Prithvi-100M

Maintainer: ibm-nasa-geospatial

Total Score

211

Last updated 5/28/2024

🐍

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

Get summaries of the top AI models delivered straight to your inbox:

Model overview

The Prithvi-100M model is a first-of-its-kind temporal Vision Transformer pre-trained by the IBM and NASA team on contiguous US Harmonised Landsat Sentinel 2 (HLS) data. The model adopts a self-supervised encoder developed with a ViT architecture and Masked AutoEncoder (MAE) learning strategy, with an MSE loss function. The model includes spatial attention across multiple patches and also temporal attention for each patch.

This model can be compared to other similar models like moondream1, which is a 1.6B parameter model built using SigLIP, Phi-1.5 and the LLaVa training dataset, as well as neural-chat-7b-v3-1, a 7B parameter LLM finetuned on the Intel Gaudi 2 processor.

Model inputs and outputs

Inputs

  • The Prithvi-100M model accepts remote sensing data in a video format (B, C, T, H, W), where the temporal dimension (T) is crucial for this application and not present in most other remote sensing models.
  • The model can handle both time series of remote sensing images as well as static imagery with T=1.
  • The input data includes the following bands from the NASA HLS V2 L30 product: Blue, Green, Red, Narrow NIR, SWIR 1, and SWIR 2.

Outputs

  • The model can perform image reconstruction on a set of HLS images from the same location at different time steps.
  • The output can be used for a variety of downstream tasks such as Burn Scars segmentation, Flood Segmentation, and Land Cover Classification.

Capabilities

The Prithvi-100M model's unique capability is its ability to handle temporal remote sensing data, which can benefit a variety of applications in the geospatial domain. By incorporating spatial and temporal attention, the model can learn meaningful representations from time-series imagery, enabling more accurate and robust analysis of land cover changes, disaster events, and other environmental phenomena.

What can I use it for?

The Prithvi-100M model can be used for a range of applications in the remote sensing and geospatial fields. Some potential use cases include:

  • Land Cover Classification: The model can be finetuned on labeled land cover data to perform accurate and efficient classification of different land cover types over time.
  • Burn Scar Mapping: The temporal capabilities of the model can be leveraged to detect and map the extent of burn scars after wildfires, which is crucial for disaster response and mitigation efforts.
  • Flood Monitoring: By analyzing time-series remote sensing data, the model can be used to identify and track the progression of flood events, supporting flood risk assessment and emergency planning.

Things to try

One interesting aspect of the Prithvi-100M model is its ability to handle both static and time-series remote sensing imagery. Researchers and developers could explore how the model's performance varies when applying it to different types of input data, such as comparing its accuracy on single-date versus multi-date land cover classification tasks.

Additionally, the model's finetuning capabilities, as demonstrated by the provided examples for burn scar segmentation, present an opportunity to investigate how the pre-trained model can be further optimized for specific downstream applications. Experimenting with different finetuning strategies and dataset compositions could yield insights into the model's adaptability and versatility.



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

AI model preview image

stable-diffusion

stability-ai

Total Score

108.0K

Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. Developed by Stability AI, it is an impressive AI model that can create stunning visuals from simple text prompts. The model has several versions, with each newer version being trained for longer and producing higher-quality images than the previous ones. The main advantage of Stable Diffusion is its ability to generate highly detailed and realistic images from a wide range of textual descriptions. This makes it a powerful tool for creative applications, allowing users to visualize their ideas and concepts in a photorealistic way. The model has been trained on a large and diverse dataset, enabling it to handle a broad spectrum of subjects and styles. Model inputs and outputs Inputs Prompt**: The text prompt that describes the desired image. This can be a simple description or a more detailed, creative prompt. Seed**: An optional random seed value to control the randomness of the image generation process. Width and Height**: The desired dimensions of the generated image, which must be multiples of 64. Scheduler**: The algorithm used to generate the image, with options like DPMSolverMultistep. Num Outputs**: The number of images to generate (up to 4). Guidance Scale**: The scale for classifier-free guidance, which controls the trade-off between image quality and faithfulness to the input prompt. Negative Prompt**: Text that specifies things the model should avoid including in the generated image. Num Inference Steps**: The number of denoising steps to perform during the image generation process. Outputs Array of image URLs**: The generated images are returned as an array of URLs pointing to the created images. Capabilities Stable Diffusion is capable of generating a wide variety of photorealistic images from text prompts. It can create images of people, animals, landscapes, architecture, and more, with a high level of detail and accuracy. The model is particularly skilled at rendering complex scenes and capturing the essence of the input prompt. One of the key strengths of Stable Diffusion is its ability to handle diverse prompts, from simple descriptions to more creative and imaginative ideas. The model can generate images of fantastical creatures, surreal landscapes, and even abstract concepts with impressive results. What can I use it for? Stable Diffusion can be used for a variety of creative applications, such as: Visualizing ideas and concepts for art, design, or storytelling Generating images for use in marketing, advertising, or social media Aiding in the development of games, movies, or other visual media Exploring and experimenting with new ideas and artistic styles The model's versatility and high-quality output make it a valuable tool for anyone looking to bring their ideas to life through visual art. By combining the power of AI with human creativity, Stable Diffusion opens up new possibilities for visual expression and innovation. Things to try One interesting aspect of Stable Diffusion is its ability to generate images with a high level of detail and realism. Users can experiment with prompts that combine specific elements, such as "a steam-powered robot exploring a lush, alien jungle," to see how the model handles complex and imaginative scenes. Additionally, the model's support for different image sizes and resolutions allows users to explore the limits of its capabilities. By generating images at various scales, users can see how the model handles the level of detail and complexity required for different use cases, such as high-resolution artwork or smaller social media graphics. Overall, Stable Diffusion is a powerful and versatile AI model that offers endless possibilities for creative expression and exploration. By experimenting with different prompts, settings, and output formats, users can unlock the full potential of this cutting-edge text-to-image technology.

