{"action":"opened","issue":{"url":"https://api.github.com/repos/jwyang/faster-rcnn.pytorch/issues/293","repository_url":"https://api.github.com/repos/jwyang/faster-rcnn.pytorch","labels_url":"https://api.github.com/repos/jwyang/faster-rcnn.pytorch/issues/293/labels{/name}","comments_url":"https://api.github.com/repos/jwyang/faster-rcnn.pytorch/issues/293/comments","events_url":"https://api.github.com/repos/jwyang/faster-rcnn.pytorch/issues/293/events","html_url":"https://github.com/jwyang/faster-rcnn.pytorch/issues/293","id":356726293,"node_id":"MDU6SXNzdWUzNTY3MjYyOTM=","number":293,"title":"fasterRCNN.load_state_dict(checkpoint['model']) KeyError: 'model'","user":{"login":"xwjBupt","id":35280631,"node_id":"MDQ6VXNlcjM1MjgwNjMx","avatar_url":"https://avatars3.githubusercontent.com/u/35280631?v=4","gravatar_id":"","url":"https://api.github.com/users/xwjBupt","html_url":"https://github.com/xwjBupt","followers_url":"https://api.github.com/users/xwjBupt/followers","following_url":"https://api.github.com/users/xwjBupt/following{/other_user}","gists_url":"https://api.github.com/users/xwjBupt/gists{/gist_id}","starred_url":"https://api.github.com/users/xwjBupt/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/xwjBupt/subscriptions","organizations_url":"https://api.github.com/users/xwjBupt/orgs","repos_url":"https://api.github.com/users/xwjBupt/repos","events_url":"https://api.github.com/users/xwjBupt/events{/privacy}","received_events_url":"https://api.github.com/users/xwjBupt/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-09-04T09:28:36Z","updated_at":"2018-09-04T09:28:36Z","closed_at":null,"author_association":"NONE","body":"Hi @jwyang ,sorry to bother you,i see you closed a similar question fasterRCNN.load_state_dict(checkpoint['model']) #30,in that issue,you said you have update the link of pretrained model ,but when i download the vgg16 pretrained model from the link in the readme today,and  load the model,i found the model do not the 'model' key ,so i print checkpoint.keys() ,it has:\r\n\r\nload checkpoint /media/xwj/Programm/Python/faster-rcnn.pytorch/data/pretrained_model/resnet101_caffe.pth\r\nodict_keys(['conv1.weight', 'bn1.weight', 'bn1.bias', 'bn1.running_mean', 'bn1.running_var', 'layer1.0.conv1.weight', 'layer1.0.bn1.weight', 'layer1.0.bn1.bias', 'layer1.0.bn1.running_mean', 'layer1.0.bn1.running_var', 'layer1.0.conv2.weight', 'layer1.0.bn2.weight', 'layer1.0.bn2.bias', 'layer1.0.bn2.running_mean', 'layer1.0.bn2.running_var', 'layer1.0.conv3.weight', 'layer1.0.bn3.weight', 'layer1.0.bn3.bias', 'layer1.0.bn3.running_mean', 'layer1.0.bn3.running_var', 'layer1.0.downsample.0.weight', 