I am testing a system for detecting and counting people in a specific area and moment (a single image). The issue is that it can only detect 300 objects at a time. Is there any possibility of increasing this number? (There are more than 300 objects in the image). I am using YOLOv8 trained from scratch. Any suggestions are welcome.
Based on the information provided in the extracts, it’s not explicitly mentioned how to increase the number of objects that YOLOv8 can detect in a single image. However, there are a few things you might consider:
Model Configuration: Check your model’s configuration file. There might be parameters that limit the number of detections per image. Adjusting these parameters could potentially increase the number of detections.
Training Data: As mentioned in the forum post, having a large amount of training data can improve the model’s ability to detect multiple classes. Although your case is about detecting more instances of the same class (people), having more diverse examples of people in different positions, lighting conditions, and densities could potentially help.
Hardware Limitations: The number of objects that can be detected could also be limited by your hardware. As mentioned in the forum post, training a model to detect a large number of classes or objects can be computationally intensive and may require high-quality GPUs or a machine learning server.
Model Limitations: It’s also possible that the limitation is inherent to the YOLOv8 model itself. If this is the case, you might need to consider using a different model or approach.
Remember to test your model thoroughly after making any changes to ensure that its performance has improved as expected. If you’re still having trouble, you might want to reach out to the Roboflow community or support for more specific advice.
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How can i adjust the parameters of the model configuration. I use for training:
model.train(data=“./datasets/Core_counts_v2/data.yaml”, epochs=1000,imgsz=640,patience=100, batch=10, workers=10, optimizer=“Adam”)
I run the model on a nvidia 3090ti y a intel machine with 128 gb RAM
I answer myself
When you make the inference, you can increase the maximum number of instances to detect. In my case, it could be something like this:
result = list(model.predict(image, conf=0.1, verbose=False, max_det=1000))
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