efficientnet_b2-int8-ov

efficientnet_b2-int8-ov

• Model creator: torchvision
• Original model: efficientnet_b2

Description

This is a torchvision version of efficientnet_b2 model converted to the OpenVINO™ 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 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

1. Install required packages:

pip install openvino-model-api[huggingface]

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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_b2-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.