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‎README.md

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@@ -34,7 +34,7 @@ InnoFW uses hydra to provide configuration structure that is suitable for the mo
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1. Create an experiment config file in the folder ```config/experiments/``` based on ```config/experiments/template.yaml```.
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2. Once you define your configuration file you can start training your model.
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```python train.py experiment=yolov5_cars```
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```python train.py experiments=yolov5_cars```
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3. InnoFW checks the configuration file for consistency of individual modules(model, dataset, loss, optimizer etc.) and if everything is fine then selects and adapter. Adapter is responsible for starting the training, testing, validation and inference pipeline.
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4. Model is being trained and checkpoints saved.
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‎config/augmentations/preprocessing/sentinel2.yaml

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divide_by_10000:
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_target_: innofw.core.augmentations.preprocessing.ToFloatWClip
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max_value: 10000
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p: 1.0
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always_apply: True

‎config/augmentations_test/arable_sentinel2.yaml

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defaults:
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- /augmentations/preprocessing:
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- sentinel2
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- /augmentations/combined:
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- none
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- /augmentations/position:
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- /augmentations/color:
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- /augmentations/postprocessing:

‎config/augmentations_train/arable-ndvi-min.yaml

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defaults:
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- /augmentations/preprocessing:
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- /augmentations/combined:
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- none
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- /augmentations/position:
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- random_resized_crop
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- /augmentations/color:
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- /augmentations/postprocessing:
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augmentations:
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position:
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random_crop:
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height: 224
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width: 224

‎config/augmentations_train/arable-ndvi-strong.yaml

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defaults:
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- /augmentations/preprocessing:
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- /augmentations/combined:
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- none
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- /augmentations/position:
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- random_resized_crop
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- flip
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- rotate90
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- transpose
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- grid_distortion
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- /augmentations/color:
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- random_gamma
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- blur
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- /augmentations/postprocessing:
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augmentations:
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position:
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flip:
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p: 0.75
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rotate90:
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p: 0.75
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transpose:
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p: 0.75
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grid_distortion:
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p: 0.75
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color:
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random_gamma:
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p: 0.75
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blur:
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p: 0.75

‎config/augmentations_train/arable-ndvi.yaml

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defaults:
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- /augmentations/preprocessing:
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- /augmentations/combined:
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- none
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- /augmentations/position:
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- random_resized_crop
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- flip
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- rotate90
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- transpose
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- grid_distortion
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- /augmentations/color:
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- random_gamma
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- blur
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- /augmentations/postprocessing:

‎config/augmentations_train/arable_sentinel2.yaml

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defaults:
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- /augmentations/preprocessing:
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- sentinel2
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- /augmentations/combined:
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- none
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- /augmentations/position:
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- /augmentations/color:
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- /augmentations/postprocessing:

‎config/augmentations_val/arable-ndvi-min.yaml

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defaults:
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- /augmentations/preprocessing:
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- /augmentations/combined:
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- none
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- /augmentations/position:
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- random_resized_crop
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- /augmentations/color:
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- /augmentations/postprocessing:
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augmentations:
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position:
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random_crop:
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height: 224
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width: 224

‎config/augmentations_val/arable-ndvi-strong.yaml

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defaults:
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- /augmentations/preprocessing:
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- /augmentations/combined:
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- none
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- /augmentations/position:
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- random_resized_crop
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- flip
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- rotate90
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- transpose
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- grid_distortion
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- /augmentations/color:
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- random_gamma
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- blur
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- /augmentations/postprocessing:
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augmentations:
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position:
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flip:
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p: 0.75
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rotate90:
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p: 0.75
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transpose:
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p: 0.75
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grid_distortion:
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p: 0.75
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color:
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random_gamma:
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p: 0.75
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blur:
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p: 0.75

‎config/augmentations_val/arable-ndvi.yaml

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defaults:
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- /augmentations/preprocessing:
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- /augmentations/combined:
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- none
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- /augmentations/position:
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- random_resized_crop
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- flip
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- rotate90
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- transpose
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- grid_distortion
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- /augmentations/color:
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- random_gamma
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- blur
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- /augmentations/postprocessing:

