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Play deep learning with CIFAR datasets
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# Convolutional Neural Networks for CIFAR-10
This repository is about some implementations of CNN Architecture for **cifar10**.
![cifar10][1]
I just use **Keras** and **Tensorflow** to implementate all of these CNN models.
~~(maybe torch/pytorch version if I have time)~~
**A pytorch version is available at [CIFAR-ZOO](https://github.com/BIGBALLON/CIFAR-ZOO)**
## Requirements
- Python (3.5)
- keras (>= 2.1.5)
- tensorflow-gpu (>= 1.4.1)
## Architectures and papers
- The first CNN model: **LeNet**
- [LeNet-5 - Yann LeCun][2]
- **Network in Network**
- [Network In Network][3]
- **Vgg19 Network**
- [Very Deep Convolutional Networks for Large-Scale Image Recognition][4]
- The **1st places** in ILSVRC 2014 localization tasks
- The **2nd places** in ILSVRC 2014 classification tasks
- **Residual Network**
- [Deep Residual Learning for Image Recognition][5]
- [Identity Mappings in Deep Residual Networks][6]
- **CVPR 2016 Best Paper Award**
- **1st places** in all five main tracks:
- ILSVRC 2015 Classification: "Ultra-deep" 152-layer nets
- ILSVRC 2015 Detection: 16% better than 2nd
- ILSVRC 2015 Localization: 27% better than 2nd
- COCO Detection: 11% better than 2nd
- COCO Segmentation: 12% better than 2nd
- **Wide Residual Network**
- [Wide Residual Networks][7]
- **ResNeXt**
- [Aggregated Residual Transformations for Deep Neural Networks][8]
- Used in [Mask-RCNN][9]
- **DenseNet**
- [Densely Connected Convolutional Networks][10]
- **CVPR 2017 Best Paper Award**
- **SENet**
- [Squeeze-and-Excitation Networks][11]
- **The 1st places** in ILSVRC 2017 classification tasks
## Documents & tutorials
There are also some documents and tutorials in [doc][12] & [issues/3][13].
Get it if you need.
You can also see the [articles][14] if you can speak Chinese. <img src="https://user-images.githubusercontent.com/7837172/44953504-b9481000-aec8-11e8-9920-abf66365b8d8.gif">
## Accuracy of all my implementations
**In particular**：
Change the batch size according to your GPU's memory.
Modify the learning rate schedule may imporve the results of accuracy!
| network | GPU | params | batch size | epoch | training time | accuracy(%) |
|:----------------------|:---------:|:-------:|:----------:|:-----:|:-------------:|:-----------:|
| Lecun-Network | GTX1080TI | 62k | 128 | 200 | 30 min | 76.23 |
| Network-in-Network | GTX1080TI | 0.97M | 128 | 200 | 1 h 40 min | 91.63 |
| Vgg19-Network | GTX1080TI | 39M | 128 | 200 | 1 h 53 min | 93.53 |
| Residual-Network20 | GTX1080TI | 0.27M | 128 | 200 | 44 min | 91.82 |
| Residual-Network32 | GTX1080TI | 0.47M | 128 | 200 | 1 h 7 min | 92.68 |
| Residual-Network110 | GTX1080TI | 1.7M | 128 | 200 | 3 h 38 min | 93.93 |
| Wide-resnet 16x8 | GTX1080TI | 11.3M | 128 | 200 | 4 h 55 min | 95.13 |
| Wide-resnet 28x10 | GTX1080TI | 36.5M | 128 | 200 | 10 h 22 min | 95.78 |
| DenseNet-100x12 | GTX1080TI | 0.85M | 64 | 250 | 17 h 20 min | 94.91 |
| DenseNet-100x24 | GTX1080TI | 3.3M | 64 | 250 | 22 h 27 min | 95.30 |
| DenseNet-160x24 | 1080 x 2 | 7.5M | 64 | 250 | 50 h 20 min | 95.90 |
| ResNeXt-4x64d | GTX1080TI | 20M | 120 | 250 | 21 h 3 min | 95.19 |
| SENet(ResNeXt-4x64d) | GTX1080TI | 20M | 120 | 250 | 21 h 57 min | 95.60 |
## About LeNet and CNN training tips/tricks
LeNet is the first CNN network proposed by LeCun.
I used different CNN training tricks to show you how to train your model efficiently.
``LeNet_keraExcerpt of 7,607 characters
Read on GitHub
WILL LEE · NCTU · China
50
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7263386b12dab5b0, topic:deep-learning, topic:tensorflow