train.py

# %pip install comet_ml --quiet
import comet_ml
import torch
from ultralytics import YOLO
import ultralytics.data as data
import ultralytics.data.dataset as dataset
import ultralytics.data.build as build

import numpy as np
comet_ml.login(project_name='reduced_images_benthic_supercategory_detector6')
#comet_ml.start(mode="get", experiment_keyd="87329baa84f547feb8f249cd3991b51d")

import os
os.environ["CUDA_VISIBLE_DEVICES"] = "1"

print("CUDA Available:", torch.cuda.is_available())
if torch.cuda.is_available():
        print("GPU Name:", torch.cuda.get_device_name(0))


model = YOLO("yolo11x.yaml")
model = YOLO("yolo11x.pt")  # Load a pretrained model
model = YOLO("yolo11x.yaml").load("yolo11x.pt")

results = model.train(data='/data/james/reduced_experiment/data6/benthic_supercategory_detector.yaml', batch = 32, epochs=100, imgsz=640, patience=15, val=True, device=0, plots=True)