使用pytorch在Pytorch中进行转移学习，并使用fasterrcnn_resnet50_fpn

Question

我正在寻找pytorch中自定义数据集的对象检测。

教程提供了一个用于使用预训练的模型的摘要分类

model_ft = models.resnet18(pretrained=True)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 2)
model_ft = model_ft.to(device)
criterion = nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
                   num_epochs=25)

我尝试使用更快的RCNN模型对对象检测使用类似的方法。

# load a model pre-trained pre-trained on COCO
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
model.eval()
for param in model.parameters():
    param.requires_grad = False
# replace the classifier with a new one, that has
# num_classes which is user-defined
num_classes = 1  # 1 class (person) + background
print(model)
model = model.to(device)
criterion = nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
model = train_model(model, criterion, optimizer_ft, exp_lr_scheduler,num_epochs=25)

pytorch丢弃了这些错误。这种方法首先正确吗？

Epoch 0/24
----------
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-69-527ca4db8e5d> in <module>()
----> 1 model = train_model(model, criterion, optimizer_ft, exp_lr_scheduler,num_epochs=25)
2 frames
/usr/local/lib/python3.6/dist-packages/torchvision/models/detection/generalized_rcnn.py in forward(self, images, targets)
     43         """
     44         if self.training and targets is None:
---> 45             raise ValueError("In training mode, targets should be passed")
     46         original_image_sizes = [img.shape[-2:] for img in images]
     47         images, targets = self.transform(images, targets)
ValueError: In training mode, targets should be passed

是否有一种方法修改此示例以进行自定义对象检测？https://www.learnopencv.com/faster-r-cnn-object-detection-with-pytorch/

Answer 1

错误消息说明了一切。您需要传递一对image, target来训练模型，其中target。是一个包含有关边界框，标签和口罩的信息。

有关更多信息和全面的教程，请查看https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html

Answer 2

如果要检测一个人和背景，则必须将num_classes设置为2。

要训练您的自定义检测模型，您需要通过图像(每个像素在0和1之间(和目标。您可以关注此Kaggle教程：https：//www.kaggle.com/abhishek/training-fast-rcnn-using-torchvision