Slicing aided fine-tuning

[yoloyash]

I'm dealing with satellite imagery from the dota dataset and I want to train a yolov8 on it. The objects are very small in size which for which sahi is great. From what I've gathered, slicing aided fine-tuning provides higher increase in map. A lot of people have asked about how to train, however, i couldn't find a clear cut answer or code. i've also gone through the tutorial mentioned by the authors in the list of tutorials (https://blog.ml6.eu/how-to-detect-small-objects-in-very-large-images-70234bab0f98). However, I want to know the official way of fine-tuning to achieve the highest possible mAP? For example, this is how it's done in the blog.
train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict(type='RandomCrop', crop_size=(height, width)), dict( type='Resize', img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768), (1333, 800)], multiscale_mode='value', keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict( type='Normalize', mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) ]

Should I also use a random crop while training? or there is a better approach?

[fcakyon]

Hello @yoloyash. Here you can find the full training scripts and configs for slicing aided fine-tuning: https://github.com/fcakyon/small-object-detection-benchmark

A sample config is also available for xview dataset (satellite imagery).

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For yolov8, you have to slice the coco formatted dataset using this: https://github.com/obss/sahi/blob/main/docs/slicing.md

Then you train your yolov8 model with slices.

Then you perform sliced inference with sahi+yolov8 as in https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_yolov5.ipynb

[yoloyash]

Got it! Appreciate your hard work and quick responses!

[fcakyon]

Good luck!

[vanguard478]

@fcakyon Do we just create a sliced dataset and train the YOLO V8 using the usual train script from ultralytics or do we have to create a training wrapper in SAHI repo?
Is the wrapper you mentioned in https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_yolov5.ipynb only for inference?