目录
代码已经封装好，对小白友好。
想要更换数据集，参考readme文件摆放好数据集即可，可以一键训练！！
1.VisionTransformer
Vision Transformer（ViT）是一种基于自注意力机制的图像分类模型，它将Transformer架构成功应用于计算机视觉领域，突破了传统卷积神经网络（CNN）的局限。
其核心思想是将输入图像分割为固定大小的图像块（如16×16像素），将这些块展平为序列后嵌入为向量，并添加位置编码以保留空间信息。通过多层的Transformer编码器处理，每个编码器包含多头自注意力机制和前馈神经网络，使模型能够捕捉图像块间的全局依赖关系，从而更高效地学习图像特征。
ViT的优势在于其强大的全局建模能力。传统CNN通过局部卷积核逐步提取特征，而ViT的自注意力机制能直接建立远距离像素间的关联，尤其适合处理图像中的复杂结构和长距离依赖。此外，ViT在数据充足时表现优异，例如在大型数据集（如ImageNet-21k或JFT-300M）上预训练后，迁移到小规模任务（如CIFAR或ImageNet-1k）时，其性能常超越ResNet等CNN模型。然而，ViT对数据量的依赖性较强，小规模数据下可能因过拟合而表现不佳，此时需结合数据增强或知识蒸馏等技术优化。
后续改进模型如DeiT（Data-efficient Image Transformer）通过引入蒸馏策略提升小数据场景下的性能，Swin Transformer则引入层次化窗口注意力机制以降低计算复杂度。ViT及其变体推动了视觉任务的变革，成为图像分类、目标检测等领域的核心架构之一，展现了Transformer在跨模态任务中的强大潜力。
2.InceptionDW模块
InceptionDW模块是一种结合了Inception架构思想和深度可分离卷积（Depthwise Separable Convolution）的高效神经网络模块，旨在提升计算效率并增强特征表达能力。
该模块的核心设计借鉴了Inception系列模型的多分支结构，通过并行使用不同尺度的深度可分离卷积（DWConv）和逐点卷积（Pointwise Conv），在减少参数量的同时捕获多尺度特征。
具体而言，InceptionDW模块通常包含多个分支，例如1×1卷积、3×3深度可分离卷积、5×5深度可分离卷积，以及全局平均池化等操作，这些分支的输出在通道维度拼接后融合，形成丰富的多尺度特征表示。
深度可分离卷积的引入大幅降低了计算成本，其将标准卷积分解为逐通道的空间卷积（DWConv）和跨通道的1×1卷积（PWConv），显著减少了参数量和FLOPs。
例如，一个3×3标准卷积的计算复杂度是输入通道数×输出通道数×3×3，而深度可分离卷积将其分解为输入通道数×3×3（DWConv）和输入通道数×输出通道数×1×1（PWConv），计算量大幅降低。InceptionDW模块通过结合多分支设计和深度可分离卷积，在保持轻量化的同时增强了模型的非线性建模能力，适用于移动端或边缘计算设备。
该模块的变体通常针对特定任务优化，例如调整分支数量、卷积核尺寸或引入注意力机制。实验表明，InceptionDW模块在图像分类（如ImageNet）、目标检测等任务中能平衡精度与效率，尤其适合资源受限的场景。其设计思想也被后续轻量级网络（如MobileNet、EfficientNet）借鉴，成为高效神经网络架构中的重要组成部分。
3.改进
网络结构图如下，这里在最后的head层引入模块：
ViT_With_InceptionDW(
(vit): VisionTransformer(
(conv_proj): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))
(encoder): Encoder(
(dropout): Dropout(p=0.0, inplace=False)
(layers): Sequential(
(encoder_layer_0): EncoderBlock(
(ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(self_attention): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(dropout): Dropout(p=0.0, inplace=False)
(ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): MLPBlock(
(0): Linear(in_features=768, out_features=3072, bias=True)
(1): GELU(approximate='none')
(2): Dropout(p=0.0, inplace=False)
(3): Linear(in_features=3072, out_features=768, bias=True)
(4): Dropout(p=0.0, inplace=False)
)
)
(encoder_layer_1): EncoderBlock(
(ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(self_attention): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(dropout): Dropout(p=0.0, inplace=False)
(ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): MLPBlock(
(0): Linear(in_features=768, out_features=3072, bias=True)
(1): GELU(approximate='none')
(2): Dropout(p=0.0, inplace=False)
(3): Linear(in_features=3072, out_features=768, bias=True)
(4): Dropout(p=0.0, inplace=False)
)
)
(encoder_layer_2): EncoderBlock(
(ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(self_attention): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(dropout): Dropout(p=0.0, inplace=False)
(ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): MLPBlock(
(0): Linear(in_features=768, out_features=3072, bias=True)
(1): GELU(approximate='none')
(2): Dropout(p=0.0, inplace=False)
(3): Linear(in_features=3072, out_features=768, bias=True)
(4): Dropout(p=0.0, inplace=False)
)
)
(encoder_layer_3): EncoderBlock(
(ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(self_attention): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(dropout): Dropout(p=0.0, inplace=False)
(ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): MLPBlock(
(0): Linear(in_features=768, out_features=3072, bias=True)
(1): GELU(approximate='none')
(2): Dropout(p=0.0, inplace=False)
(3): Linear(in_features=3072, out_features=768, bias=True)
(4): Dropout(p=0.0, inplace=False)
