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PROBLEM ABOUT THE DEPLOYMENT OF THE MODEL TO THE HARDWARE. #603
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👋 Hello @RonahJay-Emperio, thank you for raising an issue about Ultralytics HUB 🚀! Please visit our HUB Docs to learn more:
If this is a 🐛 Bug Report, please provide screenshots and steps to reproduce your problem to help us get started working on a fix. If this is a ❓ Question, please provide as much information as possible, including dataset, model, environment details etc. so that we might provide the most helpful response. We try to respond to all issues as promptly as possible. Thank you for your patience! |
@RonahJay-Emperio Thanks for raising the question. We are not able to help as we cannot see your code. We are currently working on getting the Ultralytics App code ready for open source, in the mean time we have a similair app already open sourced for iOS. My advice would be to take a look at the repo and see if there are any differences to how we load and run the model: |
Hi, @kalenmike! Thank You for your response. I used the Luxonis OAK FFC 4P device and it seems that the detection is not accurate because there's a lot of bounding boxes that are being detected during deployment. How can I detect the classes one at a time? I would really appreciate it if you can help me with this issue. Thank You. Here's the process I made before deployment:
Here's the sample code that I used: from pathlib import Path nnPathDefault = str((Path(file).parent / Path('../models/worker_activity_openvino_2022.1_6shave.blob')).resolve().absolute()) if not Path(nnPathDefault).exists(): MobilenetSSD label textslabelMap = ["Walking", "At-Desk-Working", "Fall", "Running", "Sleeping", "Standing-NotWorking", "Standing-Working", "At-Desk-NotWorking"] Create pipelinepipeline = dai.Pipeline() Define sources and outputscamRgb = pipeline.create(dai.node.ColorCamera) xoutRgb.setStreamName("rgb") PropertiescamRgb.setPreviewSize(320, 320) Define a neural network that will make predictions based on the source framesnn.setNumInferenceThreads(2) blob = dai.OpenVINO.Blob(args.nnPath) Linkingif args.sync: camRgb.preview.link(nn.input) Connect to device and start pipelinewith dai.Device(pipeline) as device:
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Hi @RonahJay-Emperio! It sounds like the issue you're experiencing might be related to overlapping detections and the confidence threshold of your model. 🤔 Given the details you've provided, here are a couple of relatively straightforward steps you can experiment with to address the problem of multiple bounding boxes and ensure your model detects classes more distinctly:
Since it looks like you're using the DepthAI library with OAK, double-check the DepthAI documentation or forums for specific advice on optimizing detections and applying NMS correctly for your device. Keep in mind adjusting the confidence threshold is often the first step and can significantly reduce unwanted detections. If the issue persists, it might be beneficial to explore other preprocessing steps or even retrain your model with varied data to improve its robustness against false positives. I hope this helps! If you're still facing issues, providing details about the detection results post-adjustment could be useful for further diagnosis. 🛠️ |
👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help. For additional resources and information, please see the links below:
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Hi! I tried to deploy the model to the hardware and it seems like the hardware cannot detect the classes based on the model that I created. I tried to test the model in the Ultralytics app and it works perfectly. My main concern is, how can we fix this issue and what are the possible cause of this error? I hope to get a fast response from you about this matter. Thank You!
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