mirror of https://github.com/alibaba/EasyCV.git
420 lines
14 KiB
Markdown
420 lines
14 KiB
Markdown
# Prepare Datasets
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EasyCV provides various datasets for multi tasks. Please refer to the following guide for data preparation and keep the same data structure.
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- [Image Classification](#Image-Classification)
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- [Object Detection](#Object-Detection)
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- [Self-Supervised Learning](#Self-Supervised-Learning)
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- [Pose (Keypoint)](#Pose)
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- [Image Segmentation](#Image-Segmentation)
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- [Object Detection 3D](#Object-Detection-3D)
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## Image Classification
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- [Cifar10](#Cifar10)
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- [Cifar100](#Cifar100)
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- [Imagenet-1k](#Imagenet-1k)
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- [Imagenet-1k-TFrecords](#Imagenet-1k-TFrecords)
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### Cifar10
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The CIFAR-10 are labeled subsets of the [80 million tiny images](http://people.csail.mit.edu/torralba/tinyimages/) dataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.
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It consists of 60000 32x32 colour images in 10 classes, with 6000 images per class.
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There are 50000 training images and 10000 test images.
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Here is the list of classes in the CIFAR-10: `airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck`.
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For more detailed information, please refer to [CIFAR](https://www.cs.toronto.edu/~kriz/cifar.html).
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#### Download
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Download data from [cifar-10-python.tar.gz ](https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz)(163MB). And uncompress files to `data/cifar10`.
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Directory structure is as follows:
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```text
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data/cifar10
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└── cifar-10-batches-py
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├── batches.meta
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├── data_batch_1
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├── data_batch_2
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├── data_batch_3
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├── data_batch_4
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├── data_batch_5
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├── readme.html
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├── read.py
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└── test_batch
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```
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### Cifar100
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The CIFAR-100 are labeled subsets of the [80 million tiny images](http://people.csail.mit.edu/torralba/tinyimages/) dataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.
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This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each.
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There are 500 training images and 100 testing images per class.
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The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs).
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For more detailed information, please refer to [CIFAR](https://www.cs.toronto.edu/~kriz/cifar.html).
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#### Download
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Download data from [cifar-100-python.tar.gz](https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz) (161MB). And uncompress files to `data/cifar100`.
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Directory structure should be as follows:
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```text
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data/cifar100
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└── cifar-100-python
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├── file.txt~
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├── meta
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├── test
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├── train
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```
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### Imagenet-1k
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ImageNet is an image database organized according to the [WordNet](http://wordnet.princeton.edu/) hierarchy (currently only the nouns).
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It is used in the ImageNet Large Scale Visual Recognition Challenge(ILSVRC) and is a benchmark for image classification.
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For more detailed information, please refer to [ImageNet](https://image-net.org/download.php).
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#### Download
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ILSVRC2012 is widely used, download it as follows:
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1. Go to the [download-url](http://www.image-net.org/download-images), Register an account and log in .
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2. Recommended ILSVRC2012, download the following files：
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- Training images (Task 1 & 2). 138GB.
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- Validation images (all tasks). 6.3GB.
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3. Unzip the downloaded file.
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4. Using this [scrip](https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh) to get data meta.
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Directory structure should be as follows:
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```
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data/imagenet
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└── train
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└── n01440764
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└── n01443537
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└── ...
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└── val
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└── n01440764
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└── n01443537
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└── ...
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└── meta
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├── train.txt
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├── val.txt
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├── ...
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```
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### Imagenet-1k-TFrecords
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Original imagenet raw images packed in TFrecord format.
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For more detailed information about Imagenet dataset, please refer to [ImageNet](https://image-net.org/download.php).
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#### Download
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1. Go to the [download-url](https://www.kaggle.com/hmendonca/imagenet-1k-tfrecords-ilsvrc2012-part-0), Register an account and log in .
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2. The dataset is divided into two parts, [part0](https://www.kaggle.com/hmendonca/imagenet-1k-tfrecords-ilsvrc2012-part-0) (79GB) and [part1](https://www.kaggle.com/hmendonca/imagenet-1k-tfrecords-ilsvrc2012-part-1) (75GB), you need download all of them.
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Directory structure should be as follows, put the image file and the idx file in the same folder:
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```
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data/imagenet
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└── train
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├── train-00000-of-01024
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├── train-00000-of-01024.idx
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├── train-00001-of-01024
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├── train-00001-of-01024.idx
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├── ...
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└── validation
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├── validation-00000-of-00128
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├── validation-00000-of-00128.idx
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├── validation-00001-of-00128
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├── validation-00001-of-00128.idx
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├── ...
