Merge pull request #1901 from MissPenguin/release/2.0

delete slim related content

Merge pull request #1901 from MissPenguin/release/2.0
delete slim related content
36dae990 · MissPenguin · GitHub · fe775780 · f6daae41 · fe775780
6 changed file
--- a/deploy/slim/quantization/README.md
+++ b/deploy/slim/quantization/README.md
-## 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_inference_dir=./output/quant_inference_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)
--- a/deploy/slim/quantization/README_en.md
+++ b/deploy/slim/quantization/README_en.md
-## 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_inference_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)
--- a/deploy/slim/quantization/export_model.py
+++ b/deploy/slim/quantization/export_model.py
-# 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()
--- a/deploy/slim/quantization/quant.py
+++ b/deploy/slim/quantization/quant.py
-# 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)
--- 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|
 <a name="文本检测模型"></a>
 ### 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) |
 <a name="多语言识别模型"></a>
@@ -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) |
--- 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|
 <a name="Detection"></a>
 ### 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) |
 <a name="Multilingual"></a>
@@ -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) |