# 1. Annotated the frames using `ultralytics auto annotator`
> Run `python annotate.py`
(See output `annotate_output.txt`)
This creates a folder under `data` named 'frames_auto_annotate_labels' and gives a .txt file for each frame containing the segmentations of the hens it detected.
2. Put the labels into a proper structure for training i.e. specify custom class (hen)
# 2. Put the labels into a proper structure for training i.e. specify custom class (hen)
-> Run `python get_stuff.py`
> Run `python get_stuff.py`
This will put all the necesaary files under `train/labels`. Move the last 20 files to `val/labels` for validation in the next stripped
This will put all the necesaary files under `train/labels`. Move the last 20 files to `val/labels` for validation in the next stripped.
3. The model is now trained using a pretrained 'yolov8x-seg'. Ensure that the directory path is good
# 3. The model is now trained using a pretrained `yolov8x-seg`
Ensure that the directory path is good
-> Run `yolo task=segment mode=train model=yolov8x-seg data=data.yaml epochs=20 imgsz=640` in a terminal opened in the directory
(See output train_output.txt)
> Run `yolo task=segment mode=train model=yolov8x-seg data=data.yaml epochs=20 imgsz=640` in a terminal opened in the directory
(See output `train_output.txt`)
This will create a `runs` folder and all the metrics and model weights will be stored there.
4. Use the best weights as input model for tracking
# 4. Use the best weights as input model for tracking
> Run `python track.py`
-> Run `python track.py`(See output output_track.py, uncomment line no. 34 if you want to visualize the output alongside running)
(See `output track_output.txt`, uncomment line no. 34 if you want to visualize the output alongside running)
The final tracking video would be saved under the directory
\ No newline at end of file
The final tracking video would be saved under the directory.
