Added yolo v8 training procedure

Readme.md

@@ -10,3 +10,6 @@
 
 ## GIT
 - [Setup and configuration](articles/git/setup_configuration/Readme.md)
+
+## COMPUTER VISION
+- [Retrain Yolo v8](articles/computer_vision/retrain_yolo_v8/Readme.md)

articles/computer_vision/retrain_yolo_v8/Readme.md

@@ -0,0 +1,131 @@
+<a href="../../../Readme.md">
+<img src="../../../common/back_arrow.png" alt="50" width="50"/>
+</a>
+
+# CV - Retrain Yolo v8 on custom data
+
+## Create environment
+
+```console
+conda create -n yolov8_custom python=3.9
+```
+
+```console
+conda activate yolov8_custom
+
+pip install simple_image_download==0.4
+
+pip install ultralytics
+```
+
+To use the GPU also copy the *pip install* command listed [here](https://pytorch.org/). 
+
+To check if CUDA is correctly set up:
+```python
+import torch
+
+torch.__version__
+
+torch.cuda.is_available()
+```
+
+## Download sample images from google
+
+```python
+from simple_image_download import simple_image_download as simp
+
+response = simpl.simple_image_download
+
+keywords = ["building workers"]
+
+for kw in keywords:
+    response().download(kw, 200)
+```
+
+## Annotate images
+
+To annotate the image [labelImg tool](https://pypi.org/project/labelImg/) can be used:
+
+```console
+pip install labelImg
+
+labelImg
+```
+
+Output folder structure:
+
+    .
+    ├── train
+    │     ├── images
+    │     │   └── image.png
+    │     └── labels
+    │         └── image.txt
+    └── val
+          ├── images
+          │   └── image.png
+          └── labels
+              └── image.txt
+
+The folder structure must be declared in a .yaml file:
+
+```yaml
+train: C:\<abs_path>\train
+val: C:\<abs_path>\val
+
+nc: 2
+
+names: ["hat", "jacket"]
+```
+
+The names must be the same declared in a file called *classes.txt*.
+
+### Annotation format (BBox)
+
+```
+<class_number> <norm_box_center_h> <norm_box_center_v> <norm_box_height> <norm_box_width>
+```
+
+
+## Train the net
+
+```console
+yolo task=detect mode=train epochs=100 data=data_custom.yaml model=yolov8m.pt imgsz=600 
+```
+
+The actual model and imgsz can be found [here](https://github.com/ultralytics/ultralytics).
+
+### Out of memory error
+In case of CUDA out of memory error a smaller batch size must be selected:
+```console
+yolo task=detect mode=train epochs=100 data=data_custom.yaml model=yolov8m.pt imgsz=600 batch=4
+```
+
+## Training generated files
+The retrained net weights are stored in */run/detect/train/weights/best.pt*.
+
+## Run the retrained network
+
+### From command line
+```console
+yolo task=detect mode=predict model=best.pt show=True conf=0.5 source=image.png
+```
+
+## From Python script
+```python
+from ultralytics import YOLO
+
+model = YOLO("best.pt")
+
+model.predict(source="image.png", show=True, save=True, conf=0.5)
+```
+
+
+## Export YOLO in ONNX format
+```console
+yolo task=detect mode=export model=best.pt format=onnx
+```
+
+## References
+- [Retrain yolo v8 for classification](https://www.youtube.com/watch?v=gRAyOPjQ9_s)
+- [Yolo v8 for segmentation](https://www.youtube.com/watch?v=75LI9MI9eEo)
+- [Segment objects with Yolov8](https://medium.com/@Mert.A/how-to-segment-with-yolov8-f33b1c63b6c6)