From f29b3bcaa47426a8321728fa421a496cca9844b7 Mon Sep 17 00:00:00 2001 From: gaotingquan Date: Mon, 20 Dec 2021 09:53:57 +0000 Subject: [PATCH] docs: rename --- docs/en/Features of PaddleClas en_US.md | 13 ------- .../classification_dataset_en.md | 33 ++++++++-------- ...~6d5e2b2e3619279438ccbf6dcb63165dcc3b63ea} | 33 ++++++++-------- .../recognition_dataset_en.md | 39 +++++++++---------- ...~6d5e2b2e3619279438ccbf6dcb63165dcc3b63ea} | 39 ++++++++++--------- .../faq_2020_s1_en.md} | 0 .../faq_2021_s1_en.md} | 0 .../faq_2021_s2_en.md} | 0 .../faq_selected_30_en.md} | 0 docs/en/introduction/function_intro_en.md | 26 ++++--------- ...d~6d5e2b2e3619279438ccbf6dcb63165dcc3b63ea | 23 +++++++++++ .../feature_visiualization_en.md} | 0 .../train_on_xpu_en.md} | 0 .../versions_en.md} | 0 14 files changed, 103 insertions(+), 103 deletions(-) delete mode 100644 docs/en/Features of PaddleClas en_US.md rename docs/en/{Image Classification Datasets en_US.md => data_preparation/classification_dataset_en.md~6d5e2b2e3619279438ccbf6dcb63165dcc3b63ea} (79%) rename docs/en/{Image Recognition Datasets en_US.md => data_preparation/recognition_dataset_en.md~6d5e2b2e3619279438ccbf6dcb63165dcc3b63ea} (86%) rename docs/en/{Image Classification FAQ Summary - 2020 Season 1.md => faq_series/faq_2020_s1_en.md} (100%) rename docs/en/{Image Classification FAQ Summary - 2021 Season 1.md => faq_series/faq_2021_s1_en.md} (100%) rename docs/en/{PaddleClas FAQ Summary - 2021 Season 2.md => faq_series/faq_2021_s2_en.md} (100%) rename docs/en/{FAQ.md => faq_series/faq_selected_30_en.md} (100%) create mode 100644 docs/en/introduction/function_intro_en.md~6d5e2b2e3619279438ccbf6dcb63165dcc3b63ea rename docs/en/{Guide to Feature Graph Visualization en_US.md => others/feature_visiualization_en.md} (100%) rename docs/en/{Introduction to Image Classification Model Kunlun (Continuously updated) en_US.md => others/train_on_xpu_en.md} (100%) rename docs/en/{Version Updates en_US.md => others/versions_en.md} (100%) diff --git a/docs/en/Features of PaddleClas en_US.md b/docs/en/Features of PaddleClas en_US.md deleted file mode 100644 index 4561a4dd..00000000 --- a/docs/en/Features of PaddleClas en_US.md +++ /dev/null @@ -1,13 +0,0 @@ -## Features of PaddleClas - -PaddleClas is an image recognition toolset for industry and academia, helping users train better computer vision models and apply them in real scenarios. Specifically, it contains the following core features. - -- Practical image recognition system: Integrate detection, feature learning, and retrieval modules to be applicable to all types of image recognition tasks. Four sample solutions are provided, including product recognition, vehicle recognition, logo recognition, and animation character recognition. -- Rich library of pre-trained models: Provide a total of 175 ImageNet pre-trained models of 36 series, among which 7 selected series of models support fast structural modification. -- Comprehensive and easy-to-use feature learning components: 12 metric learning methods are integrated and can be combined and switched at will through configuration files. -- SSLD knowledge distillation: The 14 classification pre-training models generally improved their accuracy by more than 3%; among them, the ResNet50_vd model achieved a Top-1 accuracy of 84.0% on the Image-Net-1k dataset and the Res2Net200_vd pre-training model achieved a Top-1 accuracy of 85.1%. -- Data augmentation: Provide 8 data augmentation algorithms such as AutoAugment, Cutout, Cutmix, etc. with the detailed introduction, code replication, and evaluation of effectiveness in a unified experimental environment. - -[![img](https://github.com/PaddlePaddle/PaddleClas/raw/release/2.3/docs/images/recognition.gif)](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.3/docs/images/recognition.gif) - -For more information about the quick start of image recognition, algorithm details, model training and evaluation, and prediction and deployment methods, please refer to the [README Tutorial](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.3/README_ch.md) on home page. diff --git a/docs/en/data_preparation/classification_dataset_en.md b/docs/en/data_preparation/classification_dataset_en.md index 3903a002..c1f84dd7 100644 --- a/docs/en/data_preparation/classification_dataset_en.md +++ b/docs/en/data_preparation/classification_dataset_en.md @@ -4,19 +4,19 @@ This document elaborates on the dataset format adopted by PaddleClas for image c ------ -## Catalogue +## Contents -- [1.Dataset Format](#1) -- [2.Common Datasets for Image Classification](#2) - - [2.1 ImageNet1k](#2.1) - - [2.2 Flowers102](#2.2) - - [2.3 CIFAR10 / CIFAR100](#2.3) - - [2.4 MNIST](#2.4) - - [2.5 NUS-WIDE](#2.5) +- [Dataset Format](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/classification_dataset.md#数据集格式说明) +- [Common Datasets for Image Classification](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/classification_dataset.md#图像分类任务常见数据集介绍) + - [2.1 ImageNet1k](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/classification_dataset.md#ImageNet1k) + - [2.2 Flowers102](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/classification_dataset.md#Flowers102) + - [2.3 CIFAR10 / CIFAR100](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/classification_dataset.md#CIFAR10/CIFAR100) + - [2.4 MNIST](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/classification_dataset.md#MNIST) + - [2.5 NUS-WIDE](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/classification_dataset.md#NUS-WIDE) - -## 1.Dataset Format + +## 1 Dataset Format PaddleClas adopts `txt` files to assign the training and test sets. Taking the `ImageNet1k` dataset as an example, where `train_list.txt` and `val_list.txt` have the following formats: @@ -33,12 +33,11 @@ val/ILSVRC2012_val_00000001.JPEG 65 ``` - -## 2.Common Datasets for Image Classification + +## 2 Common Datasets for Image Classification Here we present a compilation of commonly used image classification datasets, which is continuously updated and expects your supplement. - ### 2.1 ImageNet1k [ImageNet](https://image-net.org/) is a large visual database for visual target recognition research with over 14 million manually labeled images. ImageNet-1k is a subset of the ImageNet dataset, which contains 1000 categories with 1281167 images for the training set and 50000 for the validation set. Since 2010, ImageNet began to hold an annual image classification competition, namely, the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) with ImageNet-1k as its specified dataset. To date, ImageNet-1k has become one of the most significant contributors to the development of computer vision, based on which numerous initial models of downstream computer vision tasks are trained. @@ -69,7 +68,7 @@ PaddleClas/dataset/ILSVRC2012/ ``` - + ### 2.2 Flowers102 | Dataset | Size of Training Set | Size of Test Set | Number of Category | Note | @@ -106,7 +105,7 @@ PaddleClas/dataset/flowers102/ ``` - + ### 2.3 CIFAR10 / CIFAR100 The CIFAR-10 dataset comprises 60,000 color images of 10 classes with 32x32 image resolution, each with 6,000 images including 5,000 images in the training set and 1,000 images in the validation set. The 10 different classes represent airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. The CIFAR-100 dataset is an extension of CIFAR-10 and consists of 60,000 color images of 100 classes with 32x32 image resolution, each with 600 images including 500 images in the training set and 100 images in the validation set. @@ -114,7 +113,7 @@ The CIFAR-10 dataset comprises 60,000 color images of 10 classes with 32x32 imag Website：http://www.cs.toronto.edu/~kriz/cifar.html - + ### 2.4 MNIST MMNIST is a renowned dataset for handwritten digit recognition and is used as an introductory sample for deep learning in many sources. It contains 60,000 images, 50,000 for the training set and 10,000 for the validation set, with a size of 28 * 28. @@ -122,7 +121,7 @@ MMNIST is a renowned dataset for handwritten digit recognition and is used as an Website：http://yann.lecun.com/exdb/mnist/ - + ### 2.5 NUS-WIDE NUS-WIDE is a multi-category dataset. It contains 269,648 images and 81 categories with each image being labeled as one or more of the 81 categories. diff --git a/docs/en/Image Classification Datasets en_US.md b/docs/en/data_preparation/classification_dataset_en.md~6d5e2b2e3619279438ccbf6dcb63165dcc3b63ea similarity index 79% rename from docs/en/Image Classification Datasets en_US.md rename to docs/en/data_preparation/classification_dataset_en.md~6d5e2b2e3619279438ccbf6dcb63165dcc3b63ea index c1f84dd7..3903a002 100644 --- a/docs/en/Image Classification Datasets en_US.md +++ b/docs/en/data_preparation/classification_dataset_en.md~6d5e2b2e3619279438ccbf6dcb63165dcc3b63ea @@ -4,19 +4,19 @@ This document elaborates on the dataset format adopted by PaddleClas for image c ------ -## Contents +## Catalogue -- [Dataset Format](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/classification_dataset.md#数据集格式说明) -- [Common Datasets for Image Classification](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/classification_dataset.md#图像分类任务常见数据集介绍) - - [2.1 ImageNet1k](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/classification_dataset.md#ImageNet1k) - - [2.2 Flowers102](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/classification_dataset.md#Flowers102) - - [2.3 CIFAR10 / CIFAR100](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/classification_dataset.md#CIFAR10/CIFAR100) - - [2.4 MNIST](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/classification_dataset.md#MNIST) - - [2.5 NUS-WIDE](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/classification_dataset.md#NUS-WIDE) +- [1.Dataset Format](#1) +- [2.Common Datasets for Image Classification](#2) + - [2.1 ImageNet1k](#2.1) + - [2.2 Flowers102](#2.2) + - [2.3 CIFAR10 / CIFAR100](#2.3) + - [2.4 MNIST](#2.4) + - [2.5 NUS-WIDE](#2.5) - -## 1 Dataset Format + +## 1.Dataset Format PaddleClas adopts `txt` files to assign the training and test sets. Taking the `ImageNet1k` dataset as an example, where `train_list.txt` and `val_list.txt` have the following formats: @@ -33,11 +33,12 @@ val/ILSVRC2012_val_00000001.JPEG 65 ``` - -## 2 Common Datasets for Image Classification + +## 2.Common Datasets for Image Classification Here we present a compilation of commonly used image classification datasets, which is continuously updated and expects your supplement. + ### 2.1 ImageNet1k [ImageNet](https://image-net.org/) is a large visual database for visual target recognition research with over 14 million manually labeled images. ImageNet-1k is a subset of the ImageNet dataset, which contains 1000 categories with 1281167 images for the training set and 50000 for the validation set. Since 2010, ImageNet began to hold an annual image classification competition, namely, the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) with ImageNet-1k as its specified dataset. To date, ImageNet-1k has become one of the most significant contributors to the development of computer vision, based on which numerous initial models of downstream computer vision tasks are trained. @@ -68,7 +69,7 @@ PaddleClas/dataset/ILSVRC2012/ ``` - + ### 2.2 Flowers102 | Dataset | Size of Training Set | Size of Test Set | Number of Category | Note | @@ -105,7 +106,7 @@ PaddleClas/dataset/flowers102/ ``` - + ### 2.3 CIFAR10 / CIFAR100 The CIFAR-10 dataset comprises 60,000 color images of 10 classes with 32x32 image resolution, each with 6,000 images including 5,000 images in the training set and 1,000 images in the validation set. The 10 different classes represent airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. The CIFAR-100 dataset is an extension of CIFAR-10 and consists of 60,000 color images of 100 classes with 32x32 image resolution, each with 600 images including 500 images in the training set and 100 images in the validation set. @@ -113,7 +114,7 @@ The CIFAR-10 dataset comprises 60,000 color images of 10 classes with 32x32 imag Website：http://www.cs.toronto.edu/~kriz/cifar.html - + ### 2.4 MNIST MMNIST is a renowned dataset for handwritten digit recognition and is used as an introductory sample for deep learning in many sources. It contains 60,000 images, 50,000 for the training set and 10,000 for the validation set, with a size of 28 * 28. @@ -121,7 +122,7 @@ MMNIST is a renowned dataset for handwritten digit recognition and is used as an Website：http://yann.lecun.com/exdb/mnist/ - + ### 2.5 NUS-WIDE NUS-WIDE is a multi-category dataset. It contains 269,648 images and 81 categories with each image being labeled as one or more of the 81 categories. diff --git a/docs/en/data_preparation/recognition_dataset_en.md b/docs/en/data_preparation/recognition_dataset_en.md index 32a27984..5ed88d04 100644 --- a/docs/en/data_preparation/recognition_dataset_en.md +++ b/docs/en/data_preparation/recognition_dataset_en.md @@ -4,20 +4,20 @@ This document elaborates on the dataset format adopted by PaddleClas for image r ------ -## Catalogue +## Contents -- [1.Dataset Format](#1) -- [2.Common Datasets for Image