From f2bc513a68c9b5fd3be29f2b2cd6d18e4b806b06 Mon Sep 17 00:00:00 2001 From: root Date: Mon, 1 Feb 2021 09:31:17 +0000 Subject: [PATCH] delete slim related content --- deploy/slim/quantization/README.md | 61 --------- deploy/slim/quantization/README_en.md | 68 ---------- deploy/slim/quantization/export_model.py | 118 ---------------- deploy/slim/quantization/quant.py | 166 ----------------------- doc/doc_ch/models_list.md | 6 - doc/doc_en/models_list_en.md | 5 - 6 files changed, 424 deletions(-) delete mode 100644 deploy/slim/quantization/README.md delete mode 100644 deploy/slim/quantization/README_en.md delete mode 100755 deploy/slim/quantization/export_model.py delete mode 100755 deploy/slim/quantization/quant.py mode change 100644 => 100755 doc/doc_ch/models_list.md mode change 100644 => 100755 doc/doc_en/models_list_en.md diff --git a/deploy/slim/quantization/README.md b/deploy/slim/quantization/README.md deleted file mode 100644 index ccd4d06b..00000000 --- a/deploy/slim/quantization/README.md +++ /dev/null @@ -1,61 +0,0 @@ - -## 介绍 -复杂的模型有利于提高模型的性能，但也导致模型中存在一定冗余，模型量化将全精度缩减到定点数减少这种冗余，达到减少模型计算复杂度，提高模型推理性能的目的。 -模型量化可以在基本不损失模型的精度的情况下，将FP32精度的模型参数转换为Int8精度，减小模型参数大小并加速计算，使用量化后的模型在移动端等部署时更具备速度优势。 - -本教程将介绍如何使用飞桨模型压缩库PaddleSlim做PaddleOCR模型的压缩。 -[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim) 集成了模型剪枝、量化（包括量化训练和离线量化）、蒸馏和神经网络搜索等多种业界常用且领先的模型压缩功能，如果您感兴趣，可以关注并了解。 - -在开始本教程之前，建议先了解[PaddleOCR模型的训练方法](../../../doc/doc_ch/quickstart.md)以及[PaddleSlim](https://paddleslim.readthedocs.io/zh_CN/latest/index.html) - - -## 快速开始 -量化多适用于轻量模型在移动端的部署，当训练出一个模型后，如果希望进一步的压缩模型大小并加速预测，可使用量化的方法压缩模型。 - -模型量化主要包括五个步骤： -1. 安装 PaddleSlim -2. 准备训练好的模型 -3. 量化训练 -4. 导出量化推理模型 -5. 量化模型预测部署 - -### 1. 安装PaddleSlim - -```bash -git clone https://github.com/PaddlePaddle/PaddleSlim.git -cd Paddleslim -python setup.py install -``` - -### 2. 准备训练好的模型 - -PaddleOCR提供了一系列训练好的[模型](../../../doc/doc_ch/models_list.md)，如果待量化的模型不在列表中，需要按照[常规训练](../../../doc/doc_ch/quickstart.md)方法得到训练好的模型。 - -### 3. 量化训练 -量化训练包括离线量化训练和在线量化训练，在线量化训练效果更好，需加载预训练模型，在定义好量化策略后即可对模型进行量化。 - - -量化训练的代码位于slim/quantization/quant.py 中，比如训练检测模型，训练指令如下： -```bash -python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights='your trained model' Global.save_model_dir=./output/quant_model - -# 比如下载提供的训练模型 -wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar -tar -xf ch_ppocr_mobile_v2.0_det_train.tar -python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_model_dir=./output/quant_model - -``` -如果要训练识别模型的量化，修改配置文件和加载的模型参数即可。 - -### 4. 导出模型 - -在得到量化训练保存的模型后，我们可以将其导出为inference_model，用于预测部署： - -```bash -python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_model_dir=./output/quant_inference_model -``` - -### 5. 量化模型部署 - -上述步骤导出的量化模型，参数精度仍然是FP32，但是参数的数值范围是int8，导出的模型可以通过PaddleLite的opt模型转换工具完成模型转换。 -量化模型部署的可参考 [移动端模型部署](../../lite/readme.md) diff --git a/deploy/slim/quantization/README_en.md b/deploy/slim/quantization/README_en.md deleted file mode 100644 index 7da0b3e7..00000000 --- a/deploy/slim/quantization/README_en.md +++ /dev/null @@ -1,68 +0,0 @@ - -## Introduction - -Generally, a more complex model would achive better performance in the task, but it also leads to some redundancy in the model. -Quantization is a technique that reduces this redundancy by reducing the full precision data to a fixed number, -so as to reduce model calculation complexity and improve model inference performance. - -This example uses PaddleSlim provided [APIs of Quantization](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/) to compress the OCR model. - -It is recommended that you could understand following pages before reading this example： -- [The training strategy of OCR model](../../../doc/doc_en/quickstart_en.md) -- [PaddleSlim Document](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/) - -## Quick Start -Quantization is mostly suitable for the deployment of lightweight models on mobile terminals. -After training, if you want to further compress the model size and accelerate the prediction, you can use quantization methods to compress the model according to the following steps. - -1. Install PaddleSlim -2. Prepare trained model -3. Quantization-Aware Training -4. Export inference model -5. Deploy quantization inference model - - -### 1. Install PaddleSlim - -```bash -git clone