YOLOv5 Model Optimization

[Waqas649]

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Hello, I trained a custom yolov5 model, now I want to deploy it, but first I want to optimize it, is there any easy way to optimize it with openvino, some months ago, I used a online toolkit to optimize it but now its not found, anyone can help pls. Thanks

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[github-actions]

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[pderrenger]

Hello! 😊 Great to hear about your progress with your custom YOLOv5 model.

Optimizing your model for deployment with OpenVINO can indeed streamline the inference process. While we don't have a predefined online toolkit, converting your model to an OpenVINO-compatible format involves a couple of steps, starting with exporting your model to ONNX format. After that, you can utilize the OpenVINO Model Optimizer to convert the ONNX model to an IR (Intermediate Representation) format that's optimized for inference on various Intel hardware.

For a detailed step-by-step guide, including necessary commands and further optimization tips, please refer to the "Deployment" section in our official documentation at https://docs.ultralytics.com/hub. It offers a comprehensive walkthrough that should fit your needs.

If you encounter any specific issues or have further questions during the process, feel free to reach out here again. Happy optimizing! 🚀

[iraa777]

Hello,

I am trying to optimize a model I self-trained based on Yolov5s architecture. The device I am trying to run it on has no GPU and is not NVIDIA so I cannot use TensorRT. I originally tried a quantization code, but this is not optimizing my model at all. I attach the code here:
import torch
import torch.quantization
from utils.torch_utils import select_device
from models.common import DetectMultiBackend
from models.experimental import attempt_load
from torch.quantization import QuantStub, DeQuantStub, default_dynamic_qconfig, default_qconfig
from torch.utils.mobile_optimizer import optimize_for_mobile

Define GPU

device = select_device('')

Check original model

DetectMultiBackend(weights="20230329_s.pt", device=device, dnn=False, data='data.yaml', fp16=False)

Load your YOLOv5 model

ori_model = torch.load("20230329_s.pt", map_location = device)
print(ori_model.keys())
print(1)

Supongamos que el modelo está bajo la clave 'model'

model = ori_model['model']

Prepare your model for quantization

model.eval()

Define quantization configuration targeting all layers

qconfig = default_qconfig
qconfig_dict = {'': qconfig}

Apply quantization to the model

quantized_model = torch.quantization.quantize_dynamic(
model, # Your model
qconfig_spec=qconfig_dict, # Quantization configuration
dtype=torch.qint8 # Target data type after quantization
)
print(2)

Edit model field on original model

ori_model['model'] = quantized_model

Save the quantized model

torch.save(ori_model, "quantized.pt")
print(3)
I would appreciate if anyone has had experience with this. Maybe tehre is a tool available that I don't have knowledge about that can make my work much easier.

Thankyou,

Irati

[pderrenger]

Hello Irati,

It sounds like you've given a good initial attempt at quantizing your YOLOv5 model! Quantization can indeed be tricky depending on the specific characteristics of the model and the target device's requirements.

Since you’re facing issues with standard dynamic quantization not effectively optimizing your model, you might consider trying static quantization which involves a few additional steps like preparing calibration data to better understand the distribution of inputs. This approach can sometimes yield better performance outcomes, especially if dynamic quantization doesn't meet your expectations.

Another alternative might be to explore pruning before quantization, which reduces the model size and complexity by removing unnecessary weights, potentially making the quantization more effective.

If these approaches don’t suit your needs, looking into other hardware-specific libraries compatible with your device's architecture (other than TensorRT) could be beneficial. Some devices have specialized libraries or SDKs designed to optimize models specifically for their architecture.

Keep experimenting and don’t hesitate to reach out if you have more questions! 💪

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