'layer1.0.downsample.1.weight', 'layer1.0.downsample.1.bias', 'layer1.0.downsample.1.running_mean', 'layer1.0.downsample.1.running_var', 'layer1.1.conv1.weight', 'layer1.1.bn1.weight', 'layer1.1.bn1.bias', 'layer1.1.bn1.running_mean', 'layer1.1.bn1.running_var', 'layer1.1.conv2.weight', 'layer1.1.bn2.weight', 'layer1.1.bn2.bias', 'layer1.1.bn2.running_mean', 'layer1.1.bn2.running_var', 'layer1.1.conv3.weight', 'layer1.1.bn3.weight', 'layer1.1.bn3.bias', 'layer1.1.bn3.running_mean', 'layer1.1.bn3.running_var', 'layer1.2.conv1.weight', 'layer1.2.bn1.weight', 'layer1.2.bn1.bias', 'layer1.2.bn1.running_mean', 'layer1.2.bn1.running_var', 'layer1.2.conv2.weight', 'layer1.2.bn2.weight', 'layer1.2.bn2.bias', 'layer1.2.bn2.running_mean', 'layer1.2.bn2.running_var', 'layer1.2.conv3.weight', 'layer1.2.bn3.weight', 'layer1.2.bn3.bias', 'layer1.2.bn3.running_mean', 'layer1.2.bn3.running_var', 'layer2.0.conv1.weight', 'layer2.0.bn1.weight', 'layer2.0.bn1.bias', 'layer2.0.bn1.running_mean', 'layer2.0.bn1.running_var', 'layer2.0.conv2.weight', 'layer2.0.bn2.weight', 'layer2.0.bn2.bias', 'layer2.0.bn2.running_mean', 'layer2.0.bn2.running_var', 'layer2.0.conv3.weight', 'layer2.0.bn3.weight', 'layer2.0.bn3.bias', 'layer2.0.bn3.running_mean', 'layer2.0.bn3.running_var', 'layer2.0.downsample.0.weight', 'layer2.0.downsample.1.weight', 'layer2.0.downsample.1.bias', 'layer2.0.downsample.1.running_mean', 'layer2.0.downsample.1.running_var', 'layer2.1.conv1.weight', 'layer2.1.bn1.weight', 'layer2.1.bn1.bias', 'layer2.1.bn1.running_mean', 'layer2.1.bn1.running_var', 'layer2.1.conv2.weight', 'layer2.1.bn2.weight', 'layer2.1.bn2.bias', 'layer2.1.bn2.running_mean', 'layer2.1.bn2.running_var', 'layer2.1.conv3.weight', 'layer2.1.bn3.weight', 'layer2.1.bn3.bias', 'layer2.1.bn3.running_mean', 'layer2.1.bn3.running_var', 'layer2.2.conv1.weight', 'layer2.2.bn1.weight', 'layer2.2.bn1.bias', 'layer2.2.bn1.running_mean', 'layer2.2.bn1.running_var', 'layer2.2.conv2.weight', 'layer2.2.bn2.weight', 'layer2.2.bn2.bias', 'layer2.2.bn2.running_mean', 'layer2.2.bn2.running_var', 'layer2.2.conv3.weight', 'layer2.2.bn3.weight', 'layer2.2.bn3.bias', 'layer2.2.bn3.running_mean', 'layer2.2.bn3.running_var', 'layer2.3.conv1.weight', 'layer2.3.bn1.weight', 'layer2.3.bn1.bias', 'layer2.3.bn1.running_mean', 'layer2.3.bn1.running_var', 'layer2.3.conv2.weight', 'layer2.3.bn2.weight', 'layer2.3.bn2.bias', 'layer2.3.bn2.running_mean', 'layer2.3.bn2.running_var', 'layer2.3.conv3.weight', 'layer2.3.bn3.weight', 'layer2.3.bn3.bias', 'layer2.3.bn3.running_mean', 'layer2.3.bn3.running_var', 'layer3.0.conv1.weight', 'layer3.0.bn1.weight', 'layer3.0.bn1.bias', 'layer3.0.bn1.running_mean', 