基于pytorch實(shí)現(xiàn)運(yùn)動(dòng)鞋品牌識(shí)別功能
更新時(shí)間：2024年02月01日 10:10:28 作者：Tooii
這篇文章主要給大家介紹了關(guān)于如何基于pytorch實(shí)現(xiàn)運(yùn)動(dòng)鞋品牌識(shí)別功能,文中通過圖文以及實(shí)例代碼介紹的非常詳細(xì),對(duì)大家學(xué)習(xí)或者使用PyTorch具有一定的參考學(xué)習(xí)價(jià)值,需要的朋友可以參考下
一、前期準(zhǔn)備
1.設(shè)置GPU
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision
from torchvision import transforms, datasets
import os,PIL,pathlib,random
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device
2. 導(dǎo)入數(shù)據(jù)
data_dir = '../Data/運(yùn)動(dòng)鞋品牌識(shí)別數(shù)據(jù)/'
data_dir = pathlib.Path(data_dir)
data_paths = list(data_dir.glob('*/*'))
classeNames = sorted(item.name for item in data_dir.glob('*/') if item.is_dir())
classeNames
['test', 'train']
- 第一步：使用
pathlib.Path()函數(shù)將字符串類型的文件夾路徑轉(zhuǎn)換為pathlib.Path對(duì)象。 - 第二步：使用
glob()方法獲取data_dir路徑下的所有文件路徑，并以列表形式存儲(chǔ)在data_paths中。 - 第三步：通過
split()函數(shù)對(duì)data_paths中的每個(gè)文件路徑執(zhí)行分割操作，獲得各個(gè)文件所屬的類別名稱，并存儲(chǔ)在classeNames中 - 第四步：打印
classeNames列表，顯示每個(gè)文件所屬的類別名稱。
train_transforms = transforms.Compose([
transforms.Resize([224, 224]), # 將輸入圖片resize成統(tǒng)一尺寸
# transforms.RandomHorizontalFlip(), # 隨機(jī)水平翻轉(zhuǎn)
transforms.ToTensor(), # 將PIL Image或numpy.ndarray轉(zhuǎn)換為tensor，并歸一化到[0,1]之間
transforms.Normalize( # 標(biāo)準(zhǔn)化處理-->轉(zhuǎn)換為標(biāo)準(zhǔn)正太分布（高斯分布），使模型更容易收斂
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]) # 其中 mean=[0.485,0.456,0.406]與std=[0.229,0.224,0.225] 從數(shù)據(jù)集中隨機(jī)抽樣計(jì)算得到的。
])
test_transform = transforms.Compose([
transforms.Resize([224, 224]), # 將輸入圖片resize成統(tǒng)一尺寸
transforms.ToTensor(), # 將PIL Image或numpy.ndarray轉(zhuǎn)換為tensor，并歸一化到[0,1]之間
transforms.Normalize( # 標(biāo)準(zhǔn)化處理-->轉(zhuǎn)換為標(biāo)準(zhǔn)正太分布（高斯分布），使模型更容易收斂
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]) # 其中 mean=[0.485,0.456,0.406]與std=[0.229,0.224,0.225] 從數(shù)據(jù)集中隨機(jī)抽樣計(jì)算得到的。
])
train_dataset = datasets.ImageFolder("../Data/運(yùn)動(dòng)鞋品牌識(shí)別數(shù)據(jù)/train/",transform=train_transforms)
test_dataset = datasets.ImageFolder("../Data/運(yùn)動(dòng)鞋品牌識(shí)別數(shù)據(jù)/test/",transform=train_transforms)
train_dataset.class_to_idx
{'adidas': 0, 'nike': 1}3. 劃分?jǐn)?shù)據(jù)集
batch_size = 32
train_dl = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1)
test_dl = torch.utils.data.DataLoader(test_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1)
for X, y in test_dl:
print("Shape of X [N, C, H, W]: ", X.shape)
print("Shape of y: ", y.shape, y.dtype)
break
