the evaluation indicators. The result will b When evaluating, set post-processing parameters `box_thresh=0.6`, `unclip_ratio=1.5`. If you use different datasets, different models for training, these two parameters should be adjusted for better result. -The model parameters during training are saved in the `Global.save_model_dir` directory by default. When evaluating indicators, you need to set `Global.checkpoints` to point to the saved parameter file. +The model parameters during training are saved in the `Global.save_model_dir` directory by default. When evaluating indicators, you need to set `Global.pretrained_model` to point to the saved parameter file. ```shell -python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="{path/to/weights}/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5 +python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model="{path/to/weights}/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5 ``` diff --git a/doc/doc_en/recognition_en.md b/doc/doc_en/recognition_en.md index 14ddcc75..05bb8aca 100644 --- a/doc/doc_en/recognition_en.md +++ b/doc/doc_en/recognition_en.md @@ -425,8 +425,8 @@ Eval: The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/rec/rec_icdar15_train.yml` file. ``` -# GPU evaluation, Global.checkpoints is the weight to be tested -python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy +# GPU evaluation, Global.pretrained_model is the weight to be tested +python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.pretrained_model={path/to/weights}/best_accuracy ``` @@ -437,7 +437,7 @@ python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec Using the model trained by paddleocr, you can quickly get prediction through the following script. -The default prediction picture is stored in `infer_img`, and the weight is specified via `-o Global.checkpoints`: +The default prediction picture is stored in `infer_img`, and the weight is specified via `-o Global.pretrained_model`: ``` # Predict English results -- GitLab