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train.py
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train.py
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import ultralytics
from ultralytics import YOLO
import shutil
import os
#######################################################################################################################
## 🎯 The aim of this script is to do transfert learning on YOLOv8 model. ##
## ℹ️ Note on the environments variables: ##
## - NB_OF_EPOCHS (default value: 50) is an environment variable passed to the Docker run command to specify ##
## the number of epochs ##
## - DEVICE_TO_USE (default value 0) is to specify to use GPU (0) or CPU (cpu) ##
## - PATH_TO_DATASET (default value is '/workspace/attendee/data.yaml') is to specify the path to the ##
## training dataset ##
## - PATH_TO_EXPORTED_MODEL (default value is '/workspace/attendee/') is to specify the path where export the ##
## trained model ##
## - BATCH specifies the number of images used for one training iteration before updating the model's weights. ##
## A larger batch size can lead to faster training but requires more memory.
## - FREEZE allows to freeze certain layers of a pre-trained model. This way, these layers are kept unchanged ##
## during training, which allows to preserve knowledge from the pre-trained model. ##
#######################################################################################################################
# ✅ Check configuration
# 🧠 Load a pretrained YOLO model
# 🛠 Get configuration from environment variables
# 💪 Train the model with new data ➡️ one GPU / NB_OF_EPOCHS iterations (epochs)
# 💾 Save the model
# ➡️ Copy the model to the object storage