Shape of X [N, C, H, W]: torch.Size([32, 3, 224, 224]) Shape of y: torch.Size([32]) torch.int64
二、構(gòu)建簡單的CNN網(wǎng)絡(luò)
對(duì)于一般的CNN網(wǎng)絡(luò)來說，都是由特征提取網(wǎng)絡(luò)和分類網(wǎng)絡(luò)構(gòu)成，其中特征提取網(wǎng)絡(luò)用于提取圖片的特征，分類網(wǎng)絡(luò)用于將圖片進(jìn)行分類。
網(wǎng)絡(luò)結(jié)構(gòu)圖
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.conv1=nn.Sequential(
nn.Conv2d(3, 12, kernel_size=5, padding=0), # 12*220*220
nn.BatchNorm2d(12),
nn.ReLU())
self.conv2=nn.Sequential(
nn.Conv2d(12, 12, kernel_size=5, padding=0), # 12*216*216
nn.BatchNorm2d(12),
nn.ReLU())
self.pool3=nn.Sequential(
nn.MaxPool2d(2)) # 12*108*108
self.conv4=nn.Sequential(
nn.Conv2d(12, 24, kernel_size=5, padding=0), # 24*104*104
nn.BatchNorm2d(24),
nn.ReLU())
self.conv5=nn.Sequential(
nn.Conv2d(24, 24, kernel_size=5, padding=0), # 24*100*100
nn.BatchNorm2d(24),
nn.ReLU())
self.pool6=nn.Sequential(
nn.MaxPool2d(2)) # 24*50*50
self.dropout = nn.Sequential(
nn.Dropout(0.2))
self.fc=nn.Sequential(
nn.Linear(24*50*50, len(classeNames)))
def forward(self, x):
batch_size = x.size(0)
x = self.conv1(x) # 卷積-BN-激活
x = self.conv2(x) # 卷積-BN-激活
x = self.pool3(x) # 池化
x = self.conv4(x) # 卷積-BN-激活
x = self.conv5(x) # 卷積-BN-激活
x = self.pool6(x) # 池化
x = self.dropout(x)
x = x.view(batch_size, -1) # flatten 變成全連接網(wǎng)絡(luò)需要的輸入 (batch, 24*50*50) ==> (batch, -1), -1 此處自動(dòng)算出的是24*50*50
x = self.fc(x)
return x
device = "cuda" if torch.cuda.is_available() else "cpu"
print("Using {} device".format(device))
model = Model().to(device)
model
Model(
(conv1): Sequential(
(0): Conv2d(3, 12, kernel_size=(5, 5), stride=(1, 1))
(1): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv2): Sequential(
(0): Conv2d(12, 12, kernel_size=(5, 5), stride=(1, 1))
(1): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(pool3): Sequential(
(0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(conv4): Sequential(
(0): Conv2d(12, 24, kernel_size=(5, 5), stride=(1, 1))
(1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv5): Sequential(
(0): Conv2d(24, 24, kernel_size=(5, 5), stride=(1, 1))
(1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(pool6): Sequential(
(0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(dropout): Sequential(
(0): Dropout(p=0.2, inplace=False)
)
(fc): Sequential(
(0): Linear(in_features=60000, out_features=2, bias=True)
)
)
三、 訓(xùn)練模型
1. 編寫訓(xùn)練函數(shù)
# 訓(xùn)練循環(huán)
def train(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset) # 訓(xùn)練集的大小
num_batches = len(dataloader) # 批次數(shù)目, (size/batch_size，向上取整)
