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Efficientnet B4 FP16 Ov

Model creator: torchvision Original model: efficientnetb4

KindOtherVersionRevision 479126dCgrade

Model source

Kind
Other
File size
36.8 MB
Version
vRevision 479126d
Updated
Oct 13, 2026
Downloads
6
Source
Hugging Face

Source description

efficientnet_b4-fp16-ov

Description

This is a torchvision version of efficientnet_b4 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/efficientnet_b4-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 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.

Sources

1 source
Hugging FacePrimaryVersion 479126d87acbdefc1e17406c42462761dcc2d641
huggingface.co/OpenVINO/efficientnet_b4-fp16-ov/tree/479126d87acbdefc1e17406c42462761dcc2d641

Verified Aug 26

Model artifacts

1 artifact

efficientnet_b4.bin

bin · 36.8 MB · SHA-256 6005c8496d3e…595f · Hugging Face

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Source excerpts

2 excerpts

--- license: bsd-3-clause tags: - image-classification - vision --- # efficientnet_b4-fp16-ov - Model creator: [torchvision](https://github.com/pytorch/vision) - Original model: [efficientnet_b4](https://docs.pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b4.html) ## Description This is a torchvision version of [efficientnet_b4](https://docs.pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b4.html) 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/efficientnet_b4-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 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 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: 6 downloads · 0 likes · Pipeline image-classification · Repo OpenVINO/efficientnet_b4-fp16-ov