Comments (5)
tucan9389
commented on May 29, 2024
| Model | Size (MB) |
Minimum iOS Version |
Download Link |
|---|---|---|---|
| YOLOv3 | 248.4 | iOS12 | Machine Learning - Models - Apple Developer |
| YOLOv3FP16 | 124.2 | iOS12 | Machine Learning - Models - Apple Developer |
| YOLOv3Int8LUT | 62.2 | iOS12 | Machine Learning - Models - Apple Developer |
| YOLOv3Tiny | 35.5 | iOS12 | Machine Learning - Models - Apple Developer |
| YOLOv3TinyFP16 | 17.8 | iOS12 | Machine Learning - Models - Apple Developer |
| YOLOv3TinyInt8LUT | 8.9 | iOS12 | Machine Learning - Models - Apple Developer |
| MobileNetV2_SSDLite | 9.3 | iOS12 | SSDMobileNet - Core ML Survival Guide |
| ObjectDetector | 63.7 | iOS12 | Recognizing Objects in Live Capture | Apple Developer Documentation |
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pc11000
commented on May 29, 2024
Excellent work!
Are you planning to add yolov4 too?
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tucan9389
commented on May 29, 2024
Excellent work!
Are you planning to add yolov4 too?
If I get the model, I'm able to measure the performance. But I haven't plan to convert the yolo v4 to Core ML model now myself.
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pc11000
commented on May 29, 2024
ok I may try it next week when v3 is to slow.
what is the "ObjectDetector" exactly? is it a detector from apple itself?
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tucan9389
commented on May 29, 2024
Hmm, I can't remember the detail of ObjectDetector.mlmodel but It seems it can detect general objects.
Here is the sample output of the model.
If you want to know the detail, please check the sample project provided by apple.
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Related Issues (16)
- Convert method
- Error when trying to run the code HOT 1
- Yolo8 to coreml conversion
- Saving Predicted Video HOT 1
- Support YOLOv8 HOT 7
- build iOS app error
- Why IPAD with M1 is pretty much slower than Iphone 13 Pro with A15??
- Problem with detection on IPhone 15 Pro
- How can I use my own created .mlmodel instead of yolov3?
- Yolo v4 or v5 HOT 4
- Support various TF object detection model
- Support YOLOv5 HOT 2
- images for README HOT 5
- Exporting from yolov5 to coreml HOT 6
- Are there any parameters in iOS that need to be adjusted to optimize real-time detection HOT 2
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