https://github.com/PaddlePaddle/PaddleSlim.git -cd Paddleslim -python setup.py install -``` - - -### 2. Download Pretrain Model -PaddleOCR provides a series of trained [models](../../../doc/doc_en/models_list_en.md). -If the model to be quantified is not in the list, you need to follow the [Regular Training](../../../doc/doc_en/quickstart_en.md) method to get the trained model. - - -### 3. Quant-Aware Training -Quantization training includes offline quantization training and online quantization training. -Online quantization training is more effective. It is necessary to load the pre-training model. -After the quantization strategy is defined, the model can be quantified. - -The code for quantization training is located in `slim/quantization/quant.py`. For example, to train a detection model, the training instructions are as follows: -```bash -python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights='your trained model' Global.save_model_dir=./output/quant_model - -# download provided model -wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar -tar -xf ch_ppocr_mobile_v2.0_det_train.tar -python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_model_dir=./output/quant_model - -``` - - -### 4. Export inference model - -After getting the model after pruning and finetuning we, can export it as inference_model for predictive deployment: - -```bash -python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_model_dir=./output/quant_inference_model -``` - -### 5. Deploy -The numerical range of the quantized model parameters derived from the above steps is still FP32, but the numerical range of the parameters is int8. -The derived model can be converted through the `opt tool` of PaddleLite. - -For quantitative model deployment, please refer to [Mobile terminal model deployment](../../lite/readme_en.md) diff --git a/deploy/slim/quantization/export_model.py b/deploy/slim/quantization/export_model.py deleted file mode 100755 index 100b107a..00000000 --- a/deploy/slim/quantization/export_model.py +++ /dev/null @@ -1,118 +0,0 @@ -# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -import sys - -__dir__ = os.path.dirname(os.path.abspath(__file__)) -sys.path.append(__dir__) -sys.path.append(os.path.abspath(os.path.join(__dir__, '..', '..', '..'))) -sys.path.append( - os.path.abspath(os.path.join(__dir__, '..', '..', '..', 'tools'))) - -import argparse - -import paddle -from paddle.jit import to_static - -from ppocr.modeling.architectures import build_model -from ppocr.postprocess import build_post_process -from ppocr.utils.save_load import init_model -from ppocr.utils.logging import get_logger -from tools.program import load_config, merge_config, ArgsParser -from ppocr.metrics import build_metric -import tools.program as program -from paddleslim.dygraph.quant import QAT -from ppocr.data import build_dataloader - - -def main(): - ############################################################################################################ - # 1. quantization configs - ############################################################################################################ - quant_config = { - # weight preprocess type, default is None and no preprocessing is performed. - 'weight_preprocess_type': None, - # activation preprocess type, default is None and no preprocessing is performed. - 'activation_preprocess_type': None, - # weight quantize type, default is 'channel_wise_abs_max' - 'weight_quantize_type': 'channel_wise_abs_max', - # activation quantize type, default is 'moving_average_abs_max' - 'activation_quantize_type': 'moving_average_abs_max', - # weight quantize bit num, default is 8 - 'weight_bits': 8, - # activation quantize bit num, default is 8 - 'activation_bits': 8, - # data type after quantization, such as 'uint8', 'int8', etc. default is 'int8' - 'dtype': 'int8', - # window size for 'range_abs_max' quantization. default is 10000 - 'window_size': 10000, - # The decay coefficient of moving average, default is 0.9 - 'moving_rate': 0.9, - # for dygraph quantization, layers of type in quantizable_layer_type will be quantized - 'quantizable_layer_type': ['Conv2D', 'Linear'], - } - FLAGS = ArgsParser().parse_args() - config = load_config(FLAGS.config) - merge_config(FLAGS.opt) - logger = get_logger() - # build