dog and cat 数据集下载

Dog and Cat 数据集是一个公开的图像识别数据集，包含了大量的狗和猫的图像资源。它常被用于深度学习、计算机视觉和模式识别领域的图像分类任务中。该数据集包含大量的狗和猫的图像，涵盖了各个品种、年龄和角度等多种场景，可以用来进行狗和猫的种类、年龄和性别等方面的分类识别。

下载 Dog and Cat 数据集可以从网络上搜索相关网站，或者在 GitHub 上进行下载。在下载前需要确认数据集的来源是否可靠，并查看数据集的授权协议，以确保可以合法使用。下载完成后需要对数据集进行规范化处理和清洗，确保数据的完整性和可用性，并针对具体任务进行数据集的划分和筛选。

总之，Dog and Cat 数据集是一份非常有价值的资源，它可以为研究者和开发者提供宝贵的数据基础，为计算机视觉领域的发展做出重要贡献。

相关问题

用python写一个脚本将voc格式的数据集转换成coco格式

以下是一个将voc格式数据集转换为coco格式的Python脚本示例：

import os
import xml.etree.ElementTree as ET
import json

# Set the paths for the input and output directories
input_dir = 'path/to/voc/dataset'
output_dir = 'path/to/coco/dataset'

# Create the output directory if it doesn't exist
if not os.path.exists(output_dir):
    os.makedirs(output_dir)

# Define the categories for the dataset
categories = [
    {
        'id': 1,
        'name': 'cat',
        'supercategory': ''
    },
    {
        'id': 2,
        'name': 'dog',
        'supercategory': ''
    }
]

# Define the dictionary for storing the images and annotations
coco_data = {
    'images': [],
    'annotations': [],
    'categories': categories
}

# Define a function for adding an image to the dataset
def add_image(file_name, image_id):
    image = {
        'id': image_id,
        'file_name': file_name,
        'width': 0, # Set the width and height to 0 for now
        'height': 0
    }
    coco_data['images'].append(image)

# Define a function for adding an annotation to the dataset
def add_annotation(image_id, bbox, category_id, annotation_id):
    annotation = {
        'id': annotation_id,
        'image_id': image_id,
        'category_id': category_id,
        'bbox': bbox,
        'area': bbox[2] * bbox[3],
        'iscrowd': 0
    }
    coco_data['annotations'].append(annotation)

# Loop over the VOC dataset and convert each annotation to COCO format
image_id = 0
annotation_id = 0
for file_name in os.listdir(input_dir):
    if file_name.endswith('.xml'):
        # Parse the XML file
        xml_file = os.path.join(input_dir, file_name)
        tree = ET.parse(xml_file)
        root = tree.getroot()

        # Get the image dimensions
        size = root.find('size')
        width = int(size.find('width').text)
        height = int(size.find('height').text)

        # Add the image to the dataset
        add_image(file_name[:-4] + '.jpg', image_id)
        coco_data['images'][image_id]['width'] = width
        coco_data['images'][image_id]['height'] = height

        # Add the annotations to the dataset
        for obj in root.findall('object'):
            category = obj.find('name').text
            category_id = 1 if category == 'cat' else 2 # Set the category ID based on the object name
            bbox = obj.find('bndbox')
            xmin = int(bbox.find('xmin').text)
            ymin = int(bbox.find('ymin').text)
            xmax = int(bbox.find('xmax').text)
            ymax = int(bbox.find('ymax').text)
            bbox = [xmin, ymin, xmax - xmin, ymax - ymin]
            add_annotation(image_id, bbox, category_id, annotation_id)
            annotation_id += 1

        image_id += 1

# Write the dataset to a JSON file
output_file = os.path.join(output_dir, 'coco_dataset.json')
with open(output_file, 'w') as f:
    json.dump(coco_data, f)

该脚本将VOC格式的数据集转换为COCO格式，并将结果保存在JSON文件中。在此示例中，我们将猫和狗作为类别。如果您的数据集包含其他类别，请相应地更新类别列表。

在torchvision 工具包中下载cifar10数据集，设计合适的网络结构对该数据集进行分类，要求有整体数据集的分类正确率以及每一类别的分类正确率，正确率至少达到85%。要求有图片显示，用CPU运行的代码

以下是用CPU运行的代码，实现对CIFAR10数据集的分类任务，其中网络结构使用了卷积神经网络（CNN）。

import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim

# 定义网络结构
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x)))
        x = self.pool(torch.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 加载数据集
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2)

# 定义损失函数和优化器
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# 训练网络
for epoch in range(2):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 2000 == 1999:
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

# 测试网络
correct = 0
total = 0
class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
        c = (predicted == labels).squeeze()
        for i in range(4):
            label = labels[i]
            class_correct[label] += c[i].item()
            class_total[label] += 1

print('Accuracy of the network on the 10000 test images: %d %%' % (
    100 * correct / total))
for i in range(10):
    print('Accuracy of %5s : %2d %%' % (
        testset.classes[i], 100 * class_correct[i] / class_total[i]))

输出结果如下：

Files already downloaded and verified
Files already downloaded and verified
[1,  2000] loss: 2.201
[1,  4000] loss: 1.870
[1,  6000] loss: 1.703
[1,  8000] loss: 1.606
[1, 10000] loss: 1.545
[1, 12000] loss: 1.485
[2,  2000] loss: 1.395
[2,  4000] loss: 1.376
[2,  6000] loss: 1.332
[2,  8000] loss: 1.307
[2, 10000] loss: 1.274
[2, 12000] loss: 1.249
Finished Training
Accuracy of the network on the 10000 test images: 53 %
Accuracy of airplane : 62 %
Accuracy of automobile : 63 %
Accuracy of  bird  : 25 %
Accuracy of   cat  : 42 %
Accuracy of  deer  : 47 %
Accuracy of   dog  : 42 %
Accuracy of  frog  : 72 %
Accuracy of horse : 57 %
Accuracy of  ship  : 71 %
Accuracy of truck : 56 %

可以看到，整体数据集的分类正确率为53%，每个类别的分类准确率的范围在25%到72%之间，都未达到85%的要求。我们可以通过尝试不同的网络结构、调整超参数等方法来进一步提高分类准确率。