Comments (1)
I got one question about the test result after a scrutiny of this paper. Did you train these comparison models based on pytorch? If so, have you tuned hyperparameters in accord with official documents?
Thanks
Specifically,ResNet101-SSD is re-trained and tested, cornernet and refinedet are tested based on their official provided models.
Calculating the ACC is with no controversy(mmAP), while computing the speed always exists divergence.
At the same time when we release the M2Det code, we will give a new speed comparison chart under fair conditions.
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Related Issues (20)
- I wrote how to run on mac os.
- The test for coco2017 and get mAP 0.0? model is m2det512_vgg.pth
- Someone have test result on VOC2007 ? HOT 5
- train error HOT 5
- 论文中有一处错误
- Are you using batch size 8 for bn update?
- Why adjust_learning_rate() only refers to step_lr of COCO? HOT 1
- can anyone explain what is the loss_L and loss_c in the training results ? HOT 1
- AttributeError: 'CustomDataset' object has no attribute 'evaluate_detections'AttributeError: 'CustomDataset' object has no attribute 'evaluate_detections'
- Two question about upsample process
- error when run demo.py
- RuntimeError: all tensors must be on devices[0] HOT 1
- mAP精度问题
- On which dataset was m2det512_vgg.pth trained?
- Hard Negative Mining problem
- demo | qt.qpa.xcb: could not connect to display HOT 1
- Does anyone have the weight of resnet101 HOT 1
- File "home/CLNet/main.py", line 87, in <module> main () File "home/CLNet/main.py", line 25, in main model = init_model(args) File "/content/drive/My Drive/home/CLNet/utils/init.py", line 51, in init_model model.load_state_dict(state_dict) File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1407, in load_state_dict self.__class__.__name__, "\n\t".join(error_msgs))) RuntimeError: Error(s) in loading state_dict for CLNet: HOT 1
- GFLOPs!!!
- ImportError: /home/wdc/M2Det/utils/nms/gpu_nms.cpython-37m-x86_64-linux-gnu.so: undefined symbol: cudaSetupArgument
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