'layer3.0.bn1.running_var', 'layer3.0.conv2.weight', 'layer3.0.bn2.weight', 'layer3.0.bn2.bias', 'layer3.0.bn2.running_mean', 'layer3.0.bn2.running_var', 'layer3.0.conv3.weight', 'layer3.0.bn3.weight', 'layer3.0.bn3.bias', 'layer3.0.bn3.running_mean', 'layer3.0.bn3.running_var', 'layer3.0.downsample.0.weight', 'layer3.0.downsample.1.weight', 'layer3.0.downsample.1.bias', 'layer3.0.downsample.1.running_mean', 'layer3.0.downsample.1.running_var', 'layer3.1.conv1.weight', 'layer3.1.bn1.weight', 'layer3.1.bn1.bias', 'layer3.1.bn1.running_mean', 'layer3.1.bn1.running_var', 'layer3.1.conv2.weight', 'layer3.1.bn2.weight', 'layer3.1.bn2.bias', 'layer3.1.bn2.running_mean', 'layer3.1.bn2.running_var', 'layer3.1.conv3.weight', 'layer3.1.bn3.weight', 'layer3.1.bn3.bias', 'layer3.1.bn3.running_mean', 'layer3.1.bn3.running_var', 'layer3.2.conv1.weight', 'layer3.2.bn1.weight', 'layer3.2.bn1.bias', 'layer3.2.bn1.running_mean', 'layer3.2.bn1.running_var', 'layer3.2.conv2.weight', 'layer3.2.bn2.weight', 'layer3.2.bn2.bias', 'layer3.2.bn2.running_mean', 'layer3.2.bn2.running_var', 'layer3.2.conv3.weight', 'layer3.2.bn3.weight', 'layer3.2.bn3.bias', 'layer3.2.bn3.running_mean', 'layer3.2.bn3.running_var', 'layer3.3.conv1.weight', 'layer3.3.bn1.weight', 'layer3.3.bn1.bias', 'layer3.3.bn1.running_mean', 'layer3.3.bn1.running_var', 'layer3.3.conv2.weight', 'layer3.3.bn2.weight', 'layer3.3.bn2.bias', 'layer3.3.bn2.running_mean', 'layer3.3.bn2.running_var', 'layer3.3.conv3.weight', 'layer3.3.bn3.weight', 'layer3.3.bn3.bias', 'layer3.3.bn3.running_mean', 'layer3.3.bn3.running_var', 'layer3.4.conv1.weight', 'layer3.4.bn1.weight', 'layer3.4.bn1.bias', 'layer3.4.bn1.running_mean', 'layer3.4.bn1.running_var', 'layer3.4.conv2.weight', 'layer3.4.bn2.weight', 'layer3.4.bn2.bias', 'layer3.4.bn2.running_mean', 'layer3.4.bn2.running_var', 'layer3.4.conv3.weight', 'layer3.4.bn3.weight', 'layer3.4.bn3.bias', 'layer3.4.bn3.running_mean', 'layer3.4.bn3.running_var', 'layer3.5.conv1.weight', 'layer3.5.bn1.weight', 'layer3.5.bn1.bias', 'layer3.5.bn1.running_mean', 'layer3.5.bn1.running_var', 'layer3.5.conv2.weight', 'layer3.5.bn2.weight', 'layer3.5.bn2.bias', 'layer3.5.bn2.running_mean', 'layer3.5.bn2.running_var', 'layer3.5.conv3.weight', 'layer3.5.bn3.weight', 'layer3.5.bn3.bias', 'layer3.5.bn3.running_mean', 'layer3.5.bn3.running_var', 'layer3.6.conv1.weight', 'layer3.6.bn1.weight', 'layer3.6.bn1.bias', 'layer3.6.bn1.running_mean', 'layer3.6.bn1.running_var', 'layer3.6.conv2.weight', 'layer3.6.bn2.weight', 'layer3.6.bn2.bias', 'layer3.6.bn2.running_mean', 'layer3.6.bn2.running_var', 'layer3.6.conv3.weight', 'layer3.6.bn3.weight', 'layer3.6.bn3.bias', 