tensorflow_multigpu_imagenet
Code for training different architectures( DenseNet, ResNet, AlexNet, GoogLeNet, VGG, NiN) on ImageNet dataset + Multi-GPU support + Transfer Learning support
This repository provides an easy-to-use way for training different well-known deep learning architectures on different datasets. The code directly load images from disk. Moreover, multi-GPU and transfer learning is also supported. This code is mainly based on these repositories:
https://github.com/soumith/imagenet-multiGPU.torch
https://github.com/ry/tensorflow-resnet
https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10
#Example of usages:
Training:
python train.py --path_prefix /project/datasets/imagenet/train/
Evaluating a trained model:
python eval.py --num_threads 8 --architecture alexnet --log_dir "alexnet_Run-17-07-2017-15:31:57" --path_prefix /project/datasets/imagenet/train/
Transfer learning:
python transfer.py --architecture alexnet --load_pretrained_dir ./alexnet_Run-17-07-2017-15:31:57
Evaluate a transferred model:
python transfer_eval.py --num_threads 4 --architecture alexnet --log_dir ./alexnet_Run-18-07-2017-14:08:14 --delimiter , --save_predictions trnpred.txt --path_prefix /project/dataset2