train_loss, train_acc = 0, 0 # 初始化訓(xùn)練損失和正確率
for X, y in dataloader: # 獲取圖片及其標(biāo)簽
X, y = X.to(device), y.to(device)
# 計(jì)算預(yù)測(cè)誤差
pred = model(X) # 網(wǎng)絡(luò)輸出
loss = loss_fn(pred, y) # 計(jì)算網(wǎng)絡(luò)輸出和真實(shí)值之間的差距，targets為真實(shí)值，計(jì)算二者差值即為損失
# 反向傳播
optimizer.zero_grad() # grad屬性歸零
loss.backward() # 反向傳播
optimizer.step() # 每一步自動(dòng)更新
# 記錄acc與loss
train_acc += (pred.argmax(1) == y).type(torch.float).sum().item()
train_loss += loss.item()
train_acc /= size
train_loss /= num_batches
return train_acc, train_loss
2. 編寫測(cè)試函數(shù)
def test (dataloader, model, loss_fn):
size = len(dataloader.dataset) # 測(cè)試集的大小
num_batches = len(dataloader) # 批次數(shù)目, (size/batch_size，向上取整)
test_loss, test_acc = 0, 0
# 當(dāng)不進(jìn)行訓(xùn)練時(shí)，停止梯度更新，節(jié)省計(jì)算內(nèi)存消耗
with torch.no_grad():
for imgs, target in dataloader:
imgs, target = imgs.to(device), target.to(device)
# 計(jì)算loss
target_pred = model(imgs)
loss = loss_fn(target_pred, target)
test_loss += loss.item()
test_acc += (target_pred.argmax(1) == target).type(torch.float).sum().item()
test_acc /= size
test_loss /= num_batches
return test_acc, test_loss
3. 設(shè)置動(dòng)態(tài)學(xué)習(xí)率
def adjust_learning_rate(optimizer, epoch, start_lr):
# 每 2 個(gè)epoch衰減到原來的 0.98
lr = start_lr * (0.92 ** (epoch // 2))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
learn_rate = 1e-4 # 初始學(xué)習(xí)率
optimizer = torch.optim.SGD(model.parameters(), lr=learn_rate)
4. 正式訓(xùn)練
loss_fn = nn.CrossEntropyLoss() # 創(chuàng)建損失函數(shù)
epochs = 40
train_loss = []
train_acc = []
test_loss = []
test_acc = []
for epoch in range(epochs):
# 更新學(xué)習(xí)率（使用自定義學(xué)習(xí)率時(shí)使用）
adjust_learning_rate(optimizer, epoch, learn_rate)
model.train()
epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, optimizer)
# scheduler.step() # 更新學(xué)習(xí)率（調(diào)用官方動(dòng)態(tài)學(xué)習(xí)率接口時(shí)使用）
model.eval()
epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)
train_acc.append(epoch_train_acc)
train_loss.append(epoch_train_loss)
test_acc.append(epoch_test_acc)
test_loss.append(epoch_test_loss)
# 獲取當(dāng)前的學(xué)習(xí)率
lr = optimizer.state_dict()['param_groups'][0]['lr']
template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, Lr:{:.2E}')
print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss,
epoch_test_acc*100, epoch_test_loss, lr))
print('Done')
