Formats
OpenAI GPT-4o Object Detection JSONL

OpenAI GPT-4o Object Detection JSONL

The format required by OpenAI for fine-tuning GPT-4o for object detection.
Formats

OpenAI GPT-4o Object Detection JSONL

Below, we show how to convert data to and from

OpenAI GPT-4o Object Detection JSONL

. We also list popular models that use the

OpenAI GPT-4o Object Detection JSONL

data format. Our conversion tools are free to use.

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OpenAI GPT-4o Object Detection JSONL
to the following formats.

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Convert Data from OpenAI GPT-4o Object Detection JSONL

Use Roboflow to convert the following formats to
OpenAI GPT-4o Object Detection JSONL
format.

Roboflow is a trusted solution for converting and managing your data. Today, over 250,000 datasets are managed on Roboflow, comprised of 100 million labeled and annotated images.

With Roboflow, you get:

Free data conversion

SOC II Type 1 Compliant

Trusted by 250,000+ developers

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Roboflow is a trusted solution for converting and managing your data. Today, over 100,000 datasets are managed on Roboflow, comprised of 100 million labeled and annotated images.

Once your data is in Roboflow, just add the link from your dataset and you're ready to go. We even include the code to export to common inference formats like TFLite, ONNX, and CoreML.

Below are pre-configured models that use the
OpenAI GPT-4o Object Detection JSONL
data format
.

What computer vision models use OpenAI GPT-4o Object Detection JSONL?

The

No items found.

models all use the

OpenAI GPT-4o Object Detection JSONL

data format.

We don't currently have models that use this annotation format.
To see our entire list of computer vision models, check out the Roboflow Model Library.

{
  "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "detect "},
    {
      "role": "user",
      "content": [
        {
          "type": "image_url",
          "image_url": {
            "url": "URL"
          }
        }
      ]
    },
    {
      "role": "assistant",
      "content": " object ;  object"
    }
  ]
}

The loc tags correspond with the locations of objects in images. Individual annotations are separate by semi-colons, following the structure:

loc-tags class-name; loc-tags class-name;