This template is for Image Classification of AI Competition. Diffrence between original pytorch-template and efficient baseline is as follows.
- Can use hard splitted valid_data_loader
- Can add logs in wandb
- Add custom_dataset & custom_valid_dataset
- Can use albumentations not transforms in torchvision
- Can see Confusion Matrix figure in tensorboard
- Can change pretrained model (in timm) simply change to argument of config.
Python >= 3.5 (3.6 recommended)
PyTorch >= 0.4 (1.2 recommended)
tqdm (Optional for test.py)
tensorboard >= 1.14 (see Tensorboard Visualization)
wandb >= 0.12.1 (see Wandb Visualization)
sklearn >= 0.24.2
matplotlib >= 3.2.1
seaborn >= 0.11.2
timm >= 0.4.12
-
You must change TODO
custom_dataset.py # dataset path data_loader.py # augmentations model.py # if you use other model that is not in timm train.py # init your wandb account
-
And this code uses '.csv' file. Csv file should have image's path and label.
{
"name": "YOUR_MODEL_NAME",
"n_gpu": 1,
"arch": {
"type": "Model", # Model class name (in model.py)
"args": {
"num_classes": 10, # Number of classes
"pretrained_model": "efficientnet_b0" # This is pretrained model name of timm
}
},
"data_loader": {
"type": "CustomDataLoader", # your data loader
"args": {
"data_dir": "YOUR_DATA_DIR", # your data dirs
"batch_size": 128,
"shuffle": true,
"validation_split": 0.0,
"num_workers": 4,
"csv_path": "YOUR_TRAIN_CSV_FILE_PATH" # your csv path
}
},
"valid_data_loader": {
"type": "CustomValidDataLoader", # your valid data loader
"args": {
"data_dir": "YOUR_VALID_DATA_DIR", # your valid data dirs
...
# your valid csv path if you don't have valid dataset, you can use Sampler in base_data_loader.py
"csv_path": "YOUR_VALID_CSV_FILE_PATH"
}
},
}