)
)
(encoder_layer_4): EncoderBlock(
(ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(self_attention): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(dropout): Dropout(p=0.0, inplace=False)
(ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): MLPBlock(
(0): Linear(in_features=768, out_features=3072, bias=True)
(1): GELU(approximate='none')
(2): Dropout(p=0.0, inplace=False)
(3): Linear(in_features=3072, out_features=768, bias=True)
(4): Dropout(p=0.0, inplace=False)
)
)
(encoder_layer_5): EncoderBlock(
(ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(self_attention): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(dropout): Dropout(p=0.0, inplace=False)
(ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): MLPBlock(
(0): Linear(in_features=768, out_features=3072, bias=True)
(1): GELU(approximate='none')
(2): Dropout(p=0.0, inplace=False)
(3): Linear(in_features=3072, out_features=768, bias=True)
(4): Dropout(p=0.0, inplace=False)
)
)
(encoder_layer_6): EncoderBlock(
(ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(self_attention): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(dropout): Dropout(p=0.0, inplace=False)
(ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): MLPBlock(
(0): Linear(in_features=768, out_features=3072, bias=True)
(1): GELU(approximate='none')
(2): Dropout(p=0.0, inplace=False)
(3): Linear(in_features=3072, out_features=768, bias=True)
(4): Dropout(p=0.0, inplace=False)
)
)
(encoder_layer_7): EncoderBlock(
(ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(self_attention): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(dropout): Dropout(p=0.0, inplace=False)
(ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): MLPBlock(
(0): Linear(in_features=768, out_features=3072, bias=True)
(1): GELU(approximate='none')
(2): Dropout(p=0.0, inplace=False)
(3): Linear(in_features=3072, out_features=768, bias=True)
(4): Dropout(p=0.0, inplace=False)
)
)
(encoder_layer_8): EncoderBlock(
(ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(self_attention): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(dropout): Dropout(p=0.0, inplace=False)
(ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): MLPBlock(
(0): Linear(in_features=768, out_features=3072, bias=True)
(1): GELU(approximate='none')
(2): Dropout(p=0.0, inplace=False)
(3): Linear(in_features=3072, out_features=768, bias=True)
(4): Dropout(p=0.0, inplace=False)
)
)
(encoder_layer_9): EncoderBlock(
(ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(self_attention): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(dropout): Dropout(p=0.0, inplace=False)
(ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): MLPBlock(
(0): Linear(in_features=768, out_features=3072, bias=True)
(1): GELU(approximate='none')
(2): Dropout(p=0.0, inplace=False)
(3): Linear(in_features=3072, out_features=768, bias=True)
(4): Dropout(p=0.0, inplace=False)
)
)
(encoder_layer_10): EncoderBlock(
(ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(self_attention): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(dropout): Dropout(p=0.0, inplace=False)
(ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): MLPBlock(
(0): Linear(in_features=768, out_features=3072, bias=True)
(1): GELU(approximate='none')
(2): Dropout(p=0.0, inplace=False)
(3): Linear(in_features=3072, out_features=768, bias=True)
(4): Dropout(p=0.0, inplace=False)
)
)
(encoder_layer_11): EncoderBlock(
(ln_1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(self_attention): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(dropout): Dropout(p=0.0, inplace=False)
