YOLO11n Int8 Ov
Model creator: Ultralytics Original model: Ultralytics/YOLO11
Source du modèle
- Type
- Other
- Taille du fichier
- 2,65 MB
- Modèle de base
- Ultralytics/YOLO11
- Version
- vRevision 5e4c7d2
- Mis à jour
- 23 oct. 2026
- Téléchargements
- 16
- Source
- Hugging Face
Description de la source
YOLO11n-int8-ov
- Model creator: Ultralytics
- Original model: Ultralytics/YOLO11
Description
This is Ultralytics/YOLO11 model converted to the OpenVINOTM 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 OpenVINOTM 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
- Install required packages:
pip install openvino-model-api[huggingface]
- 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/YOLO11n-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.
Sources
1 source
Sources
1 sourceVérifié 18 août
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactExtraits de sources
2 extraits
Extraits de sources
2 extraits--- license: agpl-3.0 tags: - object-detection - vision base_model: - Ultralytics/YOLO11 base_model_relation: quantized --- # YOLO11n-int8-ov - Model creator: [Ultralytics](https://huggingface.co/Ultralytics) - Original model: [Ultralytics/YOLO11](https://huggingface.co/Ultralytics/YOLO11) ## Description This is [Ultralytics/YOLO11](https://huggingface.co/Ultralytics/YOLO11) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2026/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT8 by [NNCF](https://github.com/openvinotoolkit/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](https://docs.openvino.ai/2026/openvino-workflow/model-optimization-guide/quantizing-models-post-training.html). ## 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](https://github.com/open-edge-platform/model_api) 1. Install required packages: ```sh pip install openvino-model-api[huggingface] ``` <!-- markdownlint-disable MD029 --> 2. Run model inference: ```python import cv2 from model_api.models import Model from model_api.visualizer import Visualizer # 1. Load model model = Model.from_pretrained("OpenVINO/YOLO11n-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](https://open-edge-platform.github.io/model_api/latest/). ## Limitations Check the [original model card](https://huggingface.co/Ultralytics/YOLO11) for limitations. ## Legal information The original model is distributed under [GNU Affero General Public License v3.0](https://choosealicense.com/licenses/agpl-3.0/) license. More details can be found in [Ultralytics/YOLO11](http...
Source context: 16 downloads · 0 likes · Pipeline object-detection · Repo OpenVINO/YOLO11n-int8-ov
