Understanding Tensorflow Object Detection Data Structure
If you are into machine learning and object detection, you must be familiar with TensorFlow, one of the most popular open-source machine learning frameworks. In this lesson, we will dive into the data structure used in TensorFlow for object detection.
What is TensorFlow Object Detection Data Structure?
TensorFlow Object Detection API provides a collection of detection models pre-trained on the COCO dataset, the Kitti dataset, and the Open Images dataset. The data structure used in TensorFlow for object detection supports various input formats such as image, video, and webcam streams.
Data Structure
The data structure used in TensorFlow for object detection consists of input data, model, and output data. The input data can be in the form of images or video streams, which are fed into the detection model. The model is responsible for processing the input data and generating the output, which includes bounding boxes, labels, and scores of detected objects.
Understanding the Output Data
The output data from the detection model is represented in a structured format, which includes bounding box coordinates, class labels, and confidence scores. This information is crucial for identifying and localizing objects in the input data.
How to Use TensorFlow Object Detection Data Structure
To use the data structure in TensorFlow for object detection, you can leverage the pre-trained models provided by the TensorFlow Object Detection API. You can also train your own custom object detection model using your own dataset and incorporate it into the data structure.
Conclusion
Understanding the data structure used in TensorFlow for object detection is essential for building and deploying object detection models. By grasping the input, model, and output data structure, you can effectively use TensorFlow for various object detection tasks.
Hi, thx for this very good tutorial and thx for your perfect english, making it easy for understanding your explanations for non native speakers. All works fine. However has anyone already used the example with the USB Coral Edge TPU to run? Only the option set to TRUE does not work. I couldn't find anything on the web about the error message. I have already replaced the model, but I fear you have to install the Edge TPU runtime according to the Coral website, which I'm afraid will destroy the current installation, so it would be nice to get some information about the pitfalls beforehand. Thanks in advance!
Thanks Paul. I love it.
Paul, you make this so fun. Thank you.
Please make more videos on Zigbee and Lora. I love your class.
Excellent video Paul! Very clear and informative. Thanks for your effort.
Hey thank you so much for this video, I got my TensorFlow Lite working with the picamera v3. Do you have any tips on how to create my own model? I want to make one just for a simple rock paper scissors game, but when i tried to do it on google colab everything started breaking on me. Any tips would be greatly appreciated!
I AM LEGEND! My banana tracker works pretty well. Thanks for another great lesson Paul.
Im starting to get it some but this has really just kicked my behind up to this point . I still haven't managed to get the webCam working throws an error at bgrtorbg i cant figure it out says src is empty? I dont know what it is . But the picam works.❤😁👍
Hello Paul. The Orange Pi-5 is running {7 FPS 1280 x 720} and {21 FPS 800 x 600} using a new Logitech C270 USB webcam with a sharper image. The old HP webcam {10 FPS 1280 x 720} so a camera makes some difference. The CPU 8 cores were only about 40% and running cool. There is a GPU NPU that I have not learned how to use yet? This is a great learning experience and the step-by-step line-by-line TensorFlow Lite instructions really help. 🐬 Thank you so much.
Excellent lesson Paul. Heading exactly where I want to go.
Sir, please bring some home automation based projects..🙏
if you get this error : File "/home/m_torbett/Python/Lesson64HW.py", line 55, in <module>
imRGB=cv2.cvtColor(im,cv2.COLOR_BGR2RGB)
cv2.error: OpenCV(4.5.3) /tmp/pip-req-build-r02f5qx8/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'.
webCam='/dev/video1' is set to the wrong address! Run v4l2-ctl –list-devices on the terminal to get the address for your webCam.
Beautiful programming. Easy to understand. Thanks Paul. ❤❤❤
This lesson still works ok on a RPI3B 🙂
Thank you so much!!! Please don't stop doing what you're doing.
I wonder if the heat of the pi 4 after running 40 minutes could affect performance? I have noticed that it slows at the end of the lessons.
If there is some one who knows about orange pi library opi.gpio please replay to me i keep geting a errno 22 error
Thanks Paul!
I hope to be online for coffee and loud music warning but will have to lease shortly after.
Hello Sir. I'm called Rudolf.
I love your teachings and i was so happy to come across your raspberry pi tutorials after taken that of arduino.
I know your almost coming to an end of the course but I'll like to ask a favor from you.
I'm working on a project to assist the visually impaired and in it I'll like to include some Bluetooth functionalities that will allow the blind to communicate wireless to the main device using a pair of wireless headphones.
Could you please make a video on pybluez and how to send data (mostly audio) from the headphone to the main device and vice versa.
I would really appreciate it if you could help me with this Sir. Thank you in advance 🙏🥺