can not convert model to tflite file

ppl520zfl commented 2 weeks ago

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[X] I have searched the YOLOv5 issues and found no similar bug report.

YOLOv5 Component

Detection

Bug

hi, i try to convert pt file to tflite file, but get the flow exception:

here is my model config，i replace backbone to MobileNetv3,

nc: 7
depth_multiple: 0.33
width_multiple: 0.25
anchors:
  - [10, 13, 16, 30, 33, 23]
  - [30, 61, 62, 45, 59, 119]
  - [116, 90, 156, 198, 373, 326]

backbone:

  [[-1, 1, Conv3BN, [16, 2]],
   [-1, 1, MobileNetv3, [ 16,  16, 3, 1, 0, 0]],
   [-1, 1, MobileNetv3, [ 24,  64, 3, 2, 0, 0]],
   [-1, 1, MobileNetv3, [ 24,  72, 3, 1, 0, 0]],
   [-1, 1, MobileNetv3, [ 40,  72, 5, 2, 1, 0]],
   [-1, 1, MobileNetv3, [ 40, 120, 5, 1, 1, 0]],
   [-1, 1, MobileNetv3, [ 40, 120, 5, 1, 1, 0]],
   [-1, 1, MobileNetv3, [ 80, 240, 3, 2, 0, 1]],
   [-1, 1, MobileNetv3, [ 80, 200, 3, 1, 0, 1]],
   [-1, 1, MobileNetv3, [ 80, 184, 3, 1, 0, 1]],
   [-1, 1, MobileNetv3, [ 80, 184, 3, 1, 0, 1]],
   [-1, 1, MobileNetv3, [112, 480, 3, 1, 1, 1]],
   [-1, 1, MobileNetv3, [112, 672, 3, 1, 1, 1]],
   [-1, 1, MobileNetv3, [160, 672, 5, 1, 1, 1]],
   [-1, 1, MobileNetv3, [160, 672, 5, 2, 1, 1]],
   [-1, 1, MobileNetv3, [160, 960, 5, 1, 1, 1]],
  ]

head:
  [[-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 13], 1, Concat, [1]],
   [-1, 1, C3, [256, False]],

   [-1, 1, Conv, [128, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 6], 1, Concat, [1]],
   [-1, 1, C3, [128, False]],

   [-1, 1, Conv, [128, 3, 2]],
   [[-1, 20], 1, Concat, [1]],
   [-1, 1, C3, [256, False]],

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 16], 1, Concat, [1]],
   [-1, 1, C3, [512, False]],

   [[23, 26, 29], 1, Detect, [nc, anchors]],
  ]

Please help me Thanks！

Environment

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Minimal Reproducible Example

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Additional

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Are you willing to submit a PR?

[ ] Yes I'd like to help by submitting a PR!

github-actions[bot] commented 2 weeks ago

👋 Hello @ppl520zfl, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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Introducing YOLOv8 🚀

We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 🚀!

Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.

Check out our YOLOv8 Docs for details and get started with:

pip install ultralytics

glenn-jocher commented 2 weeks ago

@ppl520zfl hi there,

Thank you for reaching out and providing details about your issue. To assist you effectively, we need a bit more information. Could you please provide a minimal reproducible code example? This will help us understand the context and reproduce the issue on our end. You can refer to our guide on creating a minimal reproducible example here: Minimum Reproducible Example.

Additionally, please ensure that you are using the latest versions of torch and the YOLOv5 repository. Sometimes, issues are resolved in newer releases, and updating might solve your problem.

Looking forward to your response so we can help you resolve this issue!

ultralytics / yolov5