guanghan_nas_1

<h2>COCO 数据集（2017部分）</h2>从官方下载，未经任何处理，下载后全部解压，数据集中包含：<ul><li><a href="http://images.cocodataset.org/zips/train2017.zip">2017 Train images [118K/18GB]</a></li><li><a href="http://images.cocodataset.org/zips/val2017.zip">2017 Val images [5K/1GB]</a></li><li><a href="http://images.cocodataset.org/zips/test2017.zip">2017 Test images [41K/6GB]</a></li><li><a href="http://images.cocodataset.org/zips/unlabeled2017.zip">2017 Unlabeled images [123K/19GB]</a></li><li><a href="http://images.cocodataset.org/annotations/annotations_trainval2017.zip">Train/Val annotations [241MB]</a></li><li><a href="http://images.cocodataset.org/annotations/stuff_annotations_trainval2017.zip">Stuff Train/Val annotations [1.1GB]</a></li><li><a href="http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip">Panoptic Train/Val annotations [821MB]</a></li><li><a href="http://images.cocodataset.org/annotations/image_info_test2017.zip">Testing Image info [1MB]</a></li><li><a href="http://images.cocodataset.org/annotations/image_info_unlabeled2017.zip">Unlabeled Image info [4MB]</a></li></ul>官方数据集地址：<a href="https://cocodataset.org/">https://cocodataset.org/</a>&nbsp;

COCO 2017

Louis

9a22ce0f-9c77-48f0-ba9b-5fa9de6dec69_2ec35b01-65b1-450b-b258-63c5f6aac09a/coronahack-chest-xraydataset.zip

COVID-19-Chest-Xray

Dave

cifar10

祖安的羊驼

<h2>Cassava Leaf Disease Classification（木薯叶病分类）</h2>Kaggle 比赛链接：<a href="https://www.kaggle.com/c/cassava-leaf-disease-classification/data">https://www.kaggle.com/c/cassava-leaf-disease-classification/data</a>该数据集未经任何人为处理，与 Kaggle 原版数据集一致

Cassava Leaf Disease Classification

guanghan_nas_2

pytorch.zip

无辜的巫妖

voc0712

淘气的松鼠

Predict the popularity of shelter pet photos

PetFinder.my - Pawpularity Contest

d7a19965-f18d-4799-9272-dc07f65cd3ec_e623522a-ac84-46b8-977c-b502e3e0368c/VOCdevkit2012.zip

voc2012

祖安的雪人

cifar100

hpatches数据集里的一个子文件

hpatches-sequences-release.tar.gz

奔跑的阿峰

coco2017.zip

glove.6B.300d.zip

promotionalText 和good合并的数据集

out.zip

coco_train.zip

躁动的侏儒

Detect crown-of-thorns starfish in underwater image data

Tensorflow - Help Protect the Great Barrier Reef

<h4>THE MNIST DATABASE（手写数字识别）</h4>数据来源：<a href="http://yann.lecun.com/exdb/mnist/">http://yann.lecun.com/exdb/mnist/</a>&nbsp;该数据已将原数据的 4 个压缩包解压，下面是读取数据的代码，添加数据后可以直接使用：&nbsp;<pre><code class="language-python">import numpy as np
import os

def extract(folder): 
 def extract_file(file_name, num, shape):
 lead_bytes = 16 if 'images' in file_name else 1
 with open(os.path.join(folder, file_name), 'rb') as f:
 f.read(lead_bytes)
 buffer = f.read(np.prod((*shape, num)))
 data = np.frombuffer(buffer, dtype=np.uint8)
 data.reshape(num, *shape, 1)
 return data

 return (
 extract_file('train-images-idx3-ubyte', 60000, (28, 28)),
 extract_file('train-labels-idx1-ubyte', 60000, (1,)),
 extract_file('t10k-images-idx3-ubyte', 10000, (28, 28)),
 extract_file('t10k-labels-idx1-ubyte', 10000, (1,))
 )

train_images, train_labels, test_images, test_labels = extract('/home/featurize/data/mnist')</code></pre>&nbsp;

数据集

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数据集来源