Comments (2)
You can train multiple images in per GPU. But you need to pad zeros into images to make them same size. At the same time, you need to update bounding box and mask respectively.
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@gaopeng-eugene I wanna increase minibatch by increaseing image per devices, can you tell me exactly how should I change the code?
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Related Issues (20)
- TypeError: _update_params_on_kvstore() takes exactly 4 arguments (3 given) HOT 1
- there is an error when trained on ResNet-50 model HOT 1
- Index out of bounds error in line 143 of proposal_annotator.py HOT 1
- Some problems encountered when using deconvolution
- mxnet.base.MXNetError: [15:20:32] src/engine/threaded_engine.cc:320: Check failed: device_count_ > 0 (-1 vs. 0) GPU usage requires at least 1 GPU HOT 1
- The result finetune with cityscapes is very poor
- Network structure
- When I train my own coco-like datasets,I successfully train and test the model,but the result of all AP is 0. HOT 5
- If my data is small,such as 6000 images,is it difficult to train the model? HOT 3
- my own data set ,test map is 0
- 有哪位遇到过训练成功,测试map=0的情况吗 HOT 3
- mxnet.base.MXNetError: [16:31:18] src/operator/nn/./cudnn/cudnn_convolution-inl.h:449: Check failed: e == CUDNN_STATUS_SUCCESS (3 vs. 0) cuDNN: CUDNN_STATUS_BAD_PARAM HOT 1
- pip install -r requirements.txt HOT 3
- unable to install ./init.sh error HOT 4
- Problems when launching demo.py
- Is it possible to run FCIS on AMD?
- Trouble building on Windows 10 (TypeError: expected str, bytes or os.PathLike object, not NoneType) HOT 2
- In-depth guide to getting FCIS working on Windows 10 HOT 3
- [HELP] problem with cpu_nms HOT 1
- Result file for COCO test-dev instance segmentation
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