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```
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## Object Detection
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- [PAI-iTAG detection](#PAI-iTAG-detection)
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- [COCO2017](#COCO2017)
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- [VOC2007](#VOC2007)
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- [VOC2012](#VOC2012)
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### PAI-iTAG detection
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`PAI-iTAG` is a platform for intelligent data annotation, which supports the annotation of various data types such as images, texts, videos, and audios, as well as multi-modal mixed annotation.
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Please refer to [智能标注iTAG](https://help.aliyun.com/document_detail/311162.html) for file format and data annotation.
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#### Download
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Download [SmallCOCO](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/unittest/data/detection/small_coco_itag/small_coco_itag.tar.gz) dataset to `data/demo_itag_coco`,
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Directory structure should be as follows:
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```text
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data/demo_itag_coco/
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├── train2017
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├── train2017_20_local.manifest
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├── val2017
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└── val2017_20_local.manifest
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```
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### COCO2017
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The COCO dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.
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The COCO dataset has been updated for several editions, and coco2017 is widely used. In 2017, the training/validation split was 118K/5K and test set is a subset of 41K images of the 2015 test set.
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For more detailed information, please refer to [COCO](https://cocodataset.org/#home).
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#### Download
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Download [train2017.zip](http://images.cocodataset.org/zips/train2017.zip) (18G) ,[val2017.zip](http://images.cocodataset.org/zips/val2017.zip) (1G), [annotations_trainval2017.zip](http://images.cocodataset.org/annotations/annotations_trainval2017.zip) (241MB) and uncompress files to to `data/coco2017`.
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Directory structure is as follows:
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```text
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data/coco2017
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└── annotations
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├── instances_train2017.json
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├── instances_val2017.json
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└── train2017
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├── 000000000009.jpg
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├── 000000000025.jpg
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├── ...
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└── val2017
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├── 000000000139.jpg
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├── 000000000285.jpg
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├── ...
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```
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### VOC2007
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PASCAL VOC 2007 is a dataset for image recognition. The twenty object classes that have been selected are:
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- *Person:* person
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- *Animal:* bird, cat, cow, dog, horse, sheep
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- *Vehicle:* aeroplane, bicycle, boat, bus, car, motorbike, train
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- *Indoor:* bottle, chair, dining table, potted plant, sofa, tv/monitor
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Each image in this dataset has pixel-level segmentation annotations, bounding box annotations, and object class annotations.
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For more detailed information, please refer to [voc2007](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/index.html).
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#### Download
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Download [VOCtrainval_06-Nov-2007.tar](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar) (439MB) and uncompress files to to `data/VOCdevkit`.
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Directory structure is as follows:
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```
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data/VOCdevkit
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└── VOC2007
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└── Annotations
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├── 000005.xml
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├── 001010.xml
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├── ...
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└── JPEGImages
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├── 000005.jpg
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├── 001010.jpg
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├── ...
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└── SegmentationClass
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├── 000005.png
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├── 001010.png
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├── ...
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└── SegmentationObject
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├── 000005.png
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├── 001010.png
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├── ...
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└── ImageSets
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└── Layout
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├── train.txt
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├── trainval.txt
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├── val.txt
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└── Main
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├── train.txt
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├── val.txt
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├── ...
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└── Segmentation
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├── train.txt
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├── trainval.txt
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├── val.txt
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```
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### VOC2012
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The PASCAL VOC 2012 dataset contains 20 object categories including:
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- *Person:* person
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- *Animal:* bird, cat, cow, dog, horse, sheep
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- *Vehicle:* aeroplane, bicycle, boat, bus, car, motorbike, train
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- *Indoor:* bottle, chair, dining table, potted plant, sofa, tv/monitor
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Each image in this dataset has pixel-level segmentation annotations, bounding box annotations, and object class annotations.
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For more detailed information, please refer to [voc2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html).
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#### Download
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Download [VOCtrainval_11-May-2012.tar](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar) (2G) and uncompress files to to `data/VOCdevkit`.
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Directory structure is as follows:
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```
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data/VOCdevkit
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└── VOC2012
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└── Annotations
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├── 000005.xml
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├── 001010.xml
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├── ...
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└── JPEGImages
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├── 000005.jpg
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├── 001010.jpg
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├── ...
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└── SegmentationClass
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├── 000005.png
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├── 001010.png
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├── ...
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└── SegmentationObject
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├── 000005.png
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├── 001010.png
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├── ...
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└── ImageSets
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└── Layout
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├── train.txt
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├── trainval.txt
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├── val.txt
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└── Main
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├── train.txt
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├── val.txt
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├── ...
