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Grounding DINO finds objects in an image from words or phrases you provide: upload an image, describe what to locate, then review the predicted boxes and labels. Hautechai’s Replicate page offers a hosted H100 build; the official Grounding DINO repository is the route for local setup and implementation detail. The hosted listing and official code are related, but they are not interchangeable sources for the hosted model’s exact API behavior.
What Grounding DINO does
Grounding DINO is an open-set object detector. A conventional detector is generally trained to recognize a fixed set of categories; Grounding DINO uses language to specify what to find, including category names and more descriptive referring expressions. The authors describe it as combining the Transformer-based DINO detector with grounded pre-training. Its architecture includes a feature enhancer, language-guided query selection, and a cross-modality decoder. You do not need to understand those components to try a prompt: provide an image and text, then assess the resulting boxes and labels. The Grounding DINO paper and official implementation explain the approach.
“Open-set” and “zero-shot” describe the model’s ability to use language to request concepts beyond a fixed label list; they do not mean every phrase will be recognized correctly. Performance depends on the image, the wording, and the evaluation setting.
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What Hautechai’s Replicate listing offers
Hautechai’s Replicate model page describes its offering as “Grounding DINO: zero-shot text-prompted object detection (SwinT-OGC). H100 build.” The page also says the model is open source and can run on a computer with Docker, but currently states that it has no README. The linked official repository provides background and a local workflow; it does not establish the exact input fields or output format of this particular hosted build.
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As displayed on October 7, 2026, Replicate listed an approximate cost of $0.058 per run (about 17 runs per US dollar) and a typical prediction time of 38 seconds. Replicate says cost and prediction time vary significantly with inputs, so these are provider estimates, not stable prices, guaranteed runtimes, or independent measurements. Check the live listing before relying on them.
How to write a useful text prompt
At the task level, give the detector an image and words describing the objects or phrases you want located. The official implementation says it predicts boxes and phrase labels from the image-and-text pair. For a list of category names, its README recommends separating the names with periods, for example:
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chair . person . dog .
A referring expression can be more specific than a category name when you want to describe an object by appearance or context. Start with wording that distinguishes the target from other things in the image. If the result misses an object or labels the wrong region, try a clearer or more specific phrase and inspect the boxes rather than assuming the model understood the prompt as intended.
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The official code describes two filtering controls. box_threshold filters candidate boxes according to their highest similarity; text_threshold filters words used as predicted labels. In practical terms, raising a threshold makes selection more restrictive and may omit weaker detections. The official README’s example uses 0.35 for the box threshold and 0.25 for the text threshold, but those are example values, not universal settings for every image or prompt.
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Hautechai’s current page does not provide a README, and its API documentation was not available for confirming the hosted schema. Do not assume that it uses the official implementation’s field names, threshold defaults, or output structure. A different Replicate deployment, Adirik’s Grounding DINO listing, has its own documented fields; that documentation describes Adirik’s deployment only.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Hosted inference or local installation?
| Choice | What it involves | Useful when |
|---|---|---|
| Hautechai on Replicate | A hosted H100 build accessed through Replicate. The listing’s price and typical runtime are variable estimates, not guarantees. | You want to try hosted inference without setting up the official project locally. |
| Official implementation | Clone the project, install dependencies, download the SwinT-OGC pretrained weights, and run an image inference demo. The repository includes a CPU-only mode. | You want direct access to the implementation and more control over a local workflow. |
The trade-off is not simply GPU versus no GPU. Hosted use has a per-run service cost and depends on the provider’s interface; local use requires environment setup and uses your own machine and operating resources. The repository’s CPU-only option means a GPU is not mandatory to run the code, but the available sources do not establish a universal hardware requirement for practical speed. Review the repository’s Apache-2.0 license and the applicable terms for pretrained weights and other assets before using them in a product.
How to interpret published results
The Grounding DINO authors report 52.5 AP on the COCO zero-shot transfer benchmark without training data from COCO, and a mean 26.1 AP on the ODinW zero-shot benchmark. These are results from defined research evaluations reported in the paper, which was submitted in 2023 and revised in 2024. They are not predictions of accuracy on a particular image, a guarantee across domains, or independent tests of Hautechai’s hosted execution.
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