使用fasterrcnn\u resnet50\u fpn在Pytorch中进行迁移学习

我正在寻找PyTorch中自定义数据集的对象检测

本教程提供了一个片段，用于将预先训练的模型用于自定义对象分类

我尝试使用类似的方法，使用更快的rcnn模型进行目标检测

# 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抛出这些错误。首先，这种方法正确吗

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

0/24纪元
----------
---------------------------------------------------------------------------
ValueError回溯（最近一次调用上次）
在（）
---->1个模型=列车模型（模型、标准、优化器、exp\u lr\u调度器、num\u epochs=25）
2帧
/usr/local/lib/python3.6/dist-packages/torchvision/models/detection/generalized_rcnn.py in forward（自我、图像、目标）
43         """
44如果自我培训和目标为无：
--->45提升值错误（“在培训模式下，应通过目标”）
46原始图像大小=[img.shape[-2:]用于图像中的img]
47图像，目标=self.transform（图像，目标）
ValueError：在训练模式下，应通过目标

是否有办法修改此示例以进行自定义对象检测？

---

错误消息说明了一切。您需要传入一对

图像，target

来训练您的模型，其中

target

是一个包含边界框、标签和遮罩信息的字典

有关更多信息和全面教程，请参阅

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