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Mobilenetv2 100 Ra In1k FP16 Ov

Model creator: timm Original model: timm/mobilenetv2100.rain1k

KindOtherBase modeltimm/mobilenetv2_100.ra_in1kVersionRevision 2d3df3dUnratedgrade

Model source

Kind
Other
File size
6.65 MB
Base model
timm/mobilenetv2_100.ra_in1k
Version
vRevision 2d3df3d
Updated
Nov 5, 2026
Downloads
19
Source
Hugging Face

Source description

mobilenetv2_100_ra_in1k-fp16-ov

Description

This is timm/mobilenetv2_100.ra_in1k model converted to the OpenVINOTM IR (Intermediate Representation) format with weights compressed to FP16.

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_100_ra_in1k-fp16-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_100.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.

Sources

1 source
Hugging FacePrimaryVersion 2d3df3d5377c49b154ba0bb6dfac74af2a388550
huggingface.co/OpenVINO/mobilenetv2_100_ra_in1k-fp16-ov/tree/2d3df3d5377c49b154ba0bb6dfac74af2a388550

Verified Nov 5

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Repository files

File inventory only; completeness is unverified.

Model artifacts

1 artifact

mobilenetv2_100_ra_in1k.bin

bin · 6.65 MB · SHA-256 c315744737dc…c79b · Hugging Face

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Source excerpts

3 excerpts

--- license: apache-2.0 tags: - image-classification - vision base_model: - timm/mobilenetv2_100.ra_in1k base_model_relation: finetune --- # mobilenetv2_100_ra_in1k-fp16-ov - Model creator: [timm](https://huggingface.co/timm) - Original model: [timm/mobilenetv2_100.ra_in1k](https://huggingface.co/timm/mobilenetv2_100.ra_in1k) ## Description This is [timm/mobilenetv2_100.ra_in1k](https://huggingface.co/timm/mobilenetv2_100.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 FP16. ## 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_100_ra_in1k-fp16-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_100.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_100.ra_in1k](https://huggingface.co/timm/mobilenetv2_100.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](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). 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.

Source context: 19 downloads · 0 likes · Pipeline image-classification · Repo OpenVINO/mobilenetv2_100_ra_in1k-fp16-ov

Source context: 7 downloads · 0 likes · Pipeline image-classification · Repo OpenVINO/mobilenetv2_100_ra_in1k-fp16-ov