| --- |
| license: mit |
| base_model: |
| - Ultralytics/YOLOv8 |
| pipeline_tag: object-detection |
| --- |
| |
| ONNX version of https://github.com/Abcfsa/YOLOv8_head_detector |
|
|
|
|
|
|
|
|
|
|
| YOLOv8_head_detector |
| model trained for head detection,graduation project 2023-24 |
|
|
| Info |
| These model are trained with yolov8 on the dataset SCUT-HEAD part A and part B. medium.pt is bigger and more accurate while nano.pt is smaller and faster. |
|
|
| Usage |
| If you already have ultralytics/yolov8 installed,then just choose one of these two models to do your prediction jobs. Else first enter these in the command line |
|
|
| pip install ultralytics |
| pip install opencv-python |
| pip install pandas |
| Basic usage: |
|
|
| python example.py --model path/to/model_weights.pt --source path/to/img_folder --output path/to/save.csv --mode track/detect |
| Output format |
| Depends on mode.If mode is detect,then the columns would be ["name","xmin","ymin","xmax","ymax"]. If mode is track,then there will be an additional "id" column between "name" and "xmin". |
| Output has no index column |
| "xmin",...,"ymax" are all normalized to 0-1. |