Epoch: 1, Train_acc:52.6%, Train_loss:0.744, Test_acc:50.0%, Test_loss:0.716, Lr:1.00E-04 Epoch: 2, Train_acc:59.0%, Train_loss:0.690, Test_acc:67.1%, Test_loss:0.618, Lr:1.00E-04 Epoch: 3, Train_acc:64.1%, Train_loss:0.627, Test_acc:61.8%, Test_loss:0.637, Lr:9.20E-05 Epoch: 4, Train_acc:67.9%, Train_loss:0.588, Test_acc:77.6%, Test_loss:0.584, Lr:9.20E-05 Epoch: 5, Train_acc:74.7%, Train_loss:0.539, Test_acc:73.7%, Test_loss:0.553, Lr:8.46E-05 Epoch: 6, Train_acc:76.3%, Train_loss:0.516, Test_acc:76.3%, Test_loss:0.528, Lr:8.46E-05 Epoch: 7, Train_acc:77.1%, Train_loss:0.495, Test_acc:80.3%, Test_loss:0.533, Lr:7.79E-05 Epoch: 8, Train_acc:77.3%, Train_loss:0.491, Test_acc:76.3%, Test_loss:0.548, Lr:7.79E-05 Epoch: 9, Train_acc:78.1%, Train_loss:0.457, Test_acc:76.3%, Test_loss:0.516, Lr:7.16E-05 Epoch:10, Train_acc:83.1%, Train_loss:0.436, Test_acc:73.7%, Test_loss:0.513, Lr:7.16E-05 Epoch:11, Train_acc:81.5%, Train_loss:0.442, Test_acc:77.6%, Test_loss:0.525, Lr:6.59E-05 Epoch:12, Train_acc:83.3%, Train_loss:0.423, Test_acc:75.0%, Test_loss:0.552, Lr:6.59E-05 Epoch:13, Train_acc:82.3%, Train_loss:0.418, Test_acc:77.6%, Test_loss:0.477, Lr:6.06E-05 Epoch:14, Train_acc:85.3%, Train_loss:0.403, Test_acc:76.3%, Test_loss:0.513, Lr:6.06E-05 Epoch:15, Train_acc:86.1%, Train_loss:0.387, Test_acc:78.9%, Test_loss:0.509, Lr:5.58E-05 Epoch:16, Train_acc:87.5%, Train_loss:0.372, Test_acc:80.3%, Test_loss:0.486, Lr:5.58E-05 Epoch:17, Train_acc:88.2%, Train_loss:0.358, Test_acc:75.0%, Test_loss:0.460, Lr:5.13E-05 Epoch:18, Train_acc:88.2%, Train_loss:0.359, Test_acc:77.6%, Test_loss:0.469, Lr:5.13E-05 Epoch:19, Train_acc:88.6%, Train_loss:0.360, Test_acc:78.9%, Test_loss:0.504, Lr:4.72E-05 Epoch:20, Train_acc:89.4%, Train_loss:0.357, Test_acc:78.9%, Test_loss:0.480, Lr:4.72E-05 Epoch:21, Train_acc:90.4%, Train_loss:0.341, Test_acc:78.9%, Test_loss:0.475, Lr:4.34E-05 Epoch:22, Train_acc:90.2%, Train_loss:0.335, Test_acc:78.9%, Test_loss:0.481, Lr:4.34E-05 Epoch:23, Train_acc:89.4%, Train_loss:0.335, Test_acc:77.6%, Test_loss:0.491, Lr:4.00E-05 Epoch:24, Train_acc:91.4%, Train_loss:0.320, Test_acc:78.9%, Test_loss:0.469, Lr:4.00E-05 Epoch:25, Train_acc:92.6%, Train_loss:0.324, Test_acc:78.9%, Test_loss:0.485, Lr:3.68E-05 Epoch:26, Train_acc:92.4%, Train_loss:0.313, Test_acc:78.9%, Test_loss:0.478, Lr:3.68E-05 Epoch:27, Train_acc:91.8%, Train_loss:0.307, Test_acc:77.6%, Test_loss:0.436, Lr:3.38E-05 Epoch:28, Train_acc:90.4%, Train_loss:0.313, Test_acc:77.6%, Test_loss:0.480, Lr:3.38E-05 Epoch:29, Train_acc:93.0%, Train_loss:0.302, Test_acc:76.3%, Test_loss:0.485, Lr:3.11E-05 