dmlc--dgl
Tong He
cbee427839
* add working scripts * add frcnn training script * remove redundent files * refactor validation computation, will optimize sgdet and training * validation finally finished * f-rcnn training * test reldn * rm file * update reldn training * data preprocess to h5 * temp * use coco json * fix conflict * new obj dataset for detection * update training * before cleanup * remove abundant files * add arg parse to train * cleanup code file * update * fix * add readme * add ipynb as demo * add demo pic * update readme * add demo script * improve paths * improve readme * add docstrings * fix args description * update readme * add models from s3 * update README Co-authored-by: Minjie Wang <minjie.wang@nyu.edu>
3 行
301 B
Bash
3 行
301 B
Bash
MXNET_CUDNN_AUTOTUNE_DEFAULT=0 CUDNN_AUTOTUNE_DEFAULT=0 MXNET_GPU_MEM_POOL_TYPE=Round MXNET_GPU_MEM_POOL_ROUND_LINEAR_CUTOFF=28 python train_faster_rcnn.py \
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--gpus 0,1,2,3,4,5,6,7 --dataset visualgenome -j 60 --batch-size 8 --val-interval 20 --save-prefix faster_rcnn_resnet101_v1d_visualgenome/
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