om-simple 0.0.0.1

OM Simple

Image Classification

pip install om_simple

Data Preparation

{dataset name}/
├── train/
│   ├── {class1}/
│   ├── {class2}/
│   ├── ...
└── val/
    ├── {class1}/
    ├── {class2}/
    ├── ...

Example Code

from om_simple.img_class import ImageClassification
from om_simple.tools.utils import is_blur


# Simple Classification
X = ImageClassification("epoch019.ckpt")
z = X.predict(images=["sample.jpg"]*1000)


# Blur detection
print (is_blur("sample.jpg"))

Training Simple Image Classification

Simple implementation with everything in a single file (train.py)

Specify the dataset root directory containing the train and val directories.

python train.py -d {dataset name}

You can use most of the models in the timm by specifying --model-name directly.

usage: train.py [-h] --dataset DATASET [--outdir OUTDIR]
                [--model-name MODEL_NAME] [--img-size IMG_SIZE]
                [--epochs EPOCHS] [--save-interval SAVE_INTERVAL]
                [--batch-size BATCH_SIZE] [--num-workers NUM_WORKERS]
                [--gpu-ids GPU_IDS [GPU_IDS ...] | --n-gpu N_GPU]
                [--seed SEED]

Train classifier.

optional arguments:
  -h, --help            show this help message and exit
  --dataset DATASET, -d DATASET
                        Root directory of dataset
  --outdir OUTDIR, -o OUTDIR
                        Output directory
  --model-name MODEL_NAME, -m MODEL_NAME
                        Model name (timm)
  --img-size IMG_SIZE, -i IMG_SIZE
                        Input size of image
  --epochs EPOCHS, -e EPOCHS
                        Number of training epochs
  --save-interval SAVE_INTERVAL, -s SAVE_INTERVAL
                        Save interval (epoch)
  --batch-size BATCH_SIZE, -b BATCH_SIZE
                        Batch size
  --num-workers NUM_WORKERS, -w NUM_WORKERS
                        Number of workers
  --gpu-ids GPU_IDS [GPU_IDS ...]
                        GPU IDs to use
  --n-gpu N_GPU         Number of GPUs
  --seed SEED           Seed