DFX184/Kaggle-Classifer

Plant Pathology 2021 - FGVC8

Identify the category of foliar diseases in apple trees

Completion link

Download Dataset

BaiDu Network Disk

Get Data

passward kwj4

if you have downloaded the dataset,the document structure should be as follows:

../
    ./train_images
    ./test_images
    train.csv
    sample_submission.csv

where ../ is ROOT in config.py

Sample Data and EDA

Data

There are some different classes of image as below:

EDA

Step 1 (Key Step)

Another Step

SoftWare Version and Installation

Below are the versions of opencv-python, torch, torchvision, rich, warmup_scheduler, and numpy currently running at the time of writing this:

• opencv-python : 4.5.5.64
• torch : 1.10.0+cu102
• torchvision : 0.11.0+cu102
• rich : 10.16.2
• warmup_scheduler : 0.3.2
• numpy : 1.20.0

Install rich accelerate DataLoaderX albumentations and warmup_scheduler

pip install git+https://github.com/ildoonet/pytorch-gradual-warmup-lr.git
pip install rich
pip install accelerate
pip install prefetch_generator 
pip install -U albumentations
sudo apt-get install libturbojpeg
pip install -U jpeg4py

or you can use install.sh to configure

Custom Model

Add new model file to ./models/

example

./models/new_model.py

import torch
import torch.nn as nn
class Model(nn.Module):
    def __init__(self,in_channle,num_classes):
        super().__init__()
        pass
    def forward(self):
        pass

Custom transform

Add code in vision_transform

Configure parameters

In config.py,you can see a dict as follows:

parameter = {
    "ROOT": r"/mnt/d/dataset",
    "dataset_csv": r"train.csv",
    "batch_size": 32,
    "num_workers" : 8,
    "learning_rate" : 1e-3,
    "val_size" : 0.2,
    "seed" : 12,
    "epochs":10,
    "num_classes" : 12,
    "in_channel" : 3,
    "image_size" : (128,128)
}