Recognition](#2) - - [2.1 General Datasets](#2.1) - - [2.2 Vertical Class Datasets](#2.2) - - [2.2.1 Animation Character Recognition](#2.2.1) - - [2.2.2 Product Recognition](#2.2.2) - - [2.2.3 Logo Recognition](#2.2.3) - - [2.2.4 Vehicle Recognition](#2.2.4) +- [Dataset Format](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/recognition_dataset.md#数据集格式说明) +- [Common Datasets for Image Recognition](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/recognition_dataset.md#图像识别任务常见数据集介绍) + - [2.1 General Datasets](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/recognition_dataset.md#通用图像识别数据集) + - [2.2 Vertical Datasets](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/recognition_dataset.md#垂类图像识别数据集) + - [2.2.1 Animation Character Recognition](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/recognition_dataset.md#动漫人物识别) + - [2.2.2 Product Recognition](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/recognition_dataset.md#商品识别) + - [2.2.3 Logo Recognition](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/recognition_dataset.md#Logo识别) + - [2.2.4 Vehicle Recognition](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/recognition_dataset.md#车辆识别) - -## 1.Dataset Format + +## 1 Dataset Format The dataset for the vector search, unlike those for classification tasks, is divided into the following three parts: @@ -56,12 +56,11 @@ Each row of data is separated by "space", and the three columns of data stand fo 2. When the gallery dataset and query dataset are different, there is no need to add a unique id. Both `query_list.txt` and `gallery_list.txt` contain two columns, which are the path and label information of the training data. The dataset of yaml configuration file is ` ImageNetDataset`. - -## 2.Common Datasets for Image Recognition + +## 2. Common Datasets for Image Recognition Here we present a compilation of commonly used image recognition datasets, which is continuously updated and expects your supplement. - ### 2.1 General Datasets - SOP: The SOP dataset is a common product dataset in general recognition research and MetricLearning technology research, which contains 120,053 images of 22,634 products downloaded from eBay.com. There are 59,551 images of 11,318 in the training set and 60,502 images of 11,316 categories in the validation set. @@ -79,11 +78,11 @@ Here we present a compilation of commonly used image recognition datasets, which Website： http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html - -### 2.2 Vertical Class Datasets + +### 2.2 Vertical Datasets + - #### 2.2.1 Animation Character Recognition - iCartoonFace: iCartoonFace, developed by iQiyi (an online video platform), is the world's largest manual labeled detection and recognition dataset for cartoon characters, which contains more than 5013 cartoon characters and 389,678 high-quality live images. Compared with other datasets, it boasts features of large scale, high quality, rich diversity, and challenging difficulty, making it one of the most commonly used datasets to study cartoon character recognition. @@ -99,7 +98,7 @@ Here we present a compilation of commonly used image recognition datasets, which Website： http://cvit.iiit.ac.in/research/projects/cvit-projects/cartoonfaces - + #### 2.2.2 Product Recognition - AliProduct: The AliProduct dataset is the largest open source product dataset. As an SKU-level image classification dataset, it contains 50,000 categories and 3 million images, ranking the first in both aspects in the industry. This dataset covers a large number of household goods, food, etc. Due to its lack of manual annotation, the data is messy and unevenly distributed with many similar product images. @@ -113,7 +112,7 @@ Here we present a compilation of commonly used image recognition datasets, which - DeepFashion-Inshop: The same as the common datasets In-shop Clothes. - + ### 2.2.3 Logo Recognition - Logo-2K+: Logo-2K+ is a dataset exclusively for logo image recognition, which contains 10 major categories, 2341 minor categories, and 167,140 images. @@ -124,8 +123,6 @@ Here we present a compilation of commonly used image recognition datasets, which Website： https://cg.cs.tsinghua.edu.cn/traffic-sign/ - - ### 2.2.4 Vehicle Recognition - CompCars: The images, 136,726 images of the whole car and 27,618 partial ones, are mainly from network and surveillance data. The network data