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Efficientnet B1 Ft In1k FP16 Ov

Model creator: timm Original model: timm/efficientnetb1.ftin1k

TypeOtherModèle de basetimm/efficientnet_b1.ft_in1kVersionRevision af046e9CClasse

Source du modèle

Type
Other
Taille du fichier
14,8 MB
Modèle de base
timm/efficientnet_b1.ft_in1k
Version
vRevision af046e9
Mis à jour
13 oct. 2026
Téléchargements
8
Source
Hugging Face

Description de la source

efficientnet_b1_ft_in1k-fp16-ov

Description

This is timm/efficientnet_b1.ft_in1k model converted to the OpenVINOTM IR (Intermediate Representation) format with weights compressed to FP16.

Compatibility

The provided OpenVINOTM 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]
  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_b1_ft_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.

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_b1.ft_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.

Sources

1 source
Hugging FacePrincipalVersion af046e9dc06917576f41375e002327f392596df6
huggingface.co/OpenVINO/efficientnet_b1_ft_in1k-fp16-ov/tree/af046e9dc06917576f41375e002327f392596df6

Vérifié 26 août

Artefacts du modèle

1 artefact

efficientnet_b1_ft_in1k.bin

bin · 14,8 MB · SHA-256 10ed3e1c03bd…3dee · Hugging Face

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Extraits de sources

2 extraits

--- license: apache-2.0 tags: - image-classification - vision base_model: - timm/efficientnet_b1.ft_in1k base_model_relation: finetune --- # efficientnet_b1_ft_in1k-fp16-ov - Model creator: [timm](https://huggingface.co/timm) - Original model: [timm/efficientnet_b1.ft_in1k](https://huggingface.co/timm/efficientnet_b1.ft_in1k) ## Description This is [timm/efficientnet_b1.ft_in1k](https://huggingface.co/timm/efficientnet_b1.ft_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] ``` <!-- 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_b1_ft_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_b1.ft_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_b1.ft_in1k](https://huggingface.co/timm/efficientnet_b1.ft_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.

Source context: 8 downloads · 0 likes · Pipeline image-classification · Repo OpenVINO/efficientnet_b1_ft_in1k-fp16-ov