‎config/augmentations_val/arable_sentinel2.yaml

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defaults:
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- /augmentations/preprocessing:
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- sentinel2
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- /augmentations/combined:
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- none
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- /augmentations/position:
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- /augmentations/color:
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- /augmentations/postprocessing:

‎config/datasets/detection/air_data.yaml

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task:
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- image-detection
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name: lep
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description: Набор данных содержит видео облетов c дронов над территорией линии электропередач
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markup_info: Набор данных содержит разметку bounding box, под формат детекции столбов ЛЭП.
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date_time: 18.07.2022
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_target_: innofw.core.integrations.ultralytics.datamodule.UltralyticsDataModuleAdapter
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train:
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test:
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infer:
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source: https://api.blackhole.ai.innopolis.university/public-datasets/air_data/train.zip
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target: ./data/air_data/
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num_workers: 8
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val_size: 0.2
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channels_num: 3
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image_size: 600
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num_classes: 4
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names:
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- lep_1
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- lep_2
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- lep_3
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- lep_4

‎config/datasets/detection/auto.yaml

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task:
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- image-detection
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name: lep
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description: Набор данных содержит видео облетов c дронов над территорией линии электропередач
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markup_info: Набор данных содержит разметку bounding box, под формат детекции столбов ЛЭП.
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date_time: 18.07.2022
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_target_: innofw.core.integrations.ultralytics.datamodule.UltralyticsDataModuleAdapter
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train:
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test:
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infer:
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source: https://api.blackhole.ai.innopolis.university/public-datasets/auto/train.zip
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target: ./data/auto/
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num_workers: 8
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val_size: 0.2
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channels_num: 3
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image_size: 600
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num_classes: 4
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names:
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- lep_1
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- lep_2
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- lep_3
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- lep_4

‎config/datasets/detection/detection_rotten_fruits.yaml

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markup_info: Набор данных содержит разметку bounding box, под формат детекции гнилых фруктов.
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date_time: 19.07.2022
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#_target_: innofw.core.datamodules.lightning_datamodules.YOLOv5DataModule
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_target_: innofw.core.integrations.ultralytics.datamodule.YOLOV5DataModuleAdapter
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_target_: innofw.core.integrations.ultralytics.datamodule.UltralyticsDataModuleAdapter
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train:
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source: https://api.blackhole.ai.innopolis.university/public-datasets/detection_rotten_fruits/train.zip

‎config/datasets/detection/wares_sort.yaml

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task:
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- image-detection
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name: sort_data
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description: Набор данных содержит видео облетов c дронов над территорией линии электропередач
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markup_info: Набор данных содержит разметку bounding box, под формат детекции столбов ЛЭП.
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date_time: 18.07.2022
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_target_: innofw.core.integrations.ultralytics.datamodule.UltralyticsDataModuleAdapter
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train:
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test:
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infer:
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source: https://api.blackhole.ai.innopolis.university/public-datasets/wares_sort/train.zip
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target: ./data/wares_sort/
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num_workers: 8
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val_size: 0.2
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channels_num: 3
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image_size: 600
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num_classes: 4
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names:
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- lep_1
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- lep_2
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- lep_3
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- lep_4

‎config/datasets/detection/detection_lep.yaml ‎config/datasets/detection_lep.yaml

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task:
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- image-detection
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name: detection_lep
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name: lep
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description: Набор данных содержит видео облетов c дронов над территорией линии электропередач
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markup_info: Набор данных содержит разметку bounding box, под формат детекции столбов ЛЭП.
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date_time: 18.07.2022
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#_target_: innofw.core.datamodules.lightning_datamodules.detection.YOLOv5DataModule
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_target_: innofw.core.integrations.ultralytics.datamodule.YOLOV5DataModuleAdapter
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_target_: innofw.core.integrations.ultralytics.datamodule.UltralyticsDataModuleAdapter
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train:
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source: https://api.blackhole.ai.innopolis.university/public-datasets/testing/lep/train.zip
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target: ./data/lep/train
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target: ./data/lep/
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test:
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source: https://api.blackhole.ai.innopolis.university/public-datasets/testing/lep/test.zip
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target: ./data/lep/test
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target: ./data/lep/
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infer:
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source: https://api.blackhole.ai.innopolis.university/public-datasets/testing/lep/test.zip
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target: ./data/lep/infer
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target: ./data/lep/
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num_workers: 8
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- lep_2
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- lep_3
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- lep_4
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# Dataset structure is as follows:
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# images/
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# train/
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# *.PNG
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# test/
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# *.PNG
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# labels/
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# train/
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# *.txt
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# test/
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# *.txt
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# Note that each archive consist only corresponding folders:
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# train.zip/
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# images/
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# train/
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# *.PNG
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# labels/
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# train/
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# *.txt
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# Similar for test.zip and infer.zip

