NKB Classification

Easy framework for computer vision classification tasks.

Install ...

Download from git

git clone [email protected]:nkb-tech/nkb-classification.git nkb_classification
cd nkb_classification

... via virtualenv

Install the package

python3 -m pip install --upgrade pip
python3 -m pip install virtualenv
python3 -m venv env
source env/bin/activate
python3 -m pip install -e .

... via docker

docker build --tag nkb-cls-im --file Dockerfile .
docker run \
    -itd \
    --ipc host \
    --gpus all \
    --name nkb-cls-cont \
    --volume <host>:<docker> \ # put here path to models/configs to bind with docker image
    nkb-cls-im
docker exec -it nkb-cls-cont /bin/bash

Run train

cd nkb_classification
python3 -m train -cfg `cfg_path`

Train config guideline

All configuration needed for training are passed by train config.py file. See sample files in configs/ directory.

Important fields:

• task : Determines task. Should be "single" for single task classfication (binary or multiclass), or 'multi' for multi task classification.
• target_column only for single task : Column with class labels.
• target_names only for multi task : Column names with class labels.
• experiment : Settings regarding saving model weights, training results and Comet ML logger. Comet logger can be set to None to get disabled.
• train_data / val_data: Train / val dataset settings. Look at comments in sample configs for details.
• train_pipeline : Image preprocessing/augmentations during training phase.
• val_pipeline : Image preprocessing/augmentations during validations phase.
• model : Model settings. To choose a model, look section Available backbone models.

Run inference

cd nkb_classification
python3 -m inference -cfg `inference_cfg_path`

Dataset format

The dataset is provided as a csv table for the traditional classification task and as a yaml-config for classifiying bounding boxes crops from a detection dataset.

CSV data config

In case of the traditional classification task the dataset csv file is a table with paths to the images and annotations for each image which include train/val/test partition. Specifically, it should have the following structure

	column_0	column_1	column_2	...	column_n	path	fold
0	value_0_0	value_1_0	value_2_0	...	value_n_0	/home/user/data/img_0.jpg	train
1	value_0_1	value_1_1	value_2_1	...	value_n_1	/home/user/data/img_1.jpg	val
2	value_0_2	value_1_2	value_2_2	...	value_n_2	/home/user/data/img_2.jpg	test
3	value_0_3	value_1_3	value_2_3	...	value_n_3	/home/user/data/img_3.jpg	-1

Objects with the -1 fold value are ignored. Target columns are scpicfied in the config file by the target_names list. The path to the csv table is also provided through the config file.

Arange images by folders

To use this format, choose ImageFolder dataset type in train_data/val_data configs. In this case the structure of the dataset should be the following:

dataset_root/
├── class_0/
│   ├── image1.jpg
│   ├── image2.jpg
│   └── image3.jpg
├── class_1/
│   ├── image1.jpg
│   ├── image2.jpg
│   └── image3.jpg
└── class_2/
    ├── image1.jpg
    ├── image2.jpg
    └── image3.jpg

YAML data config

In this case the format is the same as used in computer vision detection tasks as described in ultralytics docs: https://docs.ultralytics.com/datasets

Available backbone models

1. All models from timm library. To get model names run timm.list_models() with optional argument pretrained=True. Provide exactly same model name to config.py
2. All models from unicom library. List them with unicom.available_models(). To use them, provide "unicom model_name" (i.e. "unicom ViT-B/32") in config.py.

Run onnx export

To enable export models to onnx or torchscript, run first:

python3 -m pip install -r requirements/optional.txt

After that, run:

cd nkb_classification
python3 -m export \
    --to onnx \  # supported [torchscript, onnx, engine]
    --config `config_model_path` \
    --opset 17 \
    --dynamic batch \  # supported [none, batch, all]
    --sim \  # simplify the graph or not
    --input-shape 1 3 224 224 \
    --device cuda:0 \
    --half \  # convert to fp16
    --save_path . \  # where to save the model
    --weights `weights_model_path`  # path to model weights

Develop

To enable autocheck code before commit, run:

# install tensorrt if needed to export
python3 -m pip install --no-cache nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com
python3 -m pip install -r requirements/optional.txt
pre-commit install