'layer3.6.bn3.running_mean', 'layer3.6.bn3.running_var', 'layer3.7.conv1.weight', 'layer3.7.bn1.weight', 'layer3.7.bn1.bias', 'layer3.7.bn1.running_mean', 'layer3.7.bn1.running_var', 'layer3.7.conv2.weight', 'layer3.7.bn2.weight', 'layer3.7.bn2.bias', 'layer3.7.bn2.running_mean', 'layer3.7.bn2.running_var', 'layer3.7.conv3.weight', 'layer3.7.bn3.weight', 'layer3.7.bn3.bias', 'layer3.7.bn3.running_mean', 'layer3.7.bn3.running_var', 'layer3.8.conv1.weight', 'layer3.8.bn1.weight', 'layer3.8.bn1.bias', 'layer3.8.bn1.running_mean', 'layer3.8.bn1.running_var', 'layer3.8.conv2.weight', 'layer3.8.bn2.weight', 'layer3.8.bn2.bias', 'layer3.8.bn2.running_mean', 'layer3.8.bn2.running_var', 'layer3.8.conv3.weight', 'layer3.8.bn3.weight', 'layer3.8.bn3.bias', 'layer3.8.bn3.running_mean', 'layer3.8.bn3.running_var', 'layer3.9.conv1.weight', 'layer3.9.bn1.weight', 'layer3.9.bn1.bias', 'layer3.9.bn1.running_mean', 'layer3.9.bn1.running_var', 'layer3.9.conv2.weight', 'layer3.9.bn2.weight', 'layer3.9.bn2.bias', 'layer3.9.bn2.running_mean', 'layer3.9.bn2.running_var', 'layer3.9.conv3.weight', 'layer3.9.bn3.weight', 'layer3.9.bn3.bias', 'layer3.9.bn3.running_mean', 'layer3.9.bn3.running_var', 'layer3.10.conv1.weight', 'layer3.10.bn1.weight', 'layer3.10.bn1.bias', 'layer3.10.bn1.running_mean', 'layer3.10.bn1.running_var', 'layer3.10.conv2.weight', 'layer3.10.bn2.weight', 'layer3.10.bn2.bias', 'layer3.10.bn2.running_mean', 'layer3.10.bn2.running_var', 'layer3.10.conv3.weight', 'layer3.10.bn3.weight', 'layer3.10.bn3.bias', 'layer3.10.bn3.running_mean', 'layer3.10.bn3.running_var', 'layer3.11.conv1.weight', 'layer3.11.bn1.weight', 'layer3.11.bn1.bias', 'layer3.11.bn1.running_mean', 'layer3.11.bn1.running_var', 'layer3.11.conv2.weight', 'layer3.11.bn2.weight', 'layer3.11.bn2.bias', 'layer3.11.bn2.running_mean', 'layer3.11.bn2.running_var', 'layer3.11.conv3.weight', 'layer3.11.bn3.weight', 'layer3.11.bn3.bias', 'layer3.11.bn3.running_mean', 'layer3.11.bn3.running_var', 'layer3.12.conv1.weight', 'layer3.12.bn1.weight', 'layer3.12.bn1.bias', 'layer3.12.bn1.running_mean', 'layer3.12.bn1.running_var', 'layer3.12.conv2.weight', 'layer3.12.bn2.weight', 'layer3.12.bn2.bias', 'layer3.12.bn2.running_mean', 'layer3.12.bn2.running_var', 'layer3.12.conv3.weight', 'layer3.12.bn3.weight', 'layer3.12.bn3.bias', 'layer3.12.bn3.running_mean', 'layer3.12.bn3.running_var', 'layer3.13.conv1.weight', 'layer3.13.bn1.weight', 'layer3.13.bn1.bias', 'layer3.13.bn1.running_mean', 'layer3.13.bn1.running_var', 'layer3.13.conv2.weight', 'layer3.13.bn2.weight', 'layer3.13.bn2.bias', 'layer3.13.bn2.running_mean', 'layer3.13.bn2.running_var', 'layer3.13.conv3.weight', 