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└── Segmentation
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├── train.txt
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├── trainval.txt
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├── val.txt
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```
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## Self-Supervised Learning
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- [Imagenet-1k](#SSL-Imagenet-1k)
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- [Imagenet-1k-TFrecords](#SSL-Imagenet-1k-TFrecords)
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### Imagenet-1k<span id="SSL-Imagenet-1k"></span>
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Refer to [Image Classification: Imagenet-1k](#Imagenet-1k).
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### Imagenet-1k-TFrecords<span id="SSL-Imagenet-1k-TFrecords"></span>
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Refer to [Image Classification: Imagenet-1k-TFrecords](#Imagenet-1k-TFrecords).
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## Pose
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- [COCO2017](#Pose-COCO2017)
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### COCO2017<span id="Pose-COCO2017"></span>
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The COCO dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.
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The COCO dataset has been updated for several editions, and coco2017 is widely used. In 2017, the training/validation split was 118K/5K and test set is a subset of 41K images of the 2015 test set.
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For more detailed information, please refer to [COCO](https://cocodataset.org/#home).
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#### Download
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Download it as follows:
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1. Download data: [train2017.zip](http://images.cocodataset.org/zips/train2017.zip) (18G) , [val2017.zip](http://images.cocodataset.org/zips/val2017.zip) (1G)
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2. Download annotations: [annotations_trainval2017.zip](http://images.cocodataset.org/annotations/annotations_trainval2017.zip) (241MB)
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3. Download person detection results: [HRNet-Human-Pose-Estimation](https://github.com/HRNet/HRNet-Human-Pose-Estimation) provides person detection result of COCO val2017 to reproduce our multi-person pose estimation results. Please download from [OneDrive](https://1drv.ms/f/s!AhIXJn_J-blWzzDXoz5BeFl8sWM-) or [GoogleDrive](https://drive.google.com/drive/folders/1fRUDNUDxe9fjqcRZ2bnF_TKMlO0nB_dk?usp=sharing) (26.2MB).
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Then uncompress files to `data/coco2017`, directory structure is as follows:
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```text
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data/coco2017
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└── annotations
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├── person_keypoints_train2017.json
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├── person_keypoints_val2017.json
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└── person_detection_results
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├── COCO_val2017_detections_AP_H_56_person.json
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├── COCO_test-dev2017_detections_AP_H_609_person.json
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└── train2017
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├── 000000000009.jpg
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├── 000000000025.jpg
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├── ...
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└── val2017
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├── 000000000139.jpg
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├── 000000000285.jpg
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├── ...
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```
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## Image Segmentation
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- [COCO Stuff 164k](#COCO-Stuff-164k)
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### COCO Stuff 164k
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For COCO Stuff 164k dataset, please run the following commands to download and convert the augmented dataset.
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```shell
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# download
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mkdir coco_stuff164k && cd coco_stuff164k
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wget http://images.cocodataset.org/zips/train2017.zip
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wget http://images.cocodataset.org/zips/val2017.zip
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wget http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip
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# unzip
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unzip train2017.zip -d images/
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unzip val2017.zip -d images/
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unzip stuffthingmaps_trainval2017.zip -d annotations/
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# --nproc means 8 process for conversion, which could be omitted as well.
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python tools/prepare_data/coco_stuff164k.py /path/to/coco_stuff164k --nproc 8
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```
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By convention, mask labels in `/path/to/coco_stuff164k/annotations/*2017/*_labelTrainIds.png` are used for COCO Stuff 164k training and testing.
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The details of this dataset could be found at [here](https://github.com/nightrome/cocostuff#downloads).
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## Object Detection 3D
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- [NuScenes](#NuScenes)
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### NuScenes
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Download nuScenes V1.0 full dataset data and CAN bus expansion data [HERE](https://www.nuscenes.org/download). Prepare nuscenes data by running:
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```shell
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python tools/prepare_data/prepare_nuscenes.py \
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--root_path=./data/nuscenes \
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--canbus_root_path=./data/canbus \
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--out_dir=./data/nuscenes \
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--version=v1.0
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```
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It will generate `nuscenes_infos_temporal_{train,val}.pkl` files.
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The data structure is as follows:
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```
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data/nuscenes
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├── can_bus
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├── nuscenes-v1.0
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│ ├── maps
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│ ├── samples
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│ ├── sweeps
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│ ├── v1.0-test
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| ├── v1.0-trainval
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| ├── nuscenes_infos_temporal_train.pkl
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| ├── nuscenes_infos_temporal_val.pkl
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```
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