SAHI inference with YOLOv8 💫 boost your small object detection performance

What is SAHI (Slicing Aided Hyper Inference) - https://docs.ultralytics.com/guides/sahi-tiled-inference/

What in this repo:

This repo contain not only SAHI inference implementation but also evaluation of results with mAp50, ... (standart metrics)

You will understand if SAHI inference help in your specific case

SAHI	No SAHI

more cars far away detected	standart detections

Run output:

SAHI inference + EVALUATION of results with basic yolo8 metrics

output example with basic validation on 2 images:

      val: Scanning C:\Users\irady\GitHub\YOLO8_SAHI\yolo_dataset\labels.cache.
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 
                   all          2         11      0.987      0.545       0.57      0.455
                person          2         11      0.987      0.545       0.57      0.455
Speed: 1.5ms preprocess, 329.6ms inference, 0.0ms loss, 4.5ms postprocess per image

    ```

output example with SAHI validation on 2 images:

val: Scanning C:\Users\irady\GitHub\YOLO8_SAHI\yolo_dataset\labels.cache.
         Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 
Performing prediction on 9 number of slices.
Performing prediction on 9 number of slices.
         Class     Images  Instances      Box(P          R      mAP50  mAP50-95):
           all          2         11          1      0.545      0.773      0.628
        person          2         11          1      0.545      0.773      0.628
Speed: 7.5ms preprocess, 0.0ms inference, 0.0ms loss, 0.0ms postprocess per image

and also check sahi/ folder - there all validation plots will be saved

How to use:

git clone
in utils.get_category_mapping() change returned dictionary for your classes
in main() change paths for your .pt and .yaml, and set desired input imgsz, source for inference etc
VALIDATION : in main() run run_sahi_validation() or run_basic_validation()

INFERENCE : in main() run run_sahi_prediction() or run_basic_prediction()

also you can update size of sliding window in head of utils.sahi_predict() :

    VERBOSE_SAHI = 2
    SLICE_H = 640
    SLICE_W = 640
    OVERLAP_HEIGHT_RATIO = 0.2
    OVERLAP_WIDTH_RATIO = 0.2```

What you need:

your .pt model file
validation dataset in yolo standart format

.yaml file for dataset

#file sahi_data.yaml
path: ../YOLO8_SAHI/yolo_dataset/ # dataset root dir

train: images # train images (relative to 'path') 128 images
val: images # val images (relative to 'path') 128 images

# Classes
names:
  0: people

fivetop / YOLO8_SAHI