Efficientnet B4 Int8 Ov

--- license: bsd-3-clause tags: - image-classification - vision --- # efficientnet_b4-int8-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 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_b4-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 caus...