'layer3.13.bn3.weight', 'layer3.13.bn3.bias', 'layer3.13.bn3.running_mean', 'layer3.13.bn3.running_var', 'layer3.14.conv1.weight', 'layer3.14.bn1.weight', 'layer3.14.bn1.bias', 'layer3.14.bn1.running_mean', 'layer3.14.bn1.running_var', 'layer3.14.conv2.weight', 'layer3.14.bn2.weight', 'layer3.14.bn2.bias', 'layer3.14.bn2.running_mean', 'layer3.14.bn2.running_var', 'layer3.14.conv3.weight', 'layer3.14.bn3.weight', 'layer3.14.bn3.bias', 'layer3.14.bn3.running_mean', 'layer3.14.bn3.running_var', 'layer3.15.conv1.weight', 'layer3.15.bn1.weight', 'layer3.15.bn1.bias', 'layer3.15.bn1.running_mean', 'layer3.15.bn1.running_var', 'layer3.15.conv2.weight', 'layer3.15.bn2.weight', 'layer3.15.bn2.bias', 'layer3.15.bn2.running_mean', 'layer3.15.bn2.running_var', 'layer3.15.conv3.weight', 'layer3.15.bn3.weight', 'layer3.15.bn3.bias', 'layer3.15.bn3.running_mean', 'layer3.15.bn3.running_var', 'layer3.16.conv1.weight', 'layer3.16.bn1.weight', 'layer3.16.bn1.bias', 'layer3.16.bn1.running_mean', 'layer3.16.bn1.running_var', 'layer3.16.conv2.weight', 'layer3.16.bn2.weight', 'layer3.16.bn2.bias', 'layer3.16.bn2.running_mean', 'layer3.16.bn2.running_var', 'layer3.16.conv3.weight', 'layer3.16.bn3.weight', 'layer3.16.bn3.bias', 'layer3.16.bn3.running_mean', 'layer3.16.bn3.running_var', 'layer3.17.conv1.weight', 'layer3.17.bn1.weight', 'layer3.17.bn1.bias', 'layer3.17.bn1.running_mean', 'layer3.17.bn1.running_var', 'layer3.17.conv2.weight', 'layer3.17.bn2.weight', 'layer3.17.bn2.bias', 'layer3.17.bn2.running_mean', 'layer3.17.bn2.running_var', 'layer3.17.conv3.weight', 'layer3.17.bn3.weight', 'layer3.17.bn3.bias', 'layer3.17.bn3.running_mean', 'layer3.17.bn3.running_var', 'layer3.18.conv1.weight', 'layer3.18.bn1.weight', 'layer3.18.bn1.bias', 'layer3.18.bn1.running_mean', 'layer3.18.bn1.running_var', 'layer3.18.conv2.weight', 'layer3.18.bn2.weight', 'layer3.18.bn2.bias', 'layer3.18.bn2.running_mean', 'layer3.18.bn2.running_var', 'layer3.18.conv3.weight', 'layer3.18.bn3.weight', 'layer3.18.bn3.bias', 'layer3.18.bn3.running_mean', 'layer3.18.bn3.running_var', 'layer3.19.conv1.weight', 'layer3.19.bn1.weight', 'layer3.19.bn1.bias', 'layer3.19.bn1.running_mean', 'layer3.19.bn1.running_var', 'layer3.19.conv2.weight', 'layer3.19.bn2.weight', 'layer3.19.bn2.bias', 'layer3.19.bn2.running_mean', 'layer3.19.bn2.running_var', 'layer3.19.conv3.weight', 'layer3.19.bn3.weight', 'layer3.19.bn3.bias', 'layer3.19.bn3.running_mean', 'layer3.19.bn3.running_var', 'layer3.20.conv1.weight', 'layer3.20.bn1.weight', 'layer3.20.bn1.bias', 'layer3.20.bn1.running_mean', 'layer3.20.bn1.running_var', 'layer3.20.conv2.weight', 'layer3.20.bn2.weight', 