grounding-dino
Maintainer: adirik
132
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
grounding-dino is an AI model that can detect arbitrary objects in images using human text inputs such as category names or referring expressions. It combines a Transformer-based detector called DINO with grounded pre-training to achieve open-vocabulary and text-guided object detection. The model was developed by IDEA Research and is available as a Cog model on Replicate.
Similar models include GroundingDINO, which also uses the Grounding DINO approach, as well as other object detection models like stable-diffusion and text-extract-ocr.
Model inputs and outputs
grounding-dino takes an image and a comma-separated list of text queries describing the objects you want to detect. It then outputs the detected objects with bounding boxes and predicted labels. The model also allows you to adjust the confidence thresholds for the box and text predictions.
Inputs
- image: The input image to query
- query: Comma-separated text queries describing the objects to detect
- box_threshold: Confidence level threshold for object detection
- text_threshold: Confidence level threshold for predicted labels
- show_visualisation: Option to draw and visualize the bounding boxes on the image
Outputs
- Detected objects with bounding boxes and predicted labels
Capabilities
grounding-dino can detect a wide variety of objects in images using just natural language descriptions. This makes it a powerful tool for tasks like content moderation, image retrieval, and visual analysis. The model is particularly adept at handling open-vocabulary detection, allowing you to query for any object, not just a predefined set.
What can I use it for?
You can use grounding-dino for a variety of applications that require object detection, such as:
- Visual search: Quickly find specific objects in large image databases using text queries.
- Automated content moderation: Detect inappropriate or harmful objects in user-generated content.
- Augmented reality: Overlay relevant information on objects in the real world using text-guided object detection.
- Robotic perception: Enable robots to understand and interact with their environment using language-guided object detection.
Things to try
Try experimenting with different types of text queries to see how the model handles various object descriptions. You can also play with the confidence thresholds to balance the precision and recall of the object detections. Additionally, consider integrating grounding-dino into your own applications to add powerful object detection capabilities.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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The grounding-dino-base model, developed by IDEA-Research, is an extension of the closed-set object detection model DINO (Detecting Objects with Noisy Guidance). Grounding DINO adds a text encoder, enabling the model to perform open-set object detection - the ability to detect objects in an image without any labeled data. This model achieves impressive results, such as 52.5 AP on the COCO zero-shot dataset, as detailed in the original paper. Similar models include the GroundingDINO and grounding-dino models, which also focus on zero-shot object detection, as well as the dino-vitb16 and dinov2-base models, which use self-supervised training approaches like DINO and DINOv2 on Vision Transformers. Model inputs and outputs Inputs Images**: The model takes in an image for which it will perform zero-shot object detection. Text**: The model also takes in a text prompt that specifies the objects to detect in the image, such as "a cat. a remote control." Outputs Bounding boxes**: The model outputs bounding boxes around the detected objects in the image, along with corresponding confidence scores. Object labels**: The model also outputs the object labels that correspond to the detected bounding boxes. Capabilities The grounding-dino-base model excels at zero-shot object detection, which means it can detect objects in images without any labeled training data. This is achieved by the model's ability to ground the text prompt to the visual features in the image. The model can detect a wide variety of objects, from common household items to more obscure objects. What can I use it for? You can use the grounding-dino-base model for a variety of applications that require open-set object detection, such as robotic assistants, autonomous vehicles, and image analysis tools. By providing a simple text prompt, the model can quickly identify and localize objects in an image without the need for labeled training data. Things to try One interesting thing to try with the grounding-dino-base model is to experiment with different text prompts. The model's performance can be influenced by the specific wording and phrasing of the prompt, so you can explore how to craft prompts that elicit the desired object detection results. Additionally, you can try combining the model with other computer vision techniques, such as image segmentation or instance recognition, to create more advanced applications.
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