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[原创]pytorch_CNN英数验证码识别模型训练
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发表于: 2024-10-14 21:13
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(pytorch_CNN英数验证码识别模型训练)
手把手训练模型系列
验证码样式
问题案例：
项目github：https://github.com/taisuii/ImageCaptchaOCR
验证码样式通常为通常为英文数字
解决方案或思路：
卷积神经网络，搭建模型，使其输出为5*(26+26+10)=310的张量，解码为英文和数字
数据集准备
整理数据集：
先关注公众号：R逆向，回复：验证码数据集
用两个文件夹，train存放数据集，test存放测试数据集，内容如下，标签就是图片名字
自定义数据集：
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | class mydatasets(Dataset): def __init__(self, root_dir): super(mydatasets, self).__init__() self.list_image_path = [os.path.join(root_dir, image_name) for image_name in os.listdir(root_dir)] self.transforms = transforms.Compose([ transforms.ToTensor() ]) def __getitem__(self, index): image_path = self.list_image_path[index] img_ = Image.open(image_path) image_name = image_path.split("\\")[-1] img_tesor = self.transforms(img_) img_lable = image_name.split(".")[0] img_lable = one_hot.text2vec(img_lable) img_lable = img_lable.view(1, -1)[0] return img_tesor, img_lable def __len__(self): return self.list_image_path.__len__() |
模型训练
模型代码如下：
随便写几个层，留意输入和输出，gen_ImageCaptcha.captcha_size * len(gen_ImageCaptcha.captcha_array)就是5*(26+26+10)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | class MyModel(nn.Module): def __init__(self): super(MyModel, self).__init__() self.layer1 = nn.Sequential( nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, padding=1), nn.BatchNorm2d(64), # 添加Batch Normalization nn.ReLU(), nn.MaxPool2d(kernel_size=2) ) self.layer2 = nn.Sequential( nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1), nn.BatchNorm2d(128), nn.ReLU(), nn.MaxPool2d(2) ) self.layer3 = nn.Sequential( nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1), nn.BatchNorm2d(256), nn.ReLU(), nn.MaxPool2d(2) ) self.layer4 = nn.Sequential( nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1), nn.BatchNorm2d(512), nn.ReLU(), nn.MaxPool2d(2) ) # 使用全局平均池化代替Flatten self.global_avg_pool = nn.AdaptiveAvgPool2d(1) self.fc = nn.Sequential( nn.Linear(512, 4096), nn.Dropout(0.2), nn.ReLU(), nn.Linear(4096, gen_ImageCaptcha.captcha_size * len(gen_ImageCaptcha.captcha_array)) ) def forward(self, x): x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.global_avg_pool(x) # 全局平均池化 x = x.view(x.size(0), -1) # 展平 x = self.fc(x) return x |
模型训练代码
这里的代码很常规，分类模型的训练，这里训练24个epoch
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | def train(epoch): train_datas = mydatasets("../dataset/train") train_dataloader = DataLoader(train_datas, batch_size=64, shuffle=True) m = MyModel().cuda() loss_fn = nn.MultiLabelSoftMarginLoss().cuda() optimizer = torch.optim.Adam(m.parameters(), lr=0.001) epoch_losses = [] for i in range(epoch): losses = [] # 迭代器进度条 data_loader_tqdm = tqdm(train_dataloader) epoch_loss = 0 for inputs, labels in data_loader_tqdm: inputs = inputs.cuda() labels = labels.cuda() optimizer.zero_grad() outputs = m(inputs) loss = loss_fn(outputs, labels) losses.append(loss.item()) epoch_loss = np.mean(losses) data_loader_tqdm.set_description( f"This epoch is {str(i + 1)} and it's loss is {loss.item()}, average loss {epoch_loss}" ) loss.backward() optimizer.step() epoch_losses.append(epoch_loss) # 每过一个batch就保存一次模型 torch.save(m.state_dict(), f'../deplo/model/{str(i + 1)}_{epoch_loss}.pth')if __name__ == '__main__': train(24) |
loss值变化如下：
调用
模型预测：
把模型的输出转换成字符，也就是每隔62就解码一个字符
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | def test_pred(): m = MyModel() m.load_state_dict(torch.load("../deploy/model/22_0.0007106820558649762.pth")) m.eval() test_data = mydatasets("../dataset/test") test_dataloader = DataLoader(test_data, batch_size=1, shuffle=False) test_length = test_data.__len__() correct = 0 for i, (imgs, lables) in enumerate(test_dataloader): imgs = imgs lables = lables lables = lables.view(-1, gen_ImageCaptcha.captcha_array.__len__()) lables_text = one_hot.vectotext(lables) start_time = time.time() predict_outputs = m(imgs) predict_outputs = predict_outputs.view(-1, gen_ImageCaptcha.captcha_array.__len__()) predict_labels = one_hot.vectotext(predict_outputs) print(time.time() - start_time) if predict_labels == lables_text: correct += 1 else: pass print("正确率{}".format(correct / test_length * 100))if __name__ == '__main__': test_pred() |
识别速度和平均成功率如下：
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