Epoch:30, Train_acc:92.2%, Train_loss:0.306, Test_acc:78.9%, Test_loss:0.438, Lr:3.11E-05 Epoch:31, Train_acc:92.4%, Train_loss:0.306, Test_acc:77.6%, Test_loss:0.455, Lr:2.86E-05 Epoch:32, Train_acc:92.6%, Train_loss:0.299, Test_acc:78.9%, Test_loss:0.425, Lr:2.86E-05 Epoch:33, Train_acc:91.6%, Train_loss:0.299, Test_acc:77.6%, Test_loss:0.524, Lr:2.63E-05 Epoch:34, Train_acc:93.6%, Train_loss:0.290, Test_acc:78.9%, Test_loss:0.477, Lr:2.63E-05 Epoch:35, Train_acc:94.0%, Train_loss:0.290, Test_acc:78.9%, Test_loss:0.455, Lr:2.42E-05 Epoch:36, Train_acc:93.4%, Train_loss:0.282, Test_acc:78.9%, Test_loss:0.453, Lr:2.42E-05 Epoch:37, Train_acc:94.2%, Train_loss:0.281, Test_acc:78.9%, Test_loss:0.457, Lr:2.23E-05 Epoch:38, Train_acc:94.0%, Train_loss:0.289, Test_acc:78.9%, Test_loss:0.449, Lr:2.23E-05 Epoch:39, Train_acc:94.2%, Train_loss:0.279, Test_acc:77.6%, Test_loss:0.435, Lr:2.05E-05 Epoch:40, Train_acc:93.8%, Train_loss:0.280, Test_acc:77.6%, Test_loss:0.425, Lr:2.05E-05 Done
四、 結(jié)果可視化
1. Loss與Accuracy圖
import matplotlib.pyplot as plt
#隱藏警告
import warnings
warnings.filterwarnings("ignore") #忽略警告信息
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用來正常顯示中文標(biāo)簽
plt.rcParams['axes.unicode_minus'] = False # 用來正常顯示負(fù)號(hào)
plt.rcParams['figure.dpi'] = 100 #分辨率
epochs_range = range(epochs)
plt.figure(figsize=(12, 3))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, train_acc, label='Training Accuracy')
plt.plot(epochs_range, test_acc, label='Test Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1, 2, 2)
plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
2. 指定圖片進(jìn)行預(yù)測(cè)
from PIL import Image
classes = list(train_dataset.class_to_idx)
def predict_one_image(image_path, model, transform, classes):
test_img = Image.open(image_path).convert('RGB')
# plt.imshow(test_img) # 展示預(yù)測(cè)的圖片
test_img = transform(test_img)
img = test_img.to(device).unsqueeze(0)
model.eval()
output = model(img)
_,pred = torch.max(output,1)
pred_class = classes[pred]
print(f'預(yù)測(cè)結(jié)果是：{pred_class}')
# 預(yù)測(cè)訓(xùn)練集中的某張照片
predict_one_image(image_path='../Data/運(yùn)動(dòng)鞋品牌識(shí)別數(shù)據(jù)/test/nike/31.jpg',
model=model,
transform=train_transforms,
classes=classes)
預(yù)測(cè)結(jié)果是：nike
五、保存并加載模型
# 模型保存 PATH = './model/shoeBrands_model.pth' # 保存的參數(shù)文件名 torch.save(model.state_dict(), PATH) # 將參數(shù)加載到model當(dāng)中 model.load_state_dict(torch.load(PATH, map_location=device))
六、動(dòng)態(tài)學(xué)習(xí)率
1.torch.optim.lr_scheduler.StepLR