contains 163 vehicle manufacturers and 1,716 vehicle models and includes the bounding box, viewing angle, and 5 attributes (maximum speed, displacement, number of doors, number of seats, and vehicle type). And the surveillance data comprises 50,000 front view images. diff --git a/docs/en/Image Recognition Datasets en_US.md b/docs/en/data_preparation/recognition_dataset_en.md~6d5e2b2e3619279438ccbf6dcb63165dcc3b63ea similarity index 86% rename from docs/en/Image Recognition Datasets en_US.md rename to docs/en/data_preparation/recognition_dataset_en.md~6d5e2b2e3619279438ccbf6dcb63165dcc3b63ea index 5ed88d04..32a27984 100644 --- a/docs/en/Image Recognition Datasets en_US.md +++ b/docs/en/data_preparation/recognition_dataset_en.md~6d5e2b2e3619279438ccbf6dcb63165dcc3b63ea @@ -4,20 +4,20 @@ This document elaborates on the dataset format adopted by PaddleClas for image r ------ -## Contents +## Catalogue -- [Dataset Format](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/recognition_dataset.md#数据集格式说明) -- [Common Datasets for Image Recognition](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/recognition_dataset.md#图像识别任务常见数据集介绍) - - [2.1 General Datasets](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/recognition_dataset.md#通用图像识别数据集) - - [2.2 Vertical Datasets](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/recognition_dataset.md#垂类图像识别数据集) - - [2.2.1 Animation Character Recognition](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/recognition_dataset.md#动漫人物识别) - - [2.2.2 Product Recognition](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/recognition_dataset.md#商品识别) - - [2.2.3 Logo Recognition](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/recognition_dataset.md#Logo识别) - - [2.2.4 Vehicle Recognition](https://github.com/paddlepaddle/paddleclas/blob/release%2F2.3/docs/zh_CN/data_preparation/recognition_dataset.md#车辆识别) +- [1.Dataset Format](#1) +- [2.Common Datasets for Image Recognition](#2) + - [2.1 General Datasets](#2.1) + - [2.2 Vertical Class Datasets](#2.2) + - [2.2.1 Animation Character Recognition](#2.2.1) + - [2.2.2 Product Recognition](#2.2.2) + - [2.2.3 Logo Recognition](#2.2.3) + - [2.2.4 Vehicle Recognition](#2.2.4) - -## 1 Dataset Format + +## 1.Dataset Format The dataset for the vector search, unlike those for classification tasks, is divided into the following three parts: @@ -56,11 +56,12 @@ Each row of data is separated by "space", and the three columns of data stand fo 2. When the gallery dataset and query dataset are different, there is no need to add a unique id. Both `query_list.txt` and `gallery_list.txt` contain two columns, which are the path and label information of the training data. The dataset of yaml configuration file is ` ImageNetDataset`. - -## 2. Common Datasets for Image Recognition + +## 2.Common Datasets for Image Recognition Here we present a compilation of commonly used image recognition datasets, which is continuously updated and expects your supplement. + ### 2.1 General Datasets - SOP: The SOP dataset is a common product dataset in general recognition research and MetricLearning technology research, which contains 120,053 images of 22,634 products downloaded from eBay.com. There are 59,551 images of 11,318 in the training set and 60,502 images of 11,316 categories in the validation set. @@ -78,11 +79,11 @@ Here we present a compilation of commonly used image recognition datasets, which Website： http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html - -### 2.2 Vertical Datasets - + +### 2.2 Vertical Class Datasets + #### 2.2.1 Animation Character Recognition - iCartoonFace: iCartoonFace, developed by iQiyi (an online video platform), is the world's largest manual labeled detection and recognition dataset for cartoon characters, which contains more than 5013 cartoon characters and 389,678 high-quality live images. Compared with other datasets, it boasts features of large scale, high quality, rich diversity, and challenging difficulty, making it one of the most commonly used datasets to study cartoon character recognition. @@ -98,7 +99,7 @@ Here we present a compilation of commonly used image recognition datasets, which Website： http://cvit.iiit.ac.in/research/projects/cvit-projects/cartoonfaces - + #### 2.2.2 Product Recognition - AliProduct: The AliProduct dataset is the largest open source product dataset. As an SKU-level