# tensorflow_multigpu_imagenet

**Repository Path**: wangfuchao/tensorflow_multigpu_imagenet

## Basic Information

- **Project Name**: tensorflow_multigpu_imagenet
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2018-07-11
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# tensorflow_multigpu_imagenet
Code for training different architectures( DenseNet, ResNet, AlexNet, GoogLeNet, VGG, NiN) on ImageNet or other datasets + 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 reads dataset information from a text or csv file and directly loads images from disk. Moreover, multi-GPU and transfer learning are supported.

This code takes advantage of 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 --architecture alexnet --path_prefix /project/datasets/imagenet/train/ --data_info train.txt

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/ --data_info val.txt

Transfer learning:

python train.py --transfer_mode 1 --architecture alexnet --retrain_from ./alexnet_Run-17-07-2017-15:31:57