https://github.com/deep-diver/vgg
VGG models from ILSVRC 2014
https://github.com/deep-diver/vgg
cifar-10 cifar-100 convolutional-neural-networks tensorflow vggnet
Last synced: about 1 year ago
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VGG models from ILSVRC 2014
- Host: GitHub
- URL: https://github.com/deep-diver/vgg
- Owner: deep-diver
- Created: 2018-08-14T08:20:34.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2022-10-16T03:30:29.000Z (over 3 years ago)
- Last Synced: 2025-03-31T01:51:17.027Z (about 1 year ago)
- Topics: cifar-10, cifar-100, convolutional-neural-networks, tensorflow, vggnet
- Language: Python
- Homepage: https://arxiv.org/pdf/1409.1556.pdf
- Size: 271 KB
- Stars: 4
- Watchers: 1
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# VGGs in TensorFlow
This repository comes with VGG implementation in TensorFlow. VGG took the 2nd place of the ILSVRC-2014 Competition.
Currently, the VGG models in this repo have been tested with CIFAR-10 and CIFAR-100 dataset. As an indivisual deep learner, it is hard to manage such a huge dataset, ImageNet. However, I will keep working on the ImageNet dataset, please wait for it.
VGG16 model example figure from [Ref.](https://blog.heuritech.com/2016/02/29/a-brief-report-of-the-heuritech-deep-learning-meetup-5/)
> **VGG:** Visual Geometry Group @Oxford University
## Required Packages
- scikit-images
- pickle
- tqdm
- numpy
- tensorflow-gpu (>1.7)
## Usage
- From command line
- Will download CIFAR-10 or CIFAR-100 dataset and pre-process of it, and run the training on VGG. It will produce the checkpoint file for performing inference later.
```
python vgg.py --model-type ['A'|'A-LRN'|'B'|'C'|'D'|'E'] --dataset ['cifar10'|'cifar100']
```
- From source code
```python
import cifar10_utils
import cifar100_utils
from vgg import VGG
...
valid_set = (valid_features, valid_labels)
...
# model type, D is the most well known VGG16 without 1D conv layer
# check the bottom section to see what model types are supported
vggNet = VGG(dataset='cifar10', model_type='D', learning_rate=0.0001)
vggNet.train(epochs=10,
batch_size=128,
valid_set=valid_set,
save_model_path='./model')
```
## Experiment on CIFAR-10 dataset (Not Yet Tested)
- Environment
- Floydhub GPU2 instance (1 x Tesla V100)
## Configurations
- **A :** 11 weight layers
- **A-LRN :** 11 weight layers with Local Response Normalization
- **B :** 13 weight layers
- **C :** 16 weight layers with 1D conv layers
- **D :** 16 weight layers
- **E :** 19 weight layers