post process - - post_process_class = build_post_process(config['PostProcess'], - config['Global']) - - # build model - # for rec algorithm - if hasattr(post_process_class, 'character'): - char_num = len(getattr(post_process_class, 'character')) - config['Architecture']["Head"]['out_channels'] = char_num - model = build_model(config['Architecture']) - - # get QAT model - quanter = QAT(config=quant_config) - quanter.quantize(model) - - init_model(config, model, logger) - model.eval() - - # build metric - eval_class = build_metric(config['Metric']) - - # build dataloader - valid_dataloader = build_dataloader(config, 'Eval', device, logger) - - # start eval - metirc = program.eval(model, valid_dataloader, post_process_class, - eval_class) - logger.info('metric eval ***************') - for k, v in metirc.items(): - logger.info('{}:{}'.format(k, v)) - - save_path = '{}/inference'.format(config['Global']['save_inference_dir']) - infer_shape = [3, 32, 100] if config['Architecture'][ - 'model_type'] != "det" else [3, 640, 640] - - quanter.save_quantized_model( - model, - save_path, - input_spec=[ - paddle.static.InputSpec( - shape=[None] + infer_shape, dtype='float32') - ]) - logger.info('inference QAT model is saved to {}'.format(save_path)) - - -if __name__ == "__main__": - config, device, logger, vdl_writer = program.preprocess() - main() diff --git a/deploy/slim/quantization/quant.py b/deploy/slim/quantization/quant.py deleted file mode 100755 index 7671e5f8..00000000 --- a/deploy/slim/quantization/quant.py +++ /dev/null @@ -1,166 +0,0 @@ -# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import sys - -__dir__ = os.path.dirname(os.path.abspath(__file__)) -sys.path.append(__dir__) -sys.path.append(os.path.abspath(os.path.join(__dir__, '..', '..', '..'))) -sys.path.append( - os.path.abspath(os.path.join(__dir__, '..', '..', '..', 'tools'))) - -import yaml -import paddle -import paddle.distributed as dist - -paddle.seed(2) - -from ppocr.data import build_dataloader -from ppocr.modeling.architectures import build_model -from ppocr.losses import build_loss -from ppocr.optimizer import build_optimizer -from ppocr.postprocess import build_post_process -from ppocr.metrics import build_metric -from ppocr.utils.save_load import init_model -import tools.program as program -from paddleslim.dygraph.quant import QAT - -dist.get_world_size() - - -class PACT(paddle.nn.Layer): - def __init__(self): - super(PACT, self).__init__() - alpha_attr = paddle.ParamAttr( - name=self.full_name() + ".pact", - initializer=paddle.nn.initializer.Constant(value=20), - learning_rate=1.0, - regularizer=paddle.regularizer.L2Decay(2e-5)) - - self.alpha = self.create_parameter( - shape=[1], attr=alpha_attr, dtype='float32') - - def forward(self, x): - out_left = paddle.nn.functional.relu(x - self.alpha) - out_right = paddle.nn.functional.relu(-self.alpha - x) - x = x - out_left + out_right - return x - - -quant_config = { - # weight preprocess type, default is None and no preprocessing is performed. - 'weight_preprocess_type': None, - # activation preprocess type, default is None and no preprocessing is performed. - 'activation_preprocess_type': None, - # weight quantize type, default is 'channel_wise_abs_max' - 'weight_quantize_type': 'channel_wise_abs_max', - # activation quantize type, default is 'moving_average_abs_max' - 'activation_quantize_type': 'moving_average_abs_max', - # weight quantize bit num, default is 8 - 'weight_bits': 8, - # activation quantize bit num, default is 8 - 'activation_bits': 8, - # data type after quantization, such as 'uint8', 'int8', etc. default is 'int8' - 'dtype': 'int8', - # window size for 'range_abs_max' quantization. default is 10000 - 'window_size': 10000, - # The decay coefficient of moving average, default is 0.9 - 'moving_rate': 0.9, - # for dygraph quantization, layers of type in quantizable_layer_type will be quantized - 'quantizable_layer_type': ['Conv2D', 'Linear'], -} - - -def main(config, device, logger, vdl_writer): - # init dist environment - if config['Global']['distributed']: - dist.init_parallel_env() - - global_config = config['Global'] - - # build dataloader - train_dataloader = build_dataloader(config, 'Train', device, logger) - if config['Eval']: - valid_dataloader = build_dataloader(config, 