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Mobilenetv2 110d Ra In1k Int8 Ov

Model creator: timm Original model: timm/mobilenetv2110d.rain1k

Fuentehuggingface.co/OpenVINO/mobilenetv2_110d_ra_in1k-int8-ov/tree/c30bb5859656526b0674c5bbd43c267ba92b1db8
TipoOtherModelo basetimm/mobilenetv2_110d.ra_in1kVersiónRevision c30bb58Unratedcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
4,53 MB
Modelo base
timm/mobilenetv2_110d.ra_in1k
Versión
vRevision c30bb58
Actualizado
5 nov 2026
Descargas
21
Fuente
Hugging Face

Descripción de la fuente

mobilenetv2_110d_ra_in1k-int8-ov

Description

This is timm/mobilenetv2_110d.ra_in1k model converted to the OpenVINOTM IR (Intermediate Representation) format with weights compressed to INT8 by NNCF.

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/mobilenetv2_110d_ra_in1k-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 the Apache-2.0 license. More details can be found in timm/mobilenetv2_110d.ra_in1k

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.

Fuentes

1 fuente
Hugging FacePrincipalVersión c30bb5859656526b0674c5bbd43c267ba92b1db8
huggingface.co/OpenVINO/mobilenetv2_110d_ra_in1k-int8-ov/tree/c30bb5859656526b0674c5bbd43c267ba92b1db8

Verificado 5 nov

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Artefactos del modelo

1 artefacto

mobilenetv2_110d_ra_in1k.bin

bin · 4,53 MB · SHA-256 0b918ea3e563…da14 · Hugging Face

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Extractos de fuentes

3 extractos
Readme27 agohuggingface.co/OpenVINO/mobilenetv2_110d_ra_in1k-int8-ov

--- license: apache-2.0 tags: - image-classification - vision base_model: - timm/mobilenetv2_110d.ra_in1k base_model_relation: quantized --- # mobilenetv2_110d_ra_in1k-int8-ov - Model creator: [timm](https://huggingface.co/timm) - Original model: [timm/mobilenetv2_110d.ra_in1k](https://huggingface.co/timm/mobilenetv2_110d.ra_in1k) ## Description This is [timm/mobilenetv2_110d.ra_in1k](https://huggingface.co/timm/mobilenetv2_110d.ra_in1k) 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 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/mobilenetv2_110d_ra_in1k-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/timm/mobilenetv2_110d.ra_in1k) for limitations. ## Legal information The original model is distributed under the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license. More details can be found in [timm/mobilenetv2_110d.ra_in1k](https://huggingface.co/timm/mobilenetv2_110d.ra_in1k) ## Discl...

Note5 novhuggingface.co/OpenVINO/mobilenetv2_110d_ra_in1k-int8-ov

Source context: 21 downloads · 0 likes · Pipeline image-classification · Repo OpenVINO/mobilenetv2_110d_ra_in1k-int8-ov

Note27 agohuggingface.co/OpenVINO/mobilenetv2_110d_ra_in1k-int8-ov

Source context: 12 downloads · 0 likes · Pipeline image-classification · Repo OpenVINO/mobilenetv2_110d_ra_in1k-int8-ov