'layer3.20.bn2.bias', 'layer3.20.bn2.running_mean', 'layer3.20.bn2.running_var', 'layer3.20.conv3.weight', 'layer3.20.bn3.weight', 'layer3.20.bn3.bias', 'layer3.20.bn3.running_mean', 'layer3.20.bn3.running_var', 'layer3.21.conv1.weight', 'layer3.21.bn1.weight', 'layer3.21.bn1.bias', 'layer3.21.bn1.running_mean', 'layer3.21.bn1.running_var', 'layer3.21.conv2.weight', 'layer3.21.bn2.weight', 'layer3.21.bn2.bias', 'layer3.21.bn2.running_mean', 'layer3.21.bn2.running_var', 'layer3.21.conv3.weight', 'layer3.21.bn3.weight', 'layer3.21.bn3.bias', 'layer3.21.bn3.running_mean', 'layer3.21.bn3.running_var', 'layer3.22.conv1.weight', 'layer3.22.bn1.weight', 'layer3.22.bn1.bias', 'layer3.22.bn1.running_mean', 'layer3.22.bn1.running_var', 'layer3.22.conv2.weight', 'layer3.22.bn2.weight', 'layer3.22.bn2.bias', 'layer3.22.bn2.running_mean', 'layer3.22.bn2.running_var', 'layer3.22.conv3.weight', 'layer3.22.bn3.weight', 'layer3.22.bn3.bias', 'layer3.22.bn3.running_mean', 'layer3.22.bn3.running_var', 'layer4.0.conv1.weight', 'layer4.0.bn1.weight', 'layer4.0.bn1.bias', 'layer4.0.bn1.running_mean', 'layer4.0.bn1.running_var', 'layer4.0.conv2.weight', 'layer4.0.bn2.weight', 'layer4.0.bn2.bias', 'layer4.0.bn2.running_mean', 'layer4.0.bn2.running_var', 'layer4.0.conv3.weight', 'layer4.0.bn3.weight', 'layer4.0.bn3.bias', 'layer4.0.bn3.running_mean', 'layer4.0.bn3.running_var', 'layer4.0.downsample.0.weight', 'layer4.0.downsample.1.weight', 'layer4.0.downsample.1.bias', 'layer4.0.downsample.1.running_mean', 'layer4.0.downsample.1.running_var', 'layer4.1.conv1.weight', 'layer4.1.bn1.weight', 'layer4.1.bn1.bias', 'layer4.1.bn1.running_mean', 'layer4.1.bn1.running_var', 'layer4.1.conv2.weight', 'layer4.1.bn2.weight', 'layer4.1.bn2.bias', 'layer4.1.bn2.running_mean', 'layer4.1.bn2.running_var', 'layer4.1.conv3.weight', 'layer4.1.bn3.weight', 'layer4.1.bn3.bias', 'layer4.1.bn3.running_mean', 'layer4.1.bn3.running_var', 'layer4.2.conv1.weight', 'layer4.2.bn1.weight', 'layer4.2.bn1.bias', 'layer4.2.bn1.running_mean', 'layer4.2.bn1.running_var', 'layer4.2.conv2.weight', 'layer4.2.bn2.weight', 'layer4.2.bn2.bias', 'layer4.2.bn2.running_mean', 'layer4.2.bn2.running_var', 'layer4.2.conv3.weight', 'layer4.2.bn3.weight', 'layer4.2.bn3.bias', 'layer4.2.bn3.running_mean', 'layer4.2.bn3.running_var', 'fc.weight', 'fc.bias'])\r\nTraceback (most recent call last):\r\n  File \"/media/xwj/Programm/Python/faster-rcnn.pytorch/demo.py\", line 199, in <module>\r\n    fasterRCNN.load_state_dict(checkpoint['model'])\r\nKeyError: 'model'\r\n\r\nwhere is the [model]? have i done something wrong?\r\nThank you!"}}
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