YOLOv8 객체 탐지 모델 학습을 위한 이미지 수집 및 라벨링

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds

from tensorflow.keras import layers

(train_ds, val_ds, test_ds), metadata = tfds.load(
    'tf_flowers',
    split=['train[:80%]', 'train[80%:90%]', 'train[90%:]'],
    with_info=True,
    as_supervised=True,
)

data_augmentation = tf.keras.Sequential([
  layers.RandomFlip("horizontal_and_vertical"),
  layers.RandomRotation(0.2),
])

plt.figure(figsize=(10, 10))
for i in range(9):
  augmented_image = data_augmentation(image)
  ax = plt.subplot(3, 3, i + 1)
  plt.imshow(augmented_image[0])
  plt.axis("off")

train: custom_dataset/train/  
val: custom_dataset/val/ 

# number of classes
nc: 4

# class names
names: ['closed_door', 'opened_door', 'bus', 'number']

pip install wandb

wandb login

run = wandb.init(project="yolov8", job_type="add-dataset")
artifact = wandb.Artifact(name="my_data", type="dataset")
artifact.add_dir(local_path="/dataset/")  # Add dataset directory to artifact
run.log_artifact(artifact)

pip install ultralytics

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n.yaml")  # build a new model from scratch
model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)

# Use the model
model.train(data="dataset.yaml", epochs=3)  # train the model
metrics = model.val()  # evaluate model performance on the validation set
results = model(test_image)  # predict on an image
path = model.export(format="onnx")  # export the model to ONNX format

from wandb.integration.ultralytics import add_wandb_callback

# Add W&B callback for Ultralytics
add_wandb_callback(model, enable_model_checkpointing=True)

# Train/fine-tune your model
# At the end of each epoch, predictions on validation batches are logged
# to a W&B table with insightful and interactive overlays for
# computer vision tasks
model.train(project="yolov8", data='custom_dataset', epochs=5, imgsz=640)
model.val()

# Finish the W&B run
wandb.finish()