(ln_2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): MLPBlock(
(0): Linear(in_features=768, out_features=3072, bias=True)
(1): GELU(approximate='none')
(2): Dropout(p=0.0, inplace=False)
(3): Linear(in_features=3072, out_features=768, bias=True)
(4): Dropout(p=0.0, inplace=False)
)
)
)
(ln): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
)
(heads): Sequential(
(head): Linear(in_features=768, out_features=30, bias=True)
)
)
(inception_dw): InceptionDWConv2d(
(conv1x1): Sequential(
(0): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
)
(conv3x3): Sequential(
(0): Conv2d(768, 768, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=768, bias=False)
(1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU6(inplace=True)
)
(conv5x5): Sequential(
(0): Conv2d(768, 768, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=768, bias=False)
(1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU6(inplace=True)
)
(maxpool): Sequential(
(0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
(1): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU6(inplace=True)
)
)
)4.遥感地面目标识别
数据集如下：
训练集和验证集的样本数量：【代码自动生成】
json标签：【代码自动生成】
{
"0": "Airport",
"1": "BareLand",
"2": "BaseballField",
"3": "Beach",
"4": "Bridge",
"5": "Center",
"6": "Church",
"7": "Commercial",
"8": "DenseResidential",
"9": "Desert",
"10": "Farmland",
"11": "Forest",
"12": "Industrial",
"13": "Meadow",
"14": "MediumResidential",
"15": "Mountain",
"16": "Park",
"17": "Parking",
"18": "Playground",
"19": "Pond",
"20": "Port",
"21": "RailwayStation",
"22": "Resort",
"23": "River",
"24": "School",
"25": "SparseResidential",
"26": "Square",
"27": "Stadium",
"28": "StorageTanks",
"29": "Viaduct"
}5.训练过程
参数如下：其实都很好理解的，就是常见的调参，这里不多介绍了
python
parser.add_argument("--model", default='vit', type=str,help='vit')
parser.add_argument("--pretrained", default=True, type=bool) # 采用官方权重
parser.add_argument("--batch-size", default=16, type=int)
parser.add_argument("--epochs", default=30, type=int)
parser.add_argument("--optim", default='Adam', type=str,help='SGD,Adam,AdamW') # 优化器选择
parser.add_argument('--lr', default=0.0001, type=float)
parser.add_argument('--lrf',default=0.0001,type=float) # 最终学习率 = lr * lrf
parser.add_argument('--save_ret', default='runs', type=str) # 保存结果
parser.add_argument('--data_train',default='./data/train',type=str) # 训练集路径
parser.add_argument('--data_val',default='./data/val',type=str)
# 测试集
parser.add_argument("--data-test", default=False, type=bool, help='if exists test sets')数据集的文件摆放，有测试集的话，设置为true，代码会自动测试【参考readme文件】
--data--train--- 训练集的图像
--data--val--- 验证集的图像
--data--test--- 测试集的图像（如果有的话）
这里的loss采用focal loss：
python
class FocalLoss(nn.Module):
def __init__(self, alpha=0.25, gamma=2.0, reduction='mean'):
# 增大 gamma 会更强调难分类样本
# 调整 alpha 可以平衡不同类别的权重
super(FocalLoss, self).__init__()
self.alpha = alpha
self.gamma = gamma
self.reduction = reduction
def forward(self, inputs, targets):
ce_loss = F.cross_entropy(inputs, targets, reduction='none')
pt = torch.exp(-ce_loss)
focal_loss = self.alpha * (1-pt)**self.gamma * ce_loss
if self.reduction == 'mean':
return focal_loss.mean()
elif self.reduction == 'sum':
return focal_loss.sum()
else:
return focal_loss训练日志：
python
Namespace(model='vit', pretrained=True, batch_size=16, epochs=10, optim='Adam', lr=0.0001, lrf=0.0001, save_ret='runs', data_train='./data/train', data_val='./data/val', data_test=False)
Using device is: cuda
Using dataloader workers is : 8
trainSet number is : 7000 valSet number is : 3000
model output is : 30
Total parameters is:57.94 M
Train parameters is:86442270
Flops:11407.43 M
use optim is : Adam
开始训练...
