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Mobilenet v3 Large Int8 Ov

Model creator: torchvision Original model: mobilenetv3large

ArtOtherVersionRevision 0b23c8dUnratedBewertung

Modellquelle

Art
Other
Dateigröße
9,68 MB
Version
vRevision 0b23c8d
Aktualisiert
5. Nov. 2026
Downloads
23
Quelle
Hugging Face

Quellenbeschreibung

mobilenet_v3_large-int8-ov

Description

This is a torchvision version of mobilenet_v3_large model converted to the OpenVINOTM IR (Intermediate Representation) format with weights compressed to INT8.

Quantization Parameters

Weight compression was performed using nncf.quantize with the following parameters:

  • Quantization method: Post-Training Quantization (PTQ)
  • Precision: INT8 for both weights and activations

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

  1. Install required packages:
pip install openvino-model-api[huggingface]
  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/mobilenet_v3_large-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 implementation for limitations.

Legal information

The original model is distributed under the BSD-3-Clause license. More details can be found in https://github.com/pytorch/vision.

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.

Quellen

1 Quelle
Hugging FacePrimärVersion 0b23c8d64ee3fb81055562685c20dd3a1c6eb1a0
huggingface.co/OpenVINO/mobilenet_v3_large-int8-ov/tree/0b23c8d64ee3fb81055562685c20dd3a1c6eb1a0

Verifiziert 5. Nov.

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Modellartefakte

1 Artefakt

mobilenet_v3_large.bin

bin · 9,68 MB · SHA-256 697928c662e8…4244 · Hugging Face

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Quellenauszüge

3 Auszüge

--- license: bsd-3-clause tags: - image-classification - vision --- # mobilenet_v3_large-int8-ov - Model creator: [torchvision](https://github.com/pytorch/vision) - Original model: [mobilenet_v3_large](https://docs.pytorch.org/vision/main/models/generated/torchvision.models.mobilenet_v3_large.html) ## Description This is a torchvision version of [mobilenet_v3_large](https://docs.pytorch.org/vision/main/models/generated/torchvision.models.mobilenet_v3_large.html) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2026/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT8. ## Quantization Parameters Weight compression was performed using nncf.quantize with the following parameters: - **Quantization method**: Post-Training Quantization (PTQ) - **Precision**: INT8 for both weights and activations 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/mobilenet_v3_large-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 implementation](https://github.com/pytorch/vision) for limitations. ## Legal information The original model is distributed under the [BSD-3-Clause](https://spdx.org/licenses/BSD-3-Clause.html) license. More details can be found in [https://github.com/pytorch/vision](https://github.com/pytorch/vision). ## Disclaimer Intel is committed to respecting human rights...

Source context: 23 downloads · 0 likes · Pipeline image-classification · Repo OpenVINO/mobilenet_v3_large-int8-ov

Source context: 10 downloads · 0 likes · Pipeline image-classification · Repo OpenVINO/mobilenet_v3_large-int8-ov