face-features-test Image Dataset

A simple dataset for benchmarking CreateML object detection models. The images are sampled from COCO dataset with eyes and nose bounding boxes added. It’s not meant to be serious or useful in a real application. The purpose is to look at how long it takes to train CreateML models with varying dataset and batch sizes.

Training performance is affected by model configuration, dataset size and batch configuration. Larger models and batches require more memory. I used CreateML object detection project to compare the performance.


M1 Macbook Air

  • 8 GPU
  • 4/4 CPU
  • 16G memory
  • 512G SSD

M1 Max Macbook Pro

  • 24 GPU
  • 2/8 CPU
  • 32G memory
  • 2T SSD

Small Dataset
Train: 144
Valid: 16
Test: 8


batch M1 ET M1Max ET peak mem G
16 16 11 1.5
32 29 17 2.8
64 56 30 5.4
128 170 57 12

Larger Dataset
Train: 301
Valid: 29
Test: 18


batch M1 ET M1Max ET peak mem G
16 21 10 1.5
32 42 17 3.5
64 85 30 8.4
128 281 54 16.5

CreateML Settings

For all tests, training was set to Full Network. I closed CreateML between each run to make sure memory issues didn't cause a slow down. There is a bug with Monterey as of 11/2021 that leads to memory leak. I kept an eye on the memory usage. If it looked like there was a memory leak, I restarted MacOS.


In general, more GPU and memory with MBP reduces the training time. Having more memory lets you train with larger datasets. On M1 Macbook Air, the practical limit is 12G before memory pressure impacts performance. On M1 Max MBP, the practical limit is 26G before memory pressure impacts performance. To work around memory pressure, use smaller batch sizes.

On the larger dataset with batch size 128, the M1Max is 5x faster than Macbook Air. Keep in mind a real dataset should have thousands of samples like Coco or Pascal. Ideally, you want a dataset with 100K images for experimentation and millions for the real training. The new M1 Max Macbooks is a cost effective alternative to building a Windows/Linux workstation with RTX 3090 24G. For most of 2021, the price of RTX 3090 with 24G is around $3,000.00. That means an equivalent windows workstation would cost the same as the M1Max Macbook pro I used to run the benchmarks.

Full Network vs Transfer Learning

As of CreateML 3, training with full network doesn't fully utilize the GPU. I don't know why it works that way. You have to select transfer learning to fully use the GPU. The results of transfer learning with the larger dataset. In general, the training time is faster and loss is better.

batch ET min Train Acc Val Acc Test Acc Top IU Train Top IU Valid Top IU Test Peak mem G loss
16 4 75 19 12 78 23 13 1.5 0.41
32 8 75 21 10 78 26 11 2.76 0.02
64 13 75 23 8 78 24 9 5.3 0.017
128 25 75 22 13 78 25 14 8.4 0.012

Github Project

The source code and full results are up on Github https://github.com/woolfel/createmlbench



Last Updated

7 months ago

Project Type

Object Detection




eye, nose