GroundingDINO
Maintainer: ShilongLiu
110
🤷
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model Overview
GroundingDINO is a novel object detection model developed by Shilong Liu and colleagues. It combines the power of the DINO self-supervised vision transformer with grounded pre-training on image-text pairs, enabling it to perform open-set zero-shot object detection. In contrast to traditional object detectors that require extensive annotated training data, GroundingDINO can detect objects in images using only natural language descriptions, without needing any bounding box labels.
The model was trained on a large-scale dataset of image-text pairs, allowing it to ground visual concepts to their linguistic representations. This enables GroundingDINO to recognize a diverse set of object categories beyond the typical closed-set of a standard object detector. The paper demonstrates the model's impressive performance on a variety of benchmarks, outperforming prior zero-shot and few-shot object detectors.
Model Inputs and Outputs
Inputs
- Natural language text: A free-form text description of the objects to be detected in an image.
Outputs
- Bounding boxes: The model outputs a set of bounding boxes corresponding to the detected objects, along with their class labels.
- Confidence scores: Each detected object is associated with a confidence score indicating the model's certainty of the detection.
Capabilities
GroundingDINO exhibits strong zero-shot object detection capabilities, allowing it to identify a wide variety of objects in images using only text descriptions, without requiring any bounding box annotations. The model achieves state-of-the-art performance on several open-set detection benchmarks, demonstrating its ability to generalize beyond a fixed set of categories.
One compelling example showcased in the paper is GroundingDINO's ability to detect unusual objects like "a person riding a unicycle" or "a dog wearing sunglasses." These types of compositional and unconventional object descriptions highlight the model's flexibility and understanding of complex visual concepts.
What can I use it for?
GroundingDINO has numerous potential applications in computer vision and multimedia understanding. Its zero-shot capabilities make it well-suited for tasks like:
- Image and video annotation: Automatically generating detailed textual descriptions of the contents of images and videos, without the need for extensive manual labeling.
- Robotic perception: Allowing robots to recognize and interact with a wide range of objects in unstructured environments, using natural language commands.
- Intelligent assistants: Powering AI assistants that can understand and respond to queries about the visual world using grounded language understanding.
The maintainer, ShilongLiu, has also provided a Colab demo to showcase the model's capabilities.
Things to try
One interesting aspect of GroundingDINO is its ability to detect objects using complex, compositional language descriptions. Try experimenting with prompts that combine multiple attributes or describe unusual object configurations, such as "a person riding a unicycle" or "a dog wearing sunglasses." Observe how the model's performance compares to more straightforward object descriptions.
Additionally, you could explore the model's ability to generalize to new, unseen object categories by trying prompts for objects that are not part of the standard object detection datasets. This can help uncover the model's true open-set capabilities and understanding of visual concepts.
Overall, GroundingDINO represents an exciting advancement in the field of object detection, showcasing the potential of language-guided vision models to tackle complex and open-ended visual understanding tasks.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🗣️
grounding-dino-base
46
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.
Updated Invalid Date
grounding-dino
132
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.
Updated Invalid Date
sdxl-lightning-4step
412.2K
sdxl-lightning-4step is a fast text-to-image model developed by ByteDance that can generate high-quality images in just 4 steps. It is similar to other fast diffusion models like AnimateDiff-Lightning and Instant-ID MultiControlNet, which also aim to speed up the image generation process. Unlike the original Stable Diffusion model, these fast models sacrifice some flexibility and control to achieve faster generation times. Model inputs and outputs The sdxl-lightning-4step model takes in a text prompt and various parameters to control the output image, such as the width, height, number of images, and guidance scale. The model can output up to 4 images at a time, with a recommended image size of 1024x1024 or 1280x1280 pixels. Inputs Prompt**: The text prompt describing the desired image Negative prompt**: A prompt that describes what the model should not generate Width**: The width of the output image Height**: The height of the output image Num outputs**: The number of images to generate (up to 4) Scheduler**: The algorithm used to sample the latent space Guidance scale**: The scale for classifier-free guidance, which controls the trade-off between fidelity to the prompt and sample diversity Num inference steps**: The number of denoising steps, with 4 recommended for best results Seed**: A random seed to control the output image Outputs Image(s)**: One or more images generated based on the input prompt and parameters Capabilities The sdxl-lightning-4step model is capable of generating a wide variety of images based on text prompts, from realistic scenes to imaginative and creative compositions. The model's 4-step generation process allows it to produce high-quality results quickly, making it suitable for applications that require fast image generation. What can I use it for? The sdxl-lightning-4step model could be useful for applications that need to generate images in real-time, such as video game asset generation, interactive storytelling, or augmented reality experiences. Businesses could also use the model to quickly generate product visualization, marketing imagery, or custom artwork based on client prompts. Creatives may find the model helpful for ideation, concept development, or rapid prototyping. Things to try One interesting thing to try with the sdxl-lightning-4step model is to experiment with the guidance scale parameter. By adjusting the guidance scale, you can control the balance between fidelity to the prompt and diversity of the output. Lower guidance scales may result in more unexpected and imaginative images, while higher scales will produce outputs that are closer to the specified prompt.
Updated Invalid Date
grounded_sam
606
grounded_sam is an AI model that combines the strengths of Grounding DINO and Segment Anything to provide a powerful pipeline for solving complex masking problems. Grounding DINO is a strong zero-shot object detector that can generate high-quality bounding boxes and labels from free-form text, while Segment Anything is an advanced segmentation model that can generate masks for all objects in an image. This project adds the ability to prompt multiple masks and combine them, as well as to subtract negative masks for fine-grained control. Model inputs and outputs grounded_sam takes an image, a positive mask prompt, a negative mask prompt, and an adjustment factor as inputs. It then generates a set of masks that match the provided prompts. The positive prompt is used to identify the objects or regions of interest, while the negative prompt is used to exclude certain areas from the mask. The adjustment factor can be used to dilate or erode the masks. Inputs Image**: The input image to be masked. Mask Prompt**: The text prompt used to identify the objects or regions of interest. Negative Mask Prompt**: The text prompt used to exclude certain areas from the mask. Adjustment Factor**: An integer value that can be used to dilate (+) or erode (-) the generated masks. Outputs Masks**: An array of image URIs representing the generated masks. Capabilities grounded_sam is a powerful tool for programmed inpainting and selective masking. It can be used to precisely target and mask specific objects or regions in an image based on text prompts, while also excluding unwanted areas. This makes it useful for tasks like image editing, content creation, and data annotation. What can I use it for? grounded_sam can be used for a variety of applications, such as: Image Editing**: Precisely mask and modify specific elements in an image, such as removing objects, replacing backgrounds, or adjusting the appearance of specific regions. Content Creation**: Generate custom masks for use in digital art, compositing, or other creative projects. Data Annotation**: Automate the process of annotating images for tasks like object detection, instance segmentation, and more. Things to try One interesting thing to try with grounded_sam is using it to create masks for programmed inpainting. By combining the positive and negative prompts, you can precisely target the areas you want to keep or remove, and then use the adjustment factor to fine-tune the masks as needed. This can be a powerful tool for tasks like object removal, image restoration, or content-aware fill.
Updated Invalid Date