Comments (5)
If you change the number of GPUs, I think both the batch-size and the number of iterations should be changed accordingly to ensure that the model is fully trained (to ensure fair comparison with this repo), see issue #19 for more details.
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@er-muyue What if the batch size is same, but the total number of GPU is changed. DO we need to modify the setting to reproduce the result?
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@er-muyue If I understand correctly, the stated batch size (which is 16) means total batch size instead of batch size per GPU. In the above experiments, I kept the total batch size unchanged (16) even though I changed the GPU number from 8 to 2.
In your last comment, do you mean to suggest changing the total batch size and number of iterations if I changed the GPU number? If yes, could you please suggest a direction to change (e.g. in 10-shot) so that the result gap between reported and reproduced can be reduced? Thanks.
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Just realized the result reported of Pascal voc is AP50 and result reported of coco is mAP. The result is able to be reproduced. Thanks.
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Hello, I would like to ask if your versions of python, cuda and pytorch are the same as the author's, because I use a 3090 graphics card, and the versions of cuda and pytorch cannot be consistent with the author's. Only the number of GPUs has been modified in the code, and the others have not been modified. The reproduced experimental results are far from the author's.Thank you very much.
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Related Issues (20)
- How can I customize new hyperparameters in detectron2
- argument 'alpha' must be Number, not NoneType HOT 2
- 可视化
- AssertionError: Checkpoint checkpoints/voc/mydefrcn/defrcn_det_r101_base1/model_reset_remove.pth not found! HOT 6
- Why the different number of images on inference?
- How to reproduce the results in Figure4(b)? HOT 1
- Help with multi-GPU training in Google Colab HOT 1
- Guide on Fine Tuning
- TypeError: init() got an unexpected keyword argument 'first_stride' HOT 2
- Where is pascal VOC metasplit located?
- Out of memory error during evaluation but training works fine! HOT 3
- running Inference on model
- Why final checkpoint is removed in run_voc.sh script? HOT 1
- Do we need to give support image during inference? HOT 1
- cuda version HOT 2
- About the fine tune problems
- main.py: error: unrecognized arguments: --opts MODEL.WEIGHTS /public/home/jd_fky/project/DeFRCN/data/ImageNetPretrained/MSRA/R-101.pkl OUTPUT_DIR checkpoints/coco/defrcn/defrcn_det_r101_base 3 I don't know why running main.py causes this problem HOT 1
- fine-tuning on my cutsomized dataset
- OutOfMemoryError with PrototypicalCalibrationBlock HOT 5
- RuntimeError: CUDA error: no kernel image is available for execution on the device
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