keras-aichallenger-2018-plant-recognition

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keras-aichallenger-2018-plant-recognition

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农作物病害检测 2018 ai challenger

// repository documentation

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# keras-aichallenger-2018-plant-recognition

# thoughts

I have tried out various kinds of models, and use data argumentation operations, but when I reach to 0.87993 precision, I can't improve it anymore, but according to other competitors' experience, pytorch deep learning framework can get a higher percision, since I have tried out many keras models, I decide
to make my code open source, any questions, contact me, please

# environments

```
tensorflow-gpu                     1.9.0 
jupyter                            1.0.0 
Keras                              2.1.0
h5py                               2.7.1
ubuntu 16.04
gtx 1080ti
```

# models and validation accuracy
- inception_resnet_v2   0.8764
- xception              0.8740
- mobilenet_v2          0.8674
- inception_v3          0.8747
- vgg16                 failed
- vgg19                 failed
- resnet50              0.8762
- inception_v4          0.8700
- resnet34              0.8773 
- densenet121           didn't try
- densenet161           0.8800
- shufflenet_v2         0.8676
- resnet_attention_56   0.8762
- squeezenet            0.8026
- seresnet50            0.8826
- se_resnext            didn't try
- nasnet                failed
- custom                didn't try
- resnet18              0.8754

# tutorial

## datasets

1. at first, you need to download aichallenger plant datasets from the official site:

https://challenger.ai/competition/pdr2018

since the official download is not available, I make the datasets public, download websites (baidu cloud):

url: https://pan.baidu.com/s/15csF8MyJaROQo5Hgt8sLYg

extraction code: 16yc

2. for dataset generation, please refer to data_split.ipynb

it's easy to modify for your purpose

## training

I provide various kinds of models for training, if you want to use one of these models, please refer to the train.py, I provide the example code here
```
python train.py --model_name=resnet34
```
## inference
before you use the following command, you should manually add one line code in the reference.py,  to load your trained model, for example:

model=load_model('./trained_model/resnet34/resnet34.54-0.8773.hdf5')

```
python inference.py
```
## results combinations
please refer to the 
- create_csv_results.py
- csv_combinations.py

## grade 
- densenet161+inception_resnet_v2+seresnet50+resnet50+resnet34: 0.8817

- 我这里就当做baseline开源了

## contact
any questions, please email me, my email address is: w5688414@outlook.com

keras-aichallenger-2018-plant-recognition

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