image classification dataset, it contains 50,000 categories and 3 million images, ranking the first in both aspects in the industry. This dataset covers a large number of household goods, food, etc. Due to its lack of manual annotation, the data is messy and unevenly distributed with many similar product images. @@ -112,7 +113,7 @@ Here we present a compilation of commonly used image recognition datasets, which - DeepFashion-Inshop: The same as the common datasets In-shop Clothes. - + ### 2.2.3 Logo Recognition - Logo-2K+: Logo-2K+ is a dataset exclusively for logo image recognition, which contains 10 major categories, 2341 minor categories, and 167,140 images. @@ -123,6 +124,8 @@ Here we present a compilation of commonly used image recognition datasets, which Website： https://cg.cs.tsinghua.edu.cn/traffic-sign/ + + ### 2.2.4 Vehicle Recognition - CompCars: The images, 136,726 images of the whole car and 27,618 partial ones, are mainly from network and surveillance data. The network data contains 163 vehicle manufacturers and 1,716 vehicle models and includes the bounding box, viewing angle, and 5 attributes (maximum speed, displacement, number of doors, number of seats, and vehicle type). And the surveillance data comprises 50,000 front view images. diff --git a/docs/en/Image Classification FAQ Summary - 2020 Season 1.md b/docs/en/faq_series/faq_2020_s1_en.md similarity index 100% rename from docs/en/Image Classification FAQ Summary - 2020 Season 1.md rename to docs/en/faq_series/faq_2020_s1_en.md diff --git a/docs/en/Image Classification FAQ Summary - 2021 Season 1.md b/docs/en/faq_series/faq_2021_s1_en.md similarity index 100% rename from docs/en/Image Classification FAQ Summary - 2021 Season 1.md rename to docs/en/faq_series/faq_2021_s1_en.md diff --git a/docs/en/PaddleClas FAQ Summary - 2021 Season 2.md b/docs/en/faq_series/faq_2021_s2_en.md similarity index 100% rename from docs/en/PaddleClas FAQ Summary - 2021 Season 2.md rename to docs/en/faq_series/faq_2021_s2_en.md diff --git a/docs/en/FAQ.md b/docs/en/faq_series/faq_selected_30_en.md similarity index 100% rename from docs/en/FAQ.md rename to docs/en/faq_series/faq_selected_30_en.md diff --git a/docs/en/introduction/function_intro_en.md b/docs/en/introduction/function_intro_en.md index 013441b8..4561a4dd 100644 --- a/docs/en/introduction/function_intro_en.md +++ b/docs/en/introduction/function_intro_en.md @@ -1,23 +1,13 @@ ## Features of PaddleClas -PaddleClas is an image recognition toolset for industry and academia, -helping users train better computer vision models and apply them in real scenarios. -Specifically, it contains the following core features. +PaddleClas is an image recognition toolset for industry and academia, helping users train better computer vision models and apply them in real scenarios. Specifically, it contains the following core features. -- Practical image recognition system: Integrate detection, feature learning, -and retrieval modules to be applicable to all types of image recognition tasks. Four sample solutions are provided, -including product recognition, vehicle recognition, logo recognition, and animation character recognition. -- Rich library of pre-trained models: Provide a total of 175 ImageNet pre-trained models of 36 series, -among which 7 selected series of models support fast structural modification. -- Comprehensive and easy-to-use feature learning components: 12 metric learning methods are integrated and can be -combined and switched at will through configuration files. -- SSLD knowledge distillation: The 14 classification pre-training models generally improved their accuracy by -more than 3%; among them, the ResNet50_vd model achieved a Top-1 accuracy of 84.0% on the Image-Net-1k dataset -and the Res2Net200_vd pre-training model achieved a Top-1 accuracy of 85.1%. -- Data augmentation: Provide 8 data augmentation algorithms such as AutoAugment, Cutout, Cutmix, etc. -with the detailed introduction, code replication, and evaluation of effectiveness in a unified experimental environment. +- Practical image recognition system: Integrate detection, feature learning, and retrieval modules to be applicable to all types of image recognition tasks. Four sample solutions are provided, including product recognition, vehicle recognition, logo recognition, and animation character recognition. +- Rich library of