how can i Increase the inference speed even more using the yolov8nano model.

[serhatbulun]

Hi, I want to increase the inference speed on the object detection using the yolov8n model. I wonder if there are any tips to do this even further than just exporting it to a new format. One idea i was thinking about for my custom dataset is that the objects are of a specific size, mostly with squared bounding boxes, so i was thinking of limiting the models bounding box ratios and sizes, does anyone know how i could do this in the code or if this is going to give a significant change in the inference speed? Also are there any other option to increase the inference speed on the model?

Would really help with my project so any answer is appreciated!

[glenn-jocher]

Reduce model complexity: Choose a smaller and faster model architecture, like YOLOv8n or YOLOv5n. These models have fewer layers and parameters, which results in faster inference times at the cost of some accuracy.

Decrease input resolution: Lowering the input resolution will significantly reduce computation and improve inference speed. Keep in mind that this might affect the model's ability to detect small objects. Modify the --img-size parameter in the configuration file or during inference to a smaller value.

Use quantization: Quantization is the process of converting model weights and activations from floating-point representation to lower-precision integers, such as int8 or int16. Quantization can greatly speed up model inference with a slight trade-off in accuracy. TensorRT or TensorFlow Lite are popular frameworks for quantization.

Optimize the code: You can use TensorRT, OpenVINO, or other optimization tools to optimize the model for your specific hardware. These tools can fuse layers, prune weights, and perform other optimizations to improve inference speed.

Limit bounding box ratios and sizes: As you mentioned, if your objects have specific characteristics, you can customize anchor boxes in the model configuration file to better match your dataset. This may not significantly improve the inference speed but can help increase the model's detection accuracy and reduce false positives. Modify the anchors parameter in the configuration file with values that better represent your object sizes and ratios.

Use batch processing: If you need to process multiple images, you can feed them in batches to the model. This will utilize GPU resources more efficiently, thereby reducing the total processing time.

Hardware acceleration: Utilize specialized hardware like GPUs or TPUs to accelerate model inference. Make sure your framework supports the specific hardware you're using.

[serhatbulun]

Hi, I was wondering where this model configuration file can be found in the code for me to change the bounding box parameters? i would like to keep it square shaped or a ratio that does not change too much maybe 2:3 or 1:3 etc

[scraus]

Thank you @glenn-jocher! What would you recommend if the use case is to perform real-time detection on mobile devices (android and ios): OpenVino or TensorRT? Also, does ultralytics have a plan to create an article about how Ultralytics Hub App was built from the start upto deployment? I think it would really be a good read!

[mahdiNahidian]

can you send me some document for quantization and Optimize the code for model I trained on yolov8 architecture?

[glenn-jocher]

@mahdiNahidian for adjusting the bounding box aspect ratios, you would typically look into the model's configuration file where anchor box sizes are defined. However, in YOLOv8, these configurations are embedded within the model architecture and training process, which automatically learns the optimal anchor sizes for your dataset.

If you want to enforce specific aspect ratios, you might need to customize the model's architecture and training pipeline, which involves a deeper dive into the source code. This is an advanced modification and is not typically recommended unless you have a strong understanding of the model's internals.

For square-shaped bounding boxes or maintaining a specific aspect ratio like 2:3 or 1:3, you would need to adjust the anchor generation process during training to produce anchors with your desired aspect ratios. This could potentially be done by modifying the anchor calculation code within the YOLOv8 source code, but such a feature is not directly exposed for user configuration.

Keep in mind that while this might improve performance for your specific use case, it could also limit the model's ability to generalize to objects with different aspect ratios. It's also important to note that this kind of modification may not lead to a significant increase in inference speed, as the speed is more directly affected by factors such as model size, input resolution, and hardware capabilities.