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OpenVINO/
efficientnet_b0_ra_in1k-int8-ov

Name: efficientnet_b0_ra_in1k-int8-ov
Availability: InStock

Classification

image-classification

vision

timm/efficientnet_b0.ra_in1k

quantized

apache-2.0

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efficientnet_b0_ra_in1k-int8-ov

Model creator: timm
Original model: timm/efficientnet_b0.ra_in1k

Description

This is timm/efficientnet_b0.ra_in1k model converted to the OpenVINO™ 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 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

Install required packages:

pip install openvino-model-api[huggingface]

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_b0_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/efficientnet_b0.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.