Efficientnet B0 Ra In1k FP16 Ov | ST Studio

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Efficientnet B0 Ra In1k FP16 Ov | ST Studio

pip install openvino-model-api[huggingface]

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-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)

Readme27. Aug.huggingface.co/OpenVINO/efficientnet_b0_ra_in1k-fp16-ov

--- license: apache-2.0 tags: - image-classification - vision base_model: - timm/efficientnet_b0.ra_in1k base_model_relation: finetune --- # efficientnet_b0_ra_in1k-fp16-ov - Model creator: [timm](https://huggingface.co/timm) - Original model: [timm/efficientnet_b0.ra_in1k](https://huggingface.co/timm/efficientnet_b0.ra_in1k) ## Description This is [timm/efficientnet_b0.ra_in1k](https://huggingface.co/timm/efficientnet_b0.ra_in1k) 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] ```  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_b0_ra_in1k-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 card](https://huggingface.co/timm/efficientnet_b0.ra_in1k) for limitations. ## Legal information The original model is distributed under the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license. More details can be found in [timm/efficientnet_b0.ra_in1k](https://huggingface.co/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](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.