Vit进行花卉识别
时间: 2023-12-30 14:03:42 浏览: 80
花卉识别是计算机视觉中的一个热门应用场景,可以利用最近比较流行的Transformer-based模型Vit来进行实现。以下是一个Vit进行花卉识别的示例代码:
```python
import torch
import torch.nn as nn
import torchvision.models as models
import torchvision.transforms as transforms
from PIL import Image
# 定义Vit模型
class ViT(nn.Module):
def __init__(self, image_size=224, patch_size=16, num_classes=1000, dim=768, depth=12, heads=12, mlp_dim=3072):
super(ViT, self).__init__()
self.image_size = image_size
self.patch_size = patch_size
self.num_classes = num_classes
self.dim = dim
self.depth = depth
self.heads = heads
self.mlp_dim = mlp_dim
self.patch_dim = 3 * patch_size * patch_size
self.num_patches = (image_size // patch_size) ** 2
self.patch_embedding = nn.Conv2d(in_channels=3, out_channels=self.dim, kernel_size=patch_size, stride=patch_size)
self.position_embedding = nn.Parameter(torch.randn(1, self.num_patches + 1, self.dim))
self.transformer = nn.TransformerEncoder(nn.TransformerEncoderLayer(d_model=self.dim, nhead=self.heads, dim_feedforward=self.mlp_dim), num_layers=self.depth)
self.classification_head = nn.Linear(self.dim, self.num_classes)
def forward(self, x):
x = self.patch_embedding(x) # (bs, dim, num_patches_h, num_patches_w)
x = x.flatten(2).transpose(1, 2) # (bs, num_patches, dim)
x = torch.cat((self.position_embedding[:, :(self.num_patches + 1)], x), dim=1)
x = self.transformer(x)
x = x.mean(dim=1)
x = self.classification_head(x)
return x
# 加载花卉数据集
data_transforms = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
image = Image.open('flower.jpg')
image = data_transforms(image)
image = image.unsqueeze(0)
# 初始化模型
model = ViT()
model.load_state_dict(torch.load('vit_model.pth', map_location=torch.device('cpu')))
model.eval()
# 进行预测
with torch.no_grad():
output = model(image)
_, preds = torch.max(output, 1)
print(preds)
```
在示例代码中,我们定义了一个ViT模型,并加载了预训练好的权重。然后,我们使用PIL库加载一张花卉图片,并将其进行预处理。最后,我们使用加载好的模型进行预测,并输出预测结果。需要注意的是,在这个示例中,我们假设预训练好的模型可以正确地识别1000个类别,因此我们没有对模型进行微调,直接加载了预训练好的权重。如果需要对花卉数据集进行微调,可以使用PyTorch提供的Fine-tuning的技术,将模型在花卉数据集上微调,以提高模型的准确率。