diff --git a/deploy/slim/quantization/README.md b/deploy/slim/quantization/README.md
deleted file mode 100644
index 4ac3f7c3016c9ef53724ad6f7745507cef3580a8..0000000000000000000000000000000000000000
--- 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_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)
diff --git a/deploy/slim/quantization/README_en.md b/deploy/slim/quantization/README_en.md
deleted file mode 100644
index 36407a2bb58ee3a36afc211ca7a8f0d786d1714f..0000000000000000000000000000000000000000
--- 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_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)
diff --git a/deploy/slim/quantization/export_model.py b/deploy/slim/quantization/export_model.py
deleted file mode 100755
index 100b107a1deb1ce9932c9cefa50659c060f5803e..0000000000000000000000000000000000000000
--- 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 7671e5f871ce6769fc51876d1fa2e5f0af63d904..0000000000000000000000000000000000000000
--- 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 fbfb3838b7f860678b10ef4507ebf6c0d4b815c9..a17ee84e0ea110079dc6f497d07bd93055857a66
--- 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 33033f8348fa4fb08d6e8998ff53cd62349c214e..1ef2835cd254e9d9b371a8d7fc54971e9e04f0be
--- 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) |