Trained YOLOv8 model converted to CoreML doesn't give any predictions about ultralytics HOT 4 OPEN

👋 Hello @tardoe, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.

If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.

If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.

Join the vibrant Ultralytics Discord 🎧 community for real-time conversations and collaborations. This platform offers a perfect space to inquire, showcase your work, and connect with fellow Ultralytics users.

Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

Environments

YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

from ultralytics.

Hi @tardoe,

Thank you for reaching out and providing detailed information about your issue. Let's work through this together to identify the problem.

Firstly, it's great to hear that the vanilla YOLOv8m model works fine after conversion to CoreML. This indicates that the conversion process itself is functioning correctly. The issue seems to be specific to your custom-trained model.

Here are a few steps to help diagnose and potentially resolve the issue:

1. Verify Model Performance Before Export:
   Ensure that your custom-trained model performs as expected in its original format before conversion. You can do this by running predictions on your test images using the predict method in the YOLO format:

   results = model.predict(source='path/to/test/images', imgsz=640)
   results.show()

2. Check CoreML Export Parameters:
   Your export command looks correct, but let's ensure that the parameters are optimal. You might want to try exporting without the nms parameter to see if it affects the predictions:

   model.export(format="coreml", half=False, imgsz=640)

3. Inspect CoreML Model in Xcode:
   After exporting, use Xcode to inspect the CoreML model. Ensure that the input and output shapes match your expectations. Sometimes, discrepancies in input/output shapes can lead to no predictions.

4. Test CoreML Model with CoreMLTools:
   Use coremltools to test the CoreML model directly in Python before deploying it to Xcode. This can help isolate whether the issue is with the model or the deployment environment:

   import coremltools as ct
   from PIL import Image
   import numpy as np
   
   # Load the CoreML model
   coreml_model = ct.models.MLModel('yolov8n.mlpackage')
   
   # Load and preprocess an image
   image = Image.open('path/to/test/image.jpg').resize((640, 640))
   image_np = np.array(image).astype(np.float32)
   
   # Make a prediction
   prediction = coreml_model.predict({'image': image_np})
   print(prediction)

5. Ensure Compatibility:
   Verify that the versions of torch, ultralytics, and coremltools are compatible. Sometimes, version mismatches can cause unexpected behavior. Ensure you are using the latest versions:

   pip install --upgrade torch ultralytics coremltools

If the issue persists, please provide a minimum reproducible example of your code and dataset configuration. This will help us reproduce the bug and investigate further. You can find guidelines for creating a minimum reproducible example here.

Feel free to reach out with any additional questions or updates on your progress. We're here to help!

from ultralytics.