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Rfdetr Medium FP16 Ov

Model creator: GetiTM Original model: RF-DETR Medium

KindOtherVersionRevision 3ec5a3fCgrade

Model source

Kind
Other
File size
57.4 MB
Version
vRevision 3ec5a3f
Updated
Nov 1, 2026
Downloads
15
Source
Hugging Face

Source description

rfdetr_medium-fp16-ov

Description

This is a GetiTM version of RF-DETR Medium model converted to the OpenVINOTM IR (Intermediate Representation) format with weights compressed to FP16.

To fine-tune your model with a custom dataset, you can use GetiTM to annotate data, perform fine-tuning, and export the resulting model.

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/rfdetr_medium-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 documentation for limitations.

Legal information

The original model is distributed under the Apache-2.0 license. More details can be found in the original model repository.

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 FacePrimaryVersion 3ec5a3f97d155b0557c89b95720ebcfc4211c99a
huggingface.co/OpenVINO/rfdetr_medium-fp16-ov/tree/3ec5a3f97d155b0557c89b95720ebcfc4211c99a

Verified Nov 1

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Model artifacts

1 artifact

rfdetr_medium.bin

bin · 57.4 MB · SHA-256 43e249a12a5f…f0fe · Hugging Face

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Source excerpts

3 excerpts

--- license: apache-2.0 tags: - object-detection - vision --- # rfdetr_medium-fp16-ov - Model creator: [Geti™](https://github.com/open-edge-platform/geti) - Original model: [RF-DETR Medium](https://github.com/roboflow/rf-detr) ## Description This is a [Geti™](https://github.com/open-edge-platform/geti) version of [RF-DETR Medium](https://github.com/roboflow/rf-detr) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2026/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to FP16. To fine-tune your model with a custom dataset, you can use Geti™ to annotate data, perform fine-tuning, and export the resulting model. ## 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/rfdetr_medium-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 documentation](https://github.com/roboflow/rf-detr) for limitations. ## Legal information The original model is distributed under the [Apache-2.0](https://roboflow.com/licensing) license. More details can be found in the [original model repository](https://github.com/roboflow/rf-detr). ## 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: 15 downloads · 0 likes · Pipeline object-detection · Repo OpenVINO/rfdetr_medium-fp16-ov

Source context: 8 downloads · 0 likes · Pipeline object-detection · Repo OpenVINO/rfdetr_medium-fp16-ov