‎config/datasets/regression/regression_credit_cards.yaml ‎config/datasets/regression/credit_cards.yaml

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‎config/datasets/semantic-segmentation/arable-bin-seg-ndvi/fields/tile-512-90-days.yaml

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_target_: innofw.core.datamodules.lightning_datamodules.semantic_segmentation.tiff.SegmentationDM
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train:
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source: https://api.blackhole.ai.innopolis.university/public-datasets/testing/arable_ndvi/train.zip
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target: data/arable_ndvi/train/
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test:
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source: https://api.blackhole.ai.innopolis.university/public-datasets/testing/arable_ndvi/test.zip
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target: data/arable_ndvi/test/
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img_foldername: img
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label_foldername: gt
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num_workers: 32
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batch_size: 16
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channels: 3
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name: Arable_NDVI
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task: image-segmentation
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description: Binary semantic segmentation dataset with arable images NDVI
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markup_info: some
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date_time: 010923

‎config/datasets/semantic-segmentation/landslides-kanopus/270223-hdf5.yaml

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_target_: innofw.core.datamodules.lightning_datamodules.segmentation_hdf5_dm.HDF5LightningDataModule # todo:
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_target_: innofw.core.datamodules.lightning_datamodules.HDF5LightningDataModule # todo:
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train:
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source: /mnt/nvmestorage/qb/data_n_weights/landslides-bin-seg-s2/210223/processed/train
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‎config/datasets/semantic-segmentation/linear-road-bin-seg-oftp/tile-2048-original.yaml

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_target_: innofw.core.datamodules.lightning_datamodules.semantic_segmentation.tiff_dm.SegmentationDM
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_target_: innofw.core.datamodules.lightning_datamodules.semantic_segmentation.tiff.SegmentationDM
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# ${.root}
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train:
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source: /mnt/nvmestorage/qb/data_n_weights/linear-road-bin-seg-oftp/301122/processed/070123-167folders-2048/

‎config/datasets/semantic-segmentation/water-erosion-kanopus/220223-hdf5.yaml

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_target_: innofw.core.datamodules.lightning_datamodules.segmentation_hdf5_dm.HDF5LightningDataModule # todo:
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_target_: innofw.core.datamodules.lightning_datamodules.HDF5LightningDataModule
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train:
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source: /mnt/nvmestorage/qb/data_n_weights/water_erosion/220223/processed/train/
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‎config/experiments/anomaly-detection/IM_040822_rbe23lls_anomaly_detection_timeseries_lstm.yaml

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- override /models: anomaly-detection/lstm_autoencoder.yaml
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- override /datasets: anomaly_detection_timeseries_ecg.yaml
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- override /losses: l1.yaml
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- override /schedulers: cosine_annealing
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project: "ecg"
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task: "anomaly-detection-timeseries"

‎config/experiments/classification/SK_010822_jvslrmvk_resnet.yaml

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- override /augmentations_test: none
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- override /losses: log_loss.yaml
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- override /optimizers: adam
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- override /schedulers:
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project: "mnist_classification"
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task: "image-classification"

‎config/experiments/clustering/demo_queue.yaml

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# @package _global_
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defaults:
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- override /models: clustering/kmeans
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- override /datasets: clustering/clustering_credit_cards
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- override /callbacks: clustering
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- override /clear_ml: test_queue
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project: credit_cards
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random_seed: '42'
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task: table-clustering

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