'Eval', device, logger) - else: - valid_dataloader = None - - # build post process - post_process_class = build_post_process(config['PostProcess'], - global_config) - - # build model - # for rec algorithm - if hasattr(post_process_class, 'character'): - char_num = len(getattr(post_process_class, 'character')) - config['Architecture']["Head"]['out_channels'] = char_num - model = build_model(config['Architecture']) - - # prepare to quant - quanter = QAT(config=quant_config, act_preprocess=PACT) - quanter.quantize(model) - - if config['Global']['distributed']: - model = paddle.DataParallel(model) - - # build loss - loss_class = build_loss(config['Loss']) - - # build optim - optimizer, lr_scheduler = build_optimizer( - config['Optimizer'], - epochs=config['Global']['epoch_num'], - step_each_epoch=len(train_dataloader), - parameters=model.parameters()) - - # build metric - eval_class = build_metric(config['Metric']) - # load pretrain model - pre_best_model_dict = init_model(config, model, logger, optimizer) - - logger.info('train dataloader has {} iters, valid dataloader has {} iters'. - format(len(train_dataloader), len(valid_dataloader))) - # start train - program.train(config, train_dataloader, valid_dataloader, device, model, - loss_class, optimizer, lr_scheduler, post_process_class, - eval_class, pre_best_model_dict, logger, vdl_writer) - - -def test_reader(config, device, logger): - loader = build_dataloader(config, 'Train', device, logger) - import time - starttime = time.time() - count = 0 - try: - for data in loader(): - count += 1 - if count % 1 == 0: - batch_time = time.time() - starttime - starttime = time.time() - logger.info("reader: {}, {}, {}".format( - count, len(data[0]), batch_time)) - except Exception as e: - logger.info(e) - logger.info("finish reader: {}, Success!".format(count)) - - -if __name__ == '__main__': - config, device, logger, vdl_writer = program.preprocess(is_train=True) - main(config, device, logger, vdl_writer) - # test_reader(config, device, logger) diff --git a/doc/doc_ch/models_list.md b/doc/doc_ch/models_list.md old mode 100644 new mode 100755 index fbfb3838..a17ee84e --- a/doc/doc_ch/models_list.md +++ b/doc/doc_ch/models_list.md @@ -14,15 +14,12 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训 |--- | --- | --- | |推理模型|inference.pdmodel、inference.pdiparams|用于python预测引擎推理，[详情](./inference.md)| |训练模型、预训练模型|\*.pdparams、\*.pdopt、\*.states |训练过程中保存的模型的参数、优化器状态和训练中间信息，多用于模型指标评估和恢复训练| -|slim模型|\*.nb|用于lite部署| - ### 一、文本检测模型 |模型名称|模型简介|配置文件|推理模型大小|下载地址| | --- | --- | --- | --- | --- | -|ch_ppocr_mobile_slim_v2.0_det|slim裁剪版超轻量模型，支持中英文、多语种文本检测|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)| |推理模型 (coming soon) / 训练模型 (coming soon)| |ch_ppocr_mobile_v2.0_det|原始超轻量模型，支持中英文、多语种文本检测|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)|3M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)| |ch_ppocr_server_v2.0_det|通用模型，支持中英文、多语种文本检测，比超轻量模型更大，但效果更好|[ch_det_res18_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml)|47M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)| @@ -35,7 +32,6 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训 |模型名称|模型简介|配置文件|推理模型大小|下载地址| | --- | --- | --- | --- | --- | -|ch_ppocr_mobile_slim_v2.0_rec|slim裁剪量化版超轻量模型，支持中英文、数字识别|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)| |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_train.tar) | |ch_ppocr_mobile_v2.0_rec|原始超轻量模型，支持中英文、数字识别|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)|3.71M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) | |ch_ppocr_server_v2.0_rec|通用模型，支持中英文、数字识别|[rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml)|94.8M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) | @@ -46,7 +42,6 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训 |模型名称|模型简介|配置文件|推理模型大小|下载地址| | --- | --- | --- | --- | --- | -|en_number_mobile_slim_v2.0_rec|slim裁剪量化版超轻量模型，支持英文、数字识别|[rec_en_number_lite_train.yml](../../configs/rec/multi_language/rec_en_number_lite_train.yml)| | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/en_number_mobile_v2.0_rec_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/en_number_mobile_v2.0_rec_slim_train.tar) | |en_number_mobile_v2.0_rec|原始超轻量模型，支持英文、数字识别|[rec_en_number_lite_train.yml](../../configs/rec/multi_language/rec_en_number_lite_train.yml)|2.56M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_train.tar) | @@ -123,5 +118,4 @@ python3 generate_multi_language_configs.py -l it \ |模型名称|模型简介|配置文件|推理模型大小|下载地址| | --- | --- | --- | --- | --- | -|ch_ppocr_mobile_slim_v2.0_cls|slim量化版模型|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)| |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) | |ch_ppocr_mobile_v2.0_cls|原始模型|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)|1.38M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | diff --git a/doc/doc_en/models_list_en.md b/doc/doc_en/models_list_en.md old mode 100644 new mode 100755 index 3eb0cd23..18b3f3f0 --- a/doc/doc_en/models_list_en.md +++ b/doc/doc_en/models_list_en.md @@ -14,14 +14,12 @@ The downloadable models provided by PaddleOCR include `inference model`, `traine |--- | --- | --- | |inference model|inference.pdmodel、inference.pdiparams|Used for reasoning based on Python prediction engine，[detail](./inference_en.md)| |trained model, pre-trained model|\*.pdparams、\*.pdopt、\*.states |The checkpoints model saved in the training process, which stores the parameters of the model, mostly used for model evaluation and continuous training.| -|slim model|\*.nb|Generally used for Lite deployment| ### 1. Text Detection Model |model name|description|config|model size|download| | --- | --- | --- | --- | --- | -|ch_ppocr_mobile_slim_v2.0_det|Slim pruned lightweight model, supporting Chinese, English, multilingual text detection|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)| |inference model (coming soon) / slim model (coming soon)| |ch_ppocr_mobile_v2.0_det|Original lightweight model, supporting Chinese, English, multilingual text detection|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)|3M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)| |ch_ppocr_server_v2.0_det|General model, which is larger than the lightweight model, but achieved better performance|[ch_det_res18_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml)|47M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)| @@ -33,7 +31,6 @@ The downloadable models provided by PaddleOCR include `inference model`, `traine |model name|description|config|model size|download| | --- | --- | --- | --- | --- | -|ch_ppocr_mobile_slim_v2.0_rec|Slim pruned and quantized lightweight model, supporting Chinese, English and number recognition|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)| | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_train.tar) | |ch_ppocr_mobile_v2.0_rec|Original lightweight model, supporting Chinese, English and number recognition|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)|3.71M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) | |ch_ppocr_server_v2.0_rec|General model, supporting Chinese, English and number recognition|[rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml)|94.8M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) | @@ -45,7 +42,6 @@ The downloadable models provided by PaddleOCR include `inference model`, `traine |model name|description|config|model size|download| | --- | --- | --- | --- | --- | -|en_number_mobile_slim_v2.0_rec|Slim pruned and quantized lightweight model, supporting English and number recognition|[rec_en_number_lite_train.yml](../../configs/rec/multi_language/rec_en_number_lite_train.yml)| | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/en_number_mobile_v2.0_rec_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/en_number_mobile_v2.0_rec_slim_train.tar) | |en_number_mobile_v2.0_rec|Original lightweight model, supporting English and number recognition|[rec_en_number_lite_train.yml](../../configs/rec/multi_language/rec_en_number_lite_train.yml)|2.56M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_train.tar) | @@ -124,5 +120,4 @@ python3 generate_multi_language_configs.py -l it \ |model name|description|config|model size|download| | --- | --- | --- | --- | --- | -|ch_ppocr_mobile_slim_v2.0_cls|Slim quantized model|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)| | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_slim_train.tar) | |ch_ppocr_mobile_v2.0_cls|Original model|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)|1.38M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | -- GitLab