train: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 438/438 [01:20<00:00, 5.41it/s, accuracy=0.911, loss=0.000974]
valid: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 188/188 [00:17<00:00, 10.85it/s, accuracy=0.885, loss=7.54e-5]
[epoch:0/10]
train loss:0.0071 train accuracy:0.9113
val loss:0.0034 val accuracy:0.8847
train: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 438/438 [01:21<00:00, 5.38it/s, accuracy=0.974, loss=0.000403]
valid: 100%|████████████████████████| 188/188 [00:17<00:00, 10.86it/s, accuracy=0.91, loss=1.95e-6]
[epoch:1/10]
train loss:0.0024 train accuracy:0.9744
val loss:0.0024 val accuracy:0.9100
train: 100%|███████████████████████| 438/438 [01:21<00:00, 5.38it/s, accuracy=0.988, loss=2.12e-5]
valid: 100%|██████████████████████| 188/188 [00:17<00:00, 10.88it/s, accuracy=0.908, loss=2.14e-10]
[epoch:2/10]
train loss:0.0016 train accuracy:0.9883
val loss:0.0026 val accuracy:0.9080
train: 100%|███████████████████████| 438/438 [01:21<00:00, 5.37it/s, accuracy=0.993, loss=0.00394]
valid: 100%|███████████████████████| 188/188 [00:17<00:00, 10.88it/s, accuracy=0.901, loss=4.86e-5]
[epoch:3/10]
train loss:0.0010 train accuracy:0.9931
val loss:0.0028 val accuracy:0.9013
train: 100%|███████████████████████| 438/438 [01:21<00:00, 5.37it/s, accuracy=0.997, loss=7.37e-6]
valid: 100%|███████████████████████| 188/188 [00:17<00:00, 10.83it/s, accuracy=0.937, loss=2.45e-6]
[epoch:4/10]
train loss:0.0005 train accuracy:0.9971
val loss:0.0018 val accuracy:0.9370
train: 100%|██████████████████████| 438/438 [01:21<00:00, 5.35it/s, accuracy=0.998, loss=0.000451]
valid: 100%|███████████████████████| 188/188 [00:17<00:00, 10.90it/s, accuracy=0.942, loss=2.38e-9]
[epoch:5/10]
train loss:0.0004 train accuracy:0.9977
val loss:0.0016 val accuracy:0.9417
train: 100%|█████████████████████████| 438/438 [01:21<00:00, 5.38it/s, accuracy=0.999, loss=0.006]
valid: 100%|███████████████████████| 188/188 [00:17<00:00, 10.84it/s, accuracy=0.956, loss=8.48e-9]
[epoch:6/10]
train loss:0.0002 train accuracy:0.9990
val loss:0.0011 val accuracy:0.9557
train: 100%|███████████████████████████| 438/438 [01:21<00:00, 5.38it/s, accuracy=1, loss=5.46e-8]
valid: 100%|███████████████████████| 188/188 [00:17<00:00, 10.87it/s, accuracy=0.965, loss=5.01e-8]
[epoch:7/10]
train loss:0.0000 train accuracy:0.9996
val loss:0.0009 val accuracy:0.9647
train: 100%|███████████████████████| 438/438 [01:21<00:00, 5.36it/s, accuracy=0.999, loss=1.57e-6]
valid: 100%|███████████████████████| 188/188 [00:17<00:00, 10.79it/s, accuracy=0.962, loss=2.27e-8]
[epoch:8/10]
train loss:0.0000 train accuracy:0.9994
val loss:0.0009 val accuracy:0.9620
train: 100%|███████████████████████| 438/438 [01:21<00:00, 5.37it/s, accuracy=0.999, loss=3.34e-6]
valid: 100%|███████████████████████| 188/188 [00:17<00:00, 10.75it/s, accuracy=0.963, loss=1.71e-8]
[epoch:9/10]
train loss:0.0000 train accuracy:0.9994
val loss:0.0009 val accuracy:0.9633
训练结束!!!
best epoch: 9
100%|████████████████████████████████████████████████████████████| 438/438 [00:19<00:00, 21.93it/s]
100%|████████████████████████████████████████████████████████████| 188/188 [00:08<00:00, 21.35it/s]
roc curve: 100%|█████████████████████████████████████████████████| 188/188 [00:08<00:00, 21.41it/s]
train finish!
