The frame rate (fps) you can achieve with your webcam and object detection model depends on several factors. These include the capabilities of your webcam, the efficiency of your object detection model, and the computational resources of your system (CPU and GPU).
In the forum post here, a user mentions that the fps values are dependent on the host system you are running the model on. Your CPU and GPU are major factors in the speed, as well as the type of stream you are using.
If you’re using OpenCV’s cv2.VideoCapture(0) to capture video from your webcam, it’s worth noting that the default frame rate might be lower than your webcam’s maximum fps. You might need to adjust the settings of cv2.VideoCapture to capture at higher frame rates.
However, even if your webcam can capture at 60 fps or 120 fps, achieving such high frame rates in real-time object detection can be challenging. Object detection models can be computationally intensive, and processing each frame might take longer than the time between frames at such high fps.
If you’re experiencing low fps, you might want to consider optimizing your object detection model or using a more efficient model. Roboflow provides a variety of object detection models with different trade-offs between speed and accuracy.
Remember that achieving high fps in object detection is not just about capturing video at high fps, but also processing each frame quickly enough. If the object detection takes too long, it will lower the effective fps of your application.
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