使用vit模型进行图像分类
时间: 2023-03-30 10:01:25 浏览: 92
可以使用vit模型进行图像分类,vit模型是一种基于注意力机制的视觉转换器,可以将图像转换为一组向量,然后使用分类器对这些向量进行分类。vit模型在图像分类任务中表现出色,具有较高的准确率和较快的训练速度。
相关问题
基于tensorflow的vit模型的图像分类代码
以下是基于TensorFlow的ViT模型的图像分类代码示例:
```python
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow.keras.layers.experimental.preprocessing import Resizing
from vit_keras import vit, utils
# Load the CIFAR-10 dataset
(ds_train, ds_test), ds_info = tfds.load('cifar10', split=['train', 'test'], with_info=True)
# Define some constants
NUM_CLASSES = ds_info.features['label'].num_classes
IMAGE_SIZE = 72 # ViT requires images to be divisible by 8
# Preprocess the data
def preprocess_data(data):
x = data['image']
y = tf.one_hot(data['label'], NUM_CLASSES)
x = Resizing(IMAGE_SIZE, IMAGE_SIZE)(x) / 255.0 # Resize and normalize
return x, y
ds_train = ds_train.map(preprocess_data).batch(32).prefetch(tf.data.experimental.AUTOTUNE)
ds_test = ds_test.map(preprocess_data).batch(32).prefetch(tf.data.experimental.AUTOTUNE)
# Define the ViT model
model = vit.vit_b16(
image_size=IMAGE_SIZE,
activation='softmax',
classes=NUM_CLASSES,
include_top=True,
pretrained=True
)
# Compile the model
model.compile(
optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.CategoricalCrossentropy(),
metrics=[tf.keras.metrics.CategoricalAccuracy()]
)
# Train the model
model.fit(
ds_train,
validation_data=ds_test,
epochs=10
)
# Evaluate the model
model.evaluate(ds_test)
```
这个代码示例使用了 TensorFlow Datasets 中的 CIFAR-10 数据集,并使用了 ViT-B16 模型进行图像分类。首先,我们定义了一些常量,包括类别数量和图像大小。然后,我们定义了一个函数来对数据进行预处理,包括缩放和归一化。接下来,我们使用 `vit.vit_b16` 函数定义了 ViT 模型,并将其编译。最后,我们使用 `model.fit` 函数训练模型,并使用 `model.evaluate` 函数评估模型。
帮我写一个基于vit模型的图像分类代码
好的,我可以回答这个问题。以下是一个基于vit模型的图像分类代码示例:
```python
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
# 定义模型
class ViT(nn.Module):
def __init__(self, image_size, patch_size, num_classes, dim):
super(ViT, self).__init__()
self.patch_size = patch_size
self.num_patches = (image_size // patch_size) ** 2
self.patch_embedding = nn.Conv2d(3, dim, kernel_size=patch_size, stride=patch_size)
self.transformer = nn.TransformerEncoder(nn.TransformerEncoderLayer(d_model=dim, nhead=8), num_layers=6)
self.classifier = nn.Linear(dim, num_classes)
def forward(self, x):
x = self.patch_embedding(x).flatten(2).transpose(1, 2)
x = self.transformer(x)
x = x.mean(dim=1)
x = self.classifier(x)
return x
# 加载数据集
train_dataset = datasets.CIFAR10(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.CIFAR10(root='./data', train=False, transform=transforms.ToTensor(), download=True)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)
# 训练模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = ViT(image_size=32, patch_size=8, num_classes=10, dim=256).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
num_epochs = 10
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.to(device)
labels = labels.to(device)
outputs = model(images)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == :
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))
# 测试模型
model.eval()
with torch.no_grad():
correct =
total =
for images, labels in test_loader:
images = images.to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size()
correct += (predicted == labels).sum().item()
print('Accuracy of the model on the test images: {} %'.format(100 * correct / total))
```
希望这个代码示例能够帮助你。