验证集上表现最好的epoch为: 9训练生成的文件：
python
{
"train parameters": {
"model version": "vit",
"pretrained": true,
"batch_size": 16,
"epochs": 10,
"optim": "Adam",
"lr": 0.0001,
"lrf": 0.0001,
"save_folder": "runs"
},
"dataset": {
"trainset number": 7000,
"valset number": 3000,
"number classes": 30
},
"model": {
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"train parameters": 86442270,
"flops": 11407434240.0
},
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"F1 score": 0.8859
},
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},
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},
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"Farmland": {
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"Meadow": {
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"SparseResidential": {
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"Square": {
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"Stadium": {
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"StorageTanks": {
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"Viaduct": {
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"mean precision": 0.9121766666666666,
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"mean f1 score": 0.9094400000000001
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"Beach": {
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"Parking": {
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"Resort": {
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"River": {
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"School": {
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"SparseResidential": {
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"Square": {
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"Stadium": {
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"StorageTanks": {
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"Viaduct": {
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},
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"Bridge": {
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"Center": {
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"Church": {
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"Commercial": {
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"DenseResidential": {
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"Desert": {
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"Farmland": {
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"Forest": {
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"Industrial": {
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"Meadow": {
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"MediumResidential": {
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"Mountain": {
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"Parking": {
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"Playground": {
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"Pond": {
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"River": {
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"SparseResidential": {
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"Square": {
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"Stadium": {
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"StorageTanks": {
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"Viaduct": {
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"Airport": {
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"BareLand": {
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"BaseballField": {
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"Beach": {
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"Bridge": {
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"Center": {
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"Church": {
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"Commercial": {
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"DenseResidential": {
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"Desert": {
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"Farmland": {
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"Meadow": {
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"MediumResidential": {
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"Mountain": {
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"Park": {
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"Parking": {
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"Playground": {
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"Pond": {
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"Port": {
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"RailwayStation": {
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"Resort": {
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"River": {
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"School": {
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"SparseResidential": {
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"Square": {
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"Stadium": {
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"StorageTanks": {
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"Viaduct": {
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"mean f1 score": 0.9062366666666666
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},
"epoch:2": {
"train info": {
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"Airport": {
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"BareLand": {
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"BaseballField": {
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"Beach": {
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"Bridge": {
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"Center": {
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"Church": {
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"Commercial": {
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"DenseResidential": {
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"Desert": {
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"Farmland": {
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"Forest": {
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"Industrial": {
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"Meadow": {
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"MediumResidential": {
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"Mountain": {
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"Park": {
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"Parking": {
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"Playground": {
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"Pond": {
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"Precision": 0.95,
"Recall": 0.8736,
"Specificity": 0.9986,
"F1 score": 0.9102
},
"River": {
"Precision": 0.9758,
"Recall": 0.9837,
"Specificity": 0.999,
"F1 score": 0.9797
},
"School": {
"Precision": 0.8876,
"Recall": 0.8778,
"Specificity": 0.9966,
"F1 score": 0.8827
},
"SparseResidential": {
"Precision": 1.0,
"Recall": 0.9889,
"Specificity": 1.0,
"F1 score": 0.9944
},
"Square": {
"Precision": 0.9574,
"Recall": 0.9091,
"Specificity": 0.9986,
"F1 score": 0.9326
},
"Stadium": {
"Precision": 1.0,
"Recall": 0.977,
"Specificity": 1.0,
"F1 score": 0.9884
},
"StorageTanks": {
"Precision": 0.9722,
"Recall": 0.9722,
"Specificity": 0.999,
"F1 score": 0.9722
},
"Viaduct": {
"Precision": 0.9844,
"Recall": 1.0,
"Specificity": 0.9993,
"F1 score": 0.9921
},
"mean precision": 0.9628066666666666,
"mean recall": 0.9615533333333333,
"mean specificity": 0.9987433333333334,
"mean f1 score": 0.9619099999999999
}
}
}
这些都是代码自动生成的，摆放好数据集即可
6.推理
这里使用QT推理：
7.下载
下载地址：基于vision-Transformer+InceptionDW模块+Focalloss改进的【遥感地面30多类土地目标】的图像分类项目资源-CSDN文库
关于神经网络的改进，可以关注本人专栏：AI 改进系列_听风吹等浪起的博客-CSDN博客