YOLO11x-int8-ov

YOLO11x-int8-ov

• Model creator: Ultralytics
• Original model: Ultralytics/YOLO11

Description

This is Ultralytics/YOLO11 model converted to the OpenVINO™ IR (Intermediate Representation) format with weights compressed to INT8 by NNCF.

Quantization Parameters

This model was quantized using Post-Training Quantization (PTQ) with the following configuration:

• Quantization method: Post-Training Quantization (PTQ)
• Precision: INT8 for both weights and activations
• Calibration dataset: COCO128 (128 images from COCO dataset)
• Framework: Ultralytics with OpenVINO export

For more information on quantization, check the OpenVINO model optimization guide.

Compatibility

The provided OpenVINO™ IR model is compatible with:

• OpenVINO version 2026.1.0 and higher
• Model API 0.4.0 and higher

Running Model Inference with Model API

1. Install required packages:

pip install openvino-model-api[huggingface]

<!-- markdownlint-disable MD029 -->

1. Run model inference:

import cv2
from model_api.models import Model
from model_api.visualizer import Visualizer

# 1. Load model
model = Model.from_pretrained("OpenVINO/YOLO11x-int8-ov")

# 2. Load image
image = cv2.imread("image.jpg")

# 3. Run inference
result = model(image)

# 4. Visualize and save results
vis = Visualizer().render(image, result)
cv2.imwrite("output.jpg", vis)

For more examples and possible optimizations, refer to the Model API Documentation.

Limitations

Check the original model card for limitations.

Legal information

The original model is distributed under GNU Affero General Public License v3.0 license. More details can be found in Ultralytics/YOLO11.

Disclaimer

Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See Intel’s Global Human Rights Principles. Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.