# tensorflow-cifar100

**Repository Path**: boxjod/tensorflow-cifar100

## Basic Information

- **Project Name**: tensorflow-cifar100
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-12-18
- **Last Updated**: 2023-12-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# tensorflow-cifar100

Tensorflow implementation on cifar100.

All models have achieved high accuracy (> 0.7).


### Usage

Requirements:

1. tensorflow-gpu=1.11.1
2. tensorlayer=1.11.0

download dataset:

[Download Website](https://www.cs.toronto.edu/~kriz/cifar.html )

download repo:

```
$ git clone https://gitee.com/boxjod/tensorflow-cifar100.git
```

train:

```
$ python3 -u train.py train --batch_size 64 --epoch 200 --network resnet50 --opt momentum --train_path /data/ChuyuanXiong/up/cifar-100-python/train --test_path /data/ChuyuanXiong/up/cifar-100-python/test
```

params:

* batch_size: 64 default
* epoch: 200 is best
* network: resnet18/resnet50/resnet110/resnet152/seresnet50/seresnet110/seresnet152/densenet121/densenet169/densenet161/densenet201/resnext50/resnext110/resnext152/seresnext50/seresnext110/seresnext152/densenet100bc/densenet190bc
* opt: adam/momentum/nesterov
* train_path:  your train path
* test_path: your test path

Have Done
```
ResNet18
ResNet34
ResNet50
ResNet110
ResNet152
ResNeXt50
ResNeXt110
ResNeXt152
SENet50
SENet110
SENet152
SE-ResNext50
SE-ResNext110
SE-ResNext152
DenseNet121
DenseNet169
DenseNet201
DenseNet100BC
DenseNet190BC

# TODO
preresnet
mobilenet
```


test:

```
python3 -u train.py test --network resnet18 --test_path '/data/ChuyuanXiong/up/cifar-100-python/test' --ckpt 'params/resnet18/Speaker_vox_iter_58000.ckpt'
```

params:

* network: resnet18/resnet50
* test_path: your test path
* ckpt:  your pre-trained model. You can try the [\$THIS_REPO/params/resnet18/Speaker_vox_iter_58000.ckpt]

Also, If you have the pre-trained model, you can try the interface to quickly access to the test results. Just try this:

```
$ cd [to the root directory of this repo]
$ python
>>> from pretrained.cifar100 import cifar100
>>> model = cifar100(model='resnet18')
>>> model.test()
```

Or you can cd to the dir and then edit and run example.py.


### Results

dataset | network | top1 acc | epoch (lr=0.1) | epoch (lr=0.02) |  batch_size | initializer |  warmup |   weight decay|
--------|---------|---------|-----------------|----------------|--------------|-------------|---------|--------------|
cifar100| resnet18   | 0.740  |   60          | > 60           |    128       | msra       |     0    |        0
cifar100| densenet169| 0.743 |  60            | > 60           |    64        | orth       |     1    |      5e-4     
cifar100| densenet100bc| 0.728 | 60           | > 60           |    32        | orth       |     1    |      5e-4
cifar100| densenet201| 0.753 |   60           | > 60           |    64        | orth       |     1    |      5e-4
cifar100| seresnet110| 0.725 |  60            | > 60           |    64        | orth       |     1    |      5e-4

// TODO

* preresnet18
* ...

### Pre-trained model download

Continuous update!

1. [ResNet18,Accuracy=0.740](https://github.com/Ecohnoch/tensorflow-cifar100/tree/master/params/resnet18)
2. [DenseNet169,Accuracy=0.743,Password=7qj2](https://pan.baidu.com/s/1Watp2FzcuLBym_x4FyrzBA)
3. [DenseNet100-BC,Accuracy=0.728,Password=fwi4](https://pan.baidu.com/s/1A6esP4TdC2NxmIfj30DG3w)
4. [Se-ResNet110,Accuracy=0.725,Password=we64](https://pan.baidu.com/s/1p8U3W1HOOrYV5hzZQFZY0g)




### References

1. [pytorch-cifar100](https://github.com/weiaicunzai/pytorch-cifar100)

### Author

Ecohnoch (Chuyuan Xiong)

If this project is very helpful for you, please star it!