Hi , I have git clone the repo and start training. But it ended immediately.
2020-04-27 11:30:23,193 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.stem.bn1.weight loaded from bn1.weight of shape (64,)
2020-04-27 11:30:23,193 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.stem.conv1.weight loaded from conv1.weight of shape (64, 3, 7, 7)
2020-04-27 11:30:23,288 maskrcnn_benchmark.data.build WARNING: When using more than one image per GPU you may encounter an out-of-memory (OOM) error if your GPU does not have sufficient memory. If this happens, you can reduce SOLVER.IMS_PER_BATCH (for training) or TEST.IMS_PER_BATCH (for inference). For training, you must also adjust the learning rate and schedule length according to the linear scaling rule. See for example: https://github.com/facebookresearch/Detectron/blob/master/configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml#L14
loading annotations into memory...
loading annotations into memory...
loading annotations into memory...
loading annotations into memory...
Done (t=0.22s)
creating index...
Done (t=0.22s)
creating index...
Done (t=0.22s)
creating index...
index created!
index created!
index created!
Done (t=0.19s)
creating index...
index created!
2020-04-27 11:30:23,782 maskrcnn_benchmark.trainer INFO: Start training
Nothing left. No errors, no training. Is there any solutions?
Hi , I have git clone the repo and start training. But it ended immediately.
2020-04-27 11:30:23,193 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.stem.bn1.weight loaded from bn1.weight of shape (64,)
2020-04-27 11:30:23,193 maskrcnn_benchmark.utils.model_serialization INFO: module.backbone.body.stem.conv1.weight loaded from conv1.weight of shape (64, 3, 7, 7)
2020-04-27 11:30:23,288 maskrcnn_benchmark.data.build WARNING: When using more than one image per GPU you may encounter an out-of-memory (OOM) error if your GPU does not have sufficient memory. If this happens, you can reduce SOLVER.IMS_PER_BATCH (for training) or TEST.IMS_PER_BATCH (for inference). For training, you must also adjust the learning rate and schedule length according to the linear scaling rule. See for example: https://github.com/facebookresearch/Detectron/blob/master/configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml#L14
loading annotations into memory...
loading annotations into memory...
loading annotations into memory...
loading annotations into memory...
Done (t=0.22s)
creating index...
Done (t=0.22s)
creating index...
Done (t=0.22s)
creating index...
index created!
index created!
index created!
Done (t=0.19s)
creating index...
index created!
2020-04-27 11:30:23,782 maskrcnn_benchmark.trainer INFO: Start training
Nothing left. No errors, no training. Is there any solutions?