Comparing YOLO 8 and GCP AutoML for Object Detection

Introduction

When working on an object-detection model, one of the first questions is which model to use. With so many options available, the right choice ultimately depends on your resources, constraints, and the specific needs of your application. In this post, I compare two of the most widely adopted modern models: YOLO (You Only Look Once) and Google Cloud AutoML Vision Object Detection. These represent two fundamentally different approaches — one is an open-source, performance‑oriented model family, and the other is a fully managed, cloud‑based service designed for ease of use and scalability. While earlier comparisons between these approaches have focused on smaller datasets or older model versions [1], this post expands on previous work by revisiting the comparison with updated models, larger datasets, and more comprehensive evaluation results.

Model Overview

**YOLO (You Only Look Once)** is a single‑stage object detection framework that formulates detection as a direct regression problem — predicting bounding boxes and class probabilities from an entire image in a single forward pass [2]. As an open‑source ecosystem supported by a large community, it continues to evolve quickly, with extensive documentation and tooling available through the Ultralytics repository.

**GCP AutoML Vision Object Detection**, in contrast, is a fully managed Google Cloud service that allows users to build custom detection models simply by uploading labeled data. The platform automates feature extraction, model architecture selection, training, hyper-parameter tuning, and deployment [3]. Because it is a proprietary service, the underlying model architectures are not exposed to users, but the benefit is a streamlined, low‑maintenance workflow that abstracts away the complexity of designing and training deep learning models.

Input & Setup

For this comparison, I created a custom subset of the COCO dataset. The subset focuses on 5 categories — cat, horse, dog, cow, sheep — selected to balance variability in appearance, scale, and pose. In total, the dataset contains approximately 10,000 images, split into three standard partitions training (80%), validation (10%), test (10%).

The dataset is available here: https://www.kaggle.com/datasets/ffaraz/coco-data-subset.

To ensure a fair comparison, I used the default configuration for both YOLO and GCP AutoML Vision Object Detection. Each model was trained on the same training and validation sets, with no custom hyper-parameter tuning applied. YOLO v8 was used as it is the more standard version.

Results

The table below summarizes the overall precision and recall for both models. Across both metrics, YOLOv8 outperforms GCP AutoML Vision, showing stronger detection accuracy and more consistent performance.

A breakdown by class further confirms the pattern: YOLOv8 performs better across nearly all categories, particularly in recall.

Summary

In this comparison, YOLOv8 consistently outperforms GCP AutoML Vision Object Detection, aligning with many earlier evaluations that show YOLO models tend to excel on larger datasets [4]. Although this trend is not universal, previous studies suggest that YOLO generally delivers stronger accuracy when trained on substantial amounts of data, like this case, whereas AutoML‑style services often perform competitively on smaller datasets [1,5].

If your goal is to quickly build and deploy an object‑detection model, especially when working with limited dataset or minimal machine‑learning expertise, GCP AutoML offers a streamlined and user‑friendly solution. However, if you prioritize performance and have access to a larger dataset along with the technical capability to manage training and deployment, YOLO is likely the better choice. It’s also worth noting that, like other managed AutoML platforms, training and deployment costs can be significantly higher on GCP AutoML compared to running YOLO on your own infrastructure.

Codes & Material

Dataset: https://www.kaggle.com/datasets/ffaraz/coco-data-subset

YOLO Model: https://www.kaggle.com/code/ffaraz/yolo-model-over-5-categories

Reference

[1] Wheeler, M. (2021) GCP AutoML vs. YOLOv5 for Training a Custom Object Detection Model. Available at: https://medium.com/@mateo.wheeler/gcp-automl-vs-yolov5-for-training-a-custom-object-detection-model-c1481b8a5c58 (Accessed: 3 February 2026).

[2] Redmon, J., Divvala, S., Girshick, R. and Farhadi, A., 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779–788).

[3] Google Cloud (2024) AutoML Vision Object Detection documentation. Available at: https://cloud.google.com/vision/automl/object-detection/docs (Accessed: 3 February 2026).

[4] Chen, J., 2025, March. Object Detection Using AutoML and YOLO: A Comparative Analysis of EasyDL, YOLOv8, and YOLOv10. In CONF-MLA 2024: Proceedings of the 2nd International Conference on Machine Learning and Automation, CONF-MLA 2024, November 21, 2024, Adana, Turkey (p. 303). European Alliance for Innovation.

[5] Jegham, N., Koh, C.Y., Abdelatti, M. and Hendawi, A., 2024. Yolo evolution: A comprehensive benchmark and architectural review of yolov12, yolo11, and their previous versions. arXiv preprint arXiv:2411.00201.