# Trial in 30mins

Based on the flowers102 dataset, it takes only 30 mins to experience PaddleClas, include training varieties of backbone and pretrained model, SSLD distillation, and multiple data augmentation, Please refer to [Installation](install.md) to install at first.


## Preparation

* enter insatallation dir

```
cd path_to_PaddleClas
```

* enter `dataset/flowers102`, download and decompress flowers102 dataset.

```shell
cd dataset/flowers102
wget https://www.robots.ox.ac.uk/~vgg/data/flowers/102/102flowers.tgz
wget https://www.robots.ox.ac.uk/~vgg/data/flowers/102/imagelabels.mat
wget https://www.robots.ox.ac.uk/~vgg/data/flowers/102/setid.mat
tar -xf 102flowers.tgz
```

* create train/val/test label files

```shell
python generate_flowers102_list.py jpg train > train_list.txt
python generate_flowers102_list.py jpg valid > val_list.txt
python generate_flowers102_list.py jpg test > extra_list.txt
cat train_list.txt extra_list.txt > train_extra_list.txt
```

**Note:** In order to offer more data to SSLD training task, train_list.txt and extra_list.txt will merge into train_extra_list.txft

* return `PaddleClas` dir

```
cd ../../
```

## Environment

### Set PYTHONPATH

```bash
export PYTHONPATH=./:$PYTHONPATH
```

### Download pretrained model


```bash
python tools/download.py -a ResNet50_vd -p ./pretrained -d True
python tools/download.py -a ResNet50_vd_ssld -p ./pretrained -d True
python tools/download.py -a MobileNetV3_large_x1_0 -p ./pretrained -d True
```

Paramters：
+ `architecture`(shortname: a): model name.
+ `path`(shortname: p) download path.
+ `decompress`(shortname: d) whether to decompress.



* All experiments are running on the NVIDIA® Tesla® V100 sigle card.


## Training

### Train from scratch

* Train ResNet50_vd

```shell
export CUDA_VISIBLE_DEVICES=0
python -m paddle.distributed.launch \
    --gpus="0" \
    tools/train.py \
        -c ./configs/quick_start/ResNet50_vd.yaml

```

The validation `Top1 Acc` curve is showmn below.

![](../../images/quick_start/r50_vd_acc.png)


### Finetune - ResNet50_vd pretrained model (Acc 79.12\%)

* finetune ResNet50_vd_ model pretrained on the 1000-class Imagenet dataset

```shell
export CUDA_VISIBLE_DEVICES=0
python -m paddle.distributed.launch \
    --gpus="0" \
    tools/train.py \
        -c ./configs/quick_start/ResNet50_vd_finetune.yaml

```

The validation `Top1 Acc` curve is shown below

![](../../images/quick_start/r50_vd_pretrained_acc.png)

Compare with training from scratch, it improve by 65\% to 94.02\%


### SSLD finetune - ResNet50_vd_ssld pretrained model (Acc 82.39\%)

Note: when finetuning model, which has been trained by SSLD, please use smaller learning rate in the middle of net.

```yaml
ARCHITECTURE:
    name: 'ResNet50_vd'
    params:
        lr_mult_list: [0.1, 0.1, 0.2, 0.2, 0.3]
pretrained_model: "./pretrained/ResNet50_vd_ssld_pretrained"
```

Tringing script

```shell
export CUDA_VISIBLE_DEVICES=0
python -m paddle.distributed.launch \
    --gpus="0" \
    tools/train.py \
        -c ./configs/quick_start/ResNet50_vd_ssld_finetune.yaml
```

Compare with finetune on the 79.12% pretrained model, it improve by 0.9% to 95%.


### More architecture - MobileNetV3

Training script

```shell
export CUDA_VISIBLE_DEVICES=0
python -m paddle.distributed.launch \
    --gpus="0" \
    tools/train.py \
        -c ./configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml
```

Compare with ResNet50_vd pretrained model, it decrease by 5% to 90%. Different architecture generates different performance, actually it is a task-oriented decision to apply the best performance model, should consider the inference time, storage, heterogeneous device, etc.


### RandomErasing

Data augmentation works when training data is small.

Training script

```shell
export CUDA_VISIBLE_DEVICES=0
python -m paddle.distributed.launch \
    --gpus="0" \
    tools/train.py \
        -c ./configs/quick_start/ResNet50_vd_ssld_random_erasing_finetune.yaml
```

It improves by 1.27\% to 96.27\%

* Save ResNet50_vd pretrained model to experience next chapter.

```shell
cp -r output/ResNet50_vd/19/  ./pretrained/flowers102_R50_vd_final/
```

### Distillation

* Use extra_list.txt as unlabeled data, Note:
    * Samples in the `extra_list.txt` and `val_list.txt` don't have intersection
    * Because of in the source code, label information is unused, This is still unlabeled distillation
    * Teacher model use the pretrained_model trained on the flowers102 dataset, and student model use the MobileNetV3_large_x1_0 pretrained model(Acc 75.32\%) trained on the ImageNet1K dataset


```yaml
total_images: 7169
ARCHITECTURE:
    name: 'ResNet50_vd_distill_MobileNetV3_large_x1_0'
pretrained_model:
    - "./pretrained/flowers102_R50_vd_final/ppcls"
    - "./pretrained/MobileNetV3_large_x1_0_pretrained/”
TRAIN:
    file_list: "./dataset/flowers102/train_extra_list.txt"
```

Final training script

```shell
export CUDA_VISIBLE_DEVICES=0
python -m paddle.distributed.launch \
    --gpus="0" \
    tools/train.py \
        -c ./configs/quick_start/R50_vd_distill_MV3_large_x1_0.yaml
```

It significantly imporve by 6.47% to 96.47% with more unlabeled data and teacher model.

### All accuracy


|Configuration | Top1 Acc |
|- |:-: |
| ResNet50_vd.yaml | 0.2735 |
| MobileNetV3_large_x1_0_finetune.yaml | 0.9000 |
| ResNet50_vd_finetune.yaml | 0.9402 |
| ResNet50_vd_ssld_finetune.yaml | 0.9500 |
| ResNet50_vd_ssld_random_erasing_finetune.yaml | 0.9627 |
| R50_vd_distill_MV3_large_x1_0.yaml | 0.9647 |


The whole accuracy curves are shown below


![](../../images/quick_start/all_acc.png)



* **NOTE**: As flowers102 is a small dataset, validatation accuracy maybe float 1%.

* Please refer to [Getting_started](./getting_started) for more details