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license: bsd-3-clause
tags:
- image-classification
- vision
---
# efficientnet_b2-int8-ov
- Model creator: [torchvision](https://github.com/pytorch/vision)
- Original model: [efficientnet_b2](https://docs.pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b2.html)
## Description
This is a torchvision version of [efficientnet_b2](https://docs.pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b2.html) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2026/documentation/openvino-ir-format.html) (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](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/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](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.
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