# grounding-dino-base by IDEA-Research: Open-Weight Model Source: https://savrn.com/models/grounding-dino-base Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve grounding-dino-base (233M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 0.5 GB | 0.6 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.2 GB | 0.3 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 25, 2026. [grounding-dino-base on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/grounding-dino-base/gpus) ## SAVRN's Notes on grounding-dino-base Ask a camera for the pallet jack near the loading door without training a pallet-jack detector, and you are describing what grounding-dino-base does. IDEA-Research attached a text encoder to a closed-set detector, so the classes come from the prompt instead of a fixed label list. It carries 233 million parameters stored in float32, yet at 16-bit it needs 0.6 GB, 0.3 GB at 8-bit and 0.1 GB at 4-bit. Against the cheapest Index listing, one MI300X with 192 GB at $1.85 an hour on-demand, that is a rounding error, so plan for many copies per card. Apache 2.0 with commercial use permitted lets you ship it inside a product, modify it and redistribute it, provided the license and NOTICE files stay and significant changes are stated. No context length to size; the prompt is a short string. Released September 25, 2023, last updated May 12, 2024, method in arXiv:2303.05499. SAVRN Research, 2026-09-18 ## Model Card By IDEA-Research, published under apache-2.0, revision 12bdfa3120f3. ### Grounding DINO model (base variant) The Grounding DINO model was proposed in [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://savrn.com/papers/grounding-dino-marrying-dino-with-grounded-pre-training-for-open-set-object-detection) by Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang. Grounding DINO extends a closed-set object detection model with a text encoder, enabling open-set object detection. The model achieves remarkable results, such as 52.5 AP on COCO zero-shot. Grounding DINO overview. Taken from the [original paper](https://savrn.com/papers/grounding-dino-marrying-dino-with-grounded-pre-training-for-open-set-object-detection). ### Intended uses & limitations You can use the raw model for zero-shot object detection (the task of detecting things in an image out-of-the-box without labeled data). #### How to use Here's how to use the model for zero-shot object detection: [Read the full model card (259 words)](https://savrn.com/models/grounding-dino-base/card) ## Configuration Architecture GroundingDinoForObjectDetection Stored precision float32 Model type grounding-dino ## Identity and Version Repository IDEA-Research/grounding-dino-base Publisher IDEA-Research Task Zero shot object detection Modality Other Library transformers Parameters 233M parameters Languages Not stated by the source Revision 12bdfa3120f3e7ec7b434d90674b3396eccf88eb First published 2023-09-25 Last updated 2024-05-12 ## Files and Weights 10 files, 1.9 GB in total. The weights are 2 files totalling 1.9 GB in bin, safetensors. Weights2 files · 1.9 GB Configuration3 files · 2.3 KB Tokenizer3 files · 944.1 KB Documentation1 file · 2.6 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 933.4 MB | 5548f844c928 | | pytorch_model.bin | Weights | 936.0 MB | 4b51daf29695 | | config.json | Configuration | 1.7 KB | — | | preprocessor_config.json | Configuration | 457 B | — | | special_tokens_map.json | Configuration | 125 B | — | | README.md | Documentation | 2.6 KB | — | | .gitattributes | Repository | 1.5 KB | — | | tokenizer.json | Tokenizer | 711.4 KB | — | | tokenizer_config.json | Tokenizer | 1.2 KB | — | | vocab.txt | Tokenizer | 231.5 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 1.9 GB [Download from IDEA-Research](https://huggingface.co/IDEA-Research/grounding-dino-base) Released by IDEA-Research through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Described by [arXiv:2303.05499](https://savrn.com/papers/grounding-dino-marrying-dino-with-grounded-pre-training-for-open-set-object-detection) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 1.9 GB | | 16-bit | 0.5 GB | | 8-bit | 0.2 GB | | 4-bit | 0.1 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About grounding-dino-base ### How much GPU memory does grounding-dino-base need? About 0.6 GB at 16-bit and 0.1 GB at 4-bit: the weights (233M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run grounding-dino-base on? At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### Can I use grounding-dino-base commercially? Yes. grounding-dino-base is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors. ## Similar Models Model · Zero shot object detection ### [grounding-dino-tiny](https://savrn.com/models/grounding-dino-tiny) [IDEA-Research](https://savrn.com/model-publishers/idea-research) The Grounding DINO model was proposed in Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection by Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang. Grounding DINO extends a closed-set object detection model with a text encoder, enabling open-set object detection. The model achieves remarkable results, such as 52.5 AP on COCO zero-shot. alt="drawing" width="600"/> You can use the raw model for zero-shot object detection (the task of detecting things in an image out-of-the-box without labeled data). Here's how to use the model for zero-shot object detection Open weights apache-2.0 172M parameters transformers [View model](https://savrn.com/models/grounding-dino-tiny) Model · Zero shot object detection ### [owlv2-base-patch16-ensemble](https://savrn.com/models/owlv2-base-patch16-ensemble) [Google](https://savrn.com/model-publishers/google) The OWLv2 model (short for Open-World Localization) was proposed in Scaling Open-Vocabulary Object Detection by Matthias Minderer, Alexey Gritsenko, Neil Houlsby. OWLv2, like OWL-ViT, is a zero-shot text-conditioned object detection model that can be used to query an image with one or multiple text queries. The model uses CLIP as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features. To use CLIP for detection, OWL-ViT removes the final token pooling layer of the vision model and attaches a lightweight classification and box head to each transformer output token. Open-vocabulary classification is enabled by replacing… Open weights apache-2.0 155M parameters transformers [View model](https://savrn.com/models/owlv2-base-patch16-ensemble) Model · Zero shot object detection ### [rfdetr-textalign-ONNX](https://savrn.com/models/rfdetr-textalign-onnx) [Trung M. Bui](https://savrn.com/model-publishers/mtbui2010) Open-vocabulary detection by adding one trained projection to a closed-set RF-DETR Small detector, served by VisionServe The detector exports the 256-d feature fi of each of its 300 object queries as an extra ONNX output, queryfeats. A trained matrix P maps that feature into the text-embedding space of a CLIP or SigLIP text tower, and each class gets a cosine score against the embedding of its name: The vocabulary is the prompt, for example "cup. cola can. banana.". Changing it needs no re-export and no second detector: the text tower runs once per new vocabulary, and the result is cached. Boxes always come from the detector. pull downloads one subfolder of this repo, writes a… Open weights apache-2.0 onnx [View model](https://savrn.com/models/rfdetr-textalign-onnx) ## IDEA-Research [All models and datasets](https://savrn.com/model-publishers/idea-research) ## Versions - [12bdfa3120f3](https://savrn.com/models/grounding-dino-base/versions/12bdfa3120f3) · current 2026-10-25 ## Explore More - [All zero shot object detection models](https://savrn.com/models/tasks/zero-shot-object-detection) - [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-19. - [Hugging Face record](https://huggingface.co/IDEA-Research/grounding-dino-base) - [How the hub is built](https://savrn.com/model-hub/methodology)