pre-trained models: Provide a total of 175 ImageNet pre-trained models of 36 series, among which 7 selected series of models support fast structural modification. +- Comprehensive and easy-to-use feature learning components: 12 metric learning methods are integrated and can be combined and switched at will through configuration files. +- SSLD knowledge distillation: The 14 classification pre-training models generally improved their accuracy by more than 3%; among them, the ResNet50_vd model achieved a Top-1 accuracy of 84.0% on the Image-Net-1k dataset and the Res2Net200_vd pre-training model achieved a Top-1 accuracy of 85.1%. +- Data augmentation: Provide 8 data augmentation algorithms such as AutoAugment, Cutout, Cutmix, etc. with the detailed introduction, code replication, and evaluation of effectiveness in a unified experimental environment. -![img](../../images/recognition.gif) +[![img](https://github.com/PaddlePaddle/PaddleClas/raw/release/2.3/docs/images/recognition.gif)](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.3/docs/images/recognition.gif) -For more information about the quick start of image recognition, algorithm details, model training and evaluation, -and prediction and deployment methods, please refer to the [README Tutorial](../../../README_en.md) on home page. +For more information about the quick start of image recognition, algorithm details, model training and evaluation, and prediction and deployment methods, please refer to the [README Tutorial](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.3/README_ch.md) on home page. diff --git a/docs/en/introduction/function_intro_en.md~6d5e2b2e3619279438ccbf6dcb63165dcc3b63ea b/docs/en/introduction/function_intro_en.md~6d5e2b2e3619279438ccbf6dcb63165dcc3b63ea new file mode 100644 index 00000000..013441b8 --- /dev/null +++ b/docs/en/introduction/function_intro_en.md~6d5e2b2e3619279438ccbf6dcb63165dcc3b63ea @@ -0,0 +1,23 @@ +## Features of PaddleClas + +PaddleClas is an image recognition toolset for industry and academia, +helping users train better computer vision models and apply them in real scenarios. +Specifically, it contains the following core features. + +- Practical image recognition system: Integrate detection, feature learning, +and retrieval modules to be applicable to all types of image recognition tasks. Four sample solutions are provided, +including product recognition, vehicle recognition, logo recognition, and animation character recognition. +- Rich library of pre-trained models: Provide a total of 175 ImageNet pre-trained models of 36 series, +among which 7 selected series of models support fast structural modification. +- Comprehensive and easy-to-use feature learning components: 12 metric learning methods are integrated and can be +combined and switched at will through configuration files. +- SSLD knowledge distillation: The 14 classification pre-training models generally improved their accuracy by +more than 3%; among them, the ResNet50_vd model achieved a Top-1 accuracy of 84.0% on the Image-Net-1k dataset +and the Res2Net200_vd pre-training model achieved a Top-1 accuracy of 85.1%. +- Data augmentation: Provide 8 data augmentation algorithms such as AutoAugment, Cutout, Cutmix, etc. +with the detailed introduction, code replication, and evaluation of effectiveness in a unified experimental environment. + +![img](../../images/recognition.gif) + +For more information about the quick start of image recognition, algorithm details, model training and evaluation, +and prediction and deployment methods, please refer to the [README Tutorial](../../../README_en.md) on home page. diff --git a/docs/en/Guide to Feature Graph Visualization en_US.md b/docs/en/others/feature_visiualization_en.md similarity index 100% rename from docs/en/Guide to Feature Graph Visualization en_US.md rename to docs/en/others/feature_visiualization_en.md diff --git a/docs/en/Introduction to Image Classification Model Kunlun (Continuously updated) en_US.md b/docs/en/others/train_on_xpu_en.md similarity index 100% rename from docs/en/Introduction to Image Classification Model Kunlun (Continuously updated) en_US.md rename to docs/en/others/train_on_xpu_en.md diff --git a/docs/en/Version Updates en_US.md b/docs/en/others/versions_en.md similarity index 100% rename from docs/en/Version Updates en_US.md rename to docs/en/others/versions_en.md -- GitLab