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What Grounding DINO does
Grounding DINO is an open-set object detector: rather than requiring a fixed list of categories, it uses language to identify objects described by a person. The authors describe combining the Transformer-based DINO detector with grounded pre-training so it can detect objects specified by category names or referring expressions. In practical terms, provide an image and words describing what to locate, then inspect the predicted boxes and phrase labels. The Grounding DINO paper explains the model; the official implementation documents a runnable workflow.
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“Open-set” and “zero-shot” describe the approach, not a promise that every phrase will be found correctly. Results depend on the image, wording, and evaluation setting; the paper’s benchmark scores are not guarantees for a particular image or for Hautechai’s hosted build.
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Hautechai’s Replicate model page describes the offering as “Grounding DINO: zero-shot text-prompted object detection (SwinT-OGC). H100 build.” It is a hosted way to try this particular build without first setting up the official repository locally.
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As listed on October 7, 2026, Replicate showed an approximate cost of $0.058 per run, about 17 runs per US dollar, and a typical prediction time of 38 seconds. These are provider estimates, not fixed prices or guaranteed runtimes; Replicate says both cost and prediction time vary significantly with inputs. Check the live listing for current availability, hardware details, pricing, and timing.
The page also states that the model is open source and can run on a computer with Docker, but says, “This model doesn’t have a readme.” Its exact API documentation could not be confirmed from the available listing. Consequently, do not assume field names, defaults, or response format from the official repository—or from a different Replicate deployment—apply to Hautechai’s API.
How to write a useful text prompt
Start with a clear description of the object or objects visible in the image. The official repository says its implementation predicts boxes and phrase labels from an image-and-text pair and suggests separating category names with periods. For example, its README uses the form chair . person . dog .. A referring expression can be more specific than a category name when the distinction matters, such as describing the object by its visible role or attributes.
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Prompt wording is part of the input, not a guarantee of detection. If the model misses an object or returns an unexpected box, try a clearer phrase and inspect the result rather than assuming the label alone fully determines what the detector will find.
What thresholds do in the official implementation
The official code describes two filters. box_threshold filters candidate boxes according to their highest similarity, while text_threshold filters words used as predicted labels. Raising a threshold can make selections more restrictive, but may also exclude weaker detections. The official README’s example values—0.35 for the box threshold and 0.25 for the text threshold—are example settings, not universal recommendations.
These parameter names and behaviors are documented for the official implementation. Hautechai’s Replicate page does not establish that its hosted interface exposes the same controls or uses the same defaults.
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Hosted Replicate or local installation?
| Consideration | Hautechai on Replicate | Official repository |
|---|---|---|
| Setup | Hosted inference through the listing; exact API inputs are not confirmed on the page. | Clone the project, install dependencies, download the SwinT-OGC pretrained weights, then run an image inference demo, as described in the README. |
| Cost | Replicate listed approximate per-run pricing on October 7, 2026; the amount can vary with inputs and may change. | Uses your own computing resources and associated operating costs; the repository does not state a universal cost. |
| Hardware and speed | The listing identifies this build as H100-backed and gives a variable typical-time estimate. | The repository includes a CPU-only mode. It does not establish a universal hardware requirement for practical speed. |
| Control | Convenient hosted route, subject to the hosted interface and its documented options. | Direct access to the official implementation and its documented workflow. |
Choose the hosted listing if you want to try this build without managing the local environment and are comfortable checking its current per-run terms. Choose the repository if you need direct access to implementation details or want to manage inference locally. The official repository is identified as Apache-2.0; review the applicable license and any pretrained-weight or asset terms directly before using them in a product.
What the published benchmark numbers mean
The Grounding DINO authors report 52.5 AP on the COCO zero-shot transfer benchmark without training data from COCO, and mean 26.1 AP on the ODinW zero-shot benchmark. These are results from the evaluation settings described in the paper, submitted in 2023 and revised in 2024. They provide context for the research implementation, but do not predict performance on every image, domain, prompt, or hosted execution. They are not independent tests of Hautechai’s Replicate build. See the paper and its evaluation details for the scope of those results.
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