Comments (3)
@johnlanbor If you use exactly the same environment as maskrcnn-benchmark for FCOS, can you run it normally? We did not change any codes in these c++ files, and therefore I think it should work well in the environment where maskrcnn-benchmark can work.
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@tianzhi0549 I compile maskrcnn benchmark and FCOS in the same environment, but FCOS cant compile correctly while benchmark can .
when i copy FCOS code to benchmark, and train with this setting: fcos_R_50_FPN_1x.yaml
when test woth 65000 pth can got AP:36
but I got loss(nan) when 79800 iter, is that because the lr? I use 4Gpus , so the lr should be half of 0.01?
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@johnlanbor You don't need to change any configs because of 4 GPUs. The number of GPUs does not change the batch size.
The loss might become NAN occasionally. You can try to continue training from your last checkpoint.
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Related Issues (20)
- Questions about details of inference speed.
- Can I use it on CPU?
- Can we use this FCOS algorithm for counting the objects in images??
- May I ask you for a train log file?
- Which is the accuracy index in the paper?
- Error pip install git+https://github.com/tianzhi0549/FCOS.git
- use my dataset to train FCOS net,why the loss is nan? HOT 2
- Can’t calculate the Params and FLOPs of Backbone
- Testing with test dataset while training
- ValueError: Unknown CUDA arch (8.6) or GPU not supported HOT 2
- weird time consuming of FCOS head when changing the backbone
- Problem with the last line of Testing-only installation HOT 1
- Deepcopy returns TypeError: 'int' object is not callable HOT 1
- assert len(proposal_losses) == 1 and proposal_losses["zero"] == 0 # loss_dict should be empty dict
- tools/train_net.py FAILED HOT 1
- Convert onnx
- Segmentation fault (core dumped)
- gempy to load csv data
- ImportError: cannot import name '_C'
- Why regression head wasn't normalized ?
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