等間隔動(dòng)態(tài)調(diào)整方法，每經(jīng)過step_size個(gè)epoch，做一次學(xué)習(xí)率decay，以gamma值為縮小倍數(shù)。
函數(shù)原型：
torch.optim.lr_scheduler.StepLR(optimizer, step_size, gamma=0.1, last_epoch=-1)參數(shù)：
- optimizer(Optimizer)：要調(diào)整學(xué)習(xí)率的優(yōu)化器
- step_size(int)：學(xué)習(xí)率調(diào)整的間隔epoch數(shù)
- gamma(float)：學(xué)習(xí)率調(diào)整的縮減比例
- last_epoch(int)：上一次調(diào)整學(xué)習(xí)率的時(shí)間點(diǎn)，默認(rèn)為-1
代碼示例：
optimizer = torch.optim.SGD(net.parameters(), lr=0.001 )
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)
2.torch.optim.lr_scheduler.LambdaLR
根據(jù)給定的函數(shù)動(dòng)態(tài)調(diào)整學(xué)習(xí)率。
函數(shù)原型：
torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda, last_epoch=-1)參數(shù)：
- optimizer(Optimizer)：要調(diào)整學(xué)習(xí)率的優(yōu)化器
- lr_lambda(function)：根據(jù)epoch返回一個(gè)值，作為學(xué)習(xí)率的倍數(shù)
- last_epoch(int)：上一次調(diào)整學(xué)習(xí)率的時(shí)間點(diǎn)，默認(rèn)為-1
代碼示例：
lambda1 = lambda epoch: (0.92 ** (epoch // 2) # 第二組參數(shù)的調(diào)整方法
optimizer = torch.optim.SGD(model.parameters(), lr=learn_rate)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1) #選定調(diào)整方法
3.torch.optim.lr_scheduler.MultiStepLR
等間隔動(dòng)態(tài)調(diào)整方法，在指定的epoch位置做一次學(xué)習(xí)率decay，以gamma值為縮小倍數(shù)。
函數(shù)原型：
torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones, gamma=0.1, last_epoch=-1)參數(shù)：
- optimizer(Optimizer)：要調(diào)整學(xué)習(xí)率的優(yōu)化器
- milestones(list)：學(xué)習(xí)率調(diào)整的epoch位置
- gamma(float)：學(xué)習(xí)率調(diào)整的縮減比例
- last_epoch(int)：上一次調(diào)整學(xué)習(xí)率的時(shí)間點(diǎn)，默認(rèn)為-1
代碼示例：
optimizer = torch.optim.SGD(net.parameters(), lr=0.001 )
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,milestones=[2,6,15],gamma=0.1)
七、個(gè)人收獲
在這個(gè)項(xiàng)目中，我首先準(zhǔn)備了數(shù)據(jù)，包括設(shè)置GPU環(huán)境、導(dǎo)入數(shù)據(jù)、劃分?jǐn)?shù)據(jù)集等。然后構(gòu)建了一個(gè)簡單的CNN網(wǎng)絡(luò)，用于對(duì)運(yùn)動(dòng)鞋品牌進(jìn)行識(shí)別。接著，我編寫了訓(xùn)練函數(shù)和測(cè)試函數(shù)，用于訓(xùn)練模型和評(píng)估模型性能。在訓(xùn)練過程中，我還使用了動(dòng)態(tài)學(xué)習(xí)率的方法，通過調(diào)整學(xué)習(xí)率來優(yōu)化模型訓(xùn)練過程。最后，我展示了訓(xùn)練過程中的損失和準(zhǔn)確率的變化情況，并對(duì)模型進(jìn)行了保存和加載，以便后續(xù)的使用。
通過這個(gè)項(xiàng)目，我深入了解了深度學(xué)習(xí)模型的訓(xùn)練流程，包括數(shù)據(jù)準(zhǔn)備、模型構(gòu)建、訓(xùn)練和評(píng)估，以及模型的保存和加載。同時(shí)，動(dòng)態(tài)學(xué)習(xí)率的應(yīng)用也豐富了我的訓(xùn)練優(yōu)化方法的知識(shí)儲(chǔ)備。這些知識(shí)將對(duì)我未來的深度學(xué)習(xí)項(xiàng)目產(chǎn)生積極的影響。
以上就是基于pytorch實(shí)現(xiàn)運(yùn)動(dòng)鞋品牌識(shí)別功能的詳細(xì)內(nèi)容，更多關(guān)于pytorch運(yùn)動(dòng)鞋品牌識(shí)別的資料請(qǐng)關(guān)注腳本之家其它相關(guān)文章！
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