Read more

Updated Invalid Date

AI model preview image

prismer

nvlabs

Total Score

1

Prismer is a powerful vision-language model developed by the researchers at NVIDIA Labs (NVLABS). It is an ensemble-based model that combines multiple expert models to provide robust and versatile performance across a range of vision-language tasks. Prismer is built upon the principles of the Prismer paper, which introduces a novel approach to leveraging an ensemble of specialized models to enhance the overall capabilities of the system. Similar models like Stable Diffusion, CogVLM, LLaVa-13B, and DeepSeek-VL showcase the growing capabilities of vision-language models in areas such as image generation, multimodal understanding, and real-world applications. Model inputs and outputs Prismer is a versatile model that can handle both visual question answering and image captioning tasks. The model takes in an input image and either a question (for visual question answering) or no additional input (for image captioning). The model's output varies depending on the chosen task, but can include the generated caption, the answer to the visual question, or the expert model labels. Inputs Input Image**: The input image, which can be in .png, .jpg, or .jpeg format. Question** (optional): The question to be answered for the visual question answering task. Use Experts**: A boolean flag to indicate whether the expert models should be used. Output Expert Labels**: A boolean flag to return the output of the individual expert models. Outputs Caption**: The generated caption describing the input image (for the image captioning task). Answer**: The answer to the visual question (for the visual question answering task). Expert Labels**: The output of the individual expert models (if Output Expert Labels is set to true). Capabilities Prismer is a powerful model that can tackle a wide range of vision-language tasks. Its ensemble-based approach allows it to leverage the strengths of multiple specialized models, resulting in robust and versatile performance. The model can accurately caption images, answer visual questions, and provide insights into the internal decision-making process through the expert labels. What can I use it for? Prismer can be used in a variety of applications that require integrating vision and language understanding, such as: Intelligent image search and retrieval Automated image captioning for social media or e-commerce Visual question answering for assistive technologies Multimodal content analysis and understanding Things to try With Prismer, you can experiment with different input images and questions to see how the model responds. Try providing images with varying levels of complexity or ambiguity, and observe how the model's outputs change. You can also explore the expert labels to gain insights into the model's decision-making process and potentially identify areas for further improvement.

Read more

Updated Invalid Date

AI model preview image

blip

salesforce

Total Score

84.4K

BLIP (Bootstrapping Language-Image Pre-training) is a vision-language model developed by Salesforce that can be used for a variety of tasks, including image captioning, visual question answering, and image-text retrieval. The model is pre-trained on a large dataset of image-text pairs and can be fine-tuned for specific tasks. Compared to similar models like blip-vqa-base, blip-image-captioning-large, and blip-image-captioning-base, BLIP is a more general-purpose model that can be used for a wider range of vision-language tasks. Model inputs and outputs BLIP takes in an image and either a caption or a question as input, and generates an output response. The model can be used for both conditional and unconditional image captioning, as well as open-ended visual question answering. Inputs Image**: An image to be processed Caption**: A caption for the image (for image-text matching tasks) Question**: A question about the image (for visual question answering tasks) Outputs Caption**: A generated caption for the input image Answer**: An answer to the input question about the image Capabilities BLIP is capable of generating high-quality captions for images and answering questions about the visual content of images. The model has been shown to achieve state-of-the-art results on a range of vision-language tasks, including image-text retrieval, image captioning, and visual question answering. What can I use it for? You can use BLIP for a variety of applications that involve processing and understanding visual and textual information, such as: Image captioning**: Generate descriptive captions for images, which can be useful for accessibility, image search, and content moderation. Visual question answering**: Answer questions about the content of images, which can be useful for building interactive interfaces and automating customer support. Image-text retrieval**: Find relevant images based on textual queries, or find relevant text based on visual input, which can be useful for building image search engines and content recommendation systems. Things to try One interesting aspect of BLIP is its ability to perform zero-shot video-text retrieval, where the model can directly transfer its understanding of vision-language relationships to the video domain without any additional training. This suggests that the model has learned rich and generalizable representations of visual and textual information that can be applied to a variety of tasks and modalities. Another interesting capability of BLIP is its use of a "bootstrap" approach to pre-training, where the model first generates synthetic captions for web-scraped image-text pairs and then filters out the noisy captions. This allows the model to effectively utilize large-scale web data, which is a common source of supervision for vision-language models, while mitigating the impact of noisy or irrelevant image-text pairs.

Read more

Updated Invalid Date

🌐

moondream2

vikhyatk

Total Score

439

moondream2 is a small vision language model designed to run efficiently on edge devices, created by vikhyatk. It is a more compact version of the earlier moondream1 model, which was a 1.6B parameter model built using SigLIP, Phi-1.5, and the LLaVa training dataset. The moondream2 model has shown steady improvements in performance across various benchmarks, including VQAv2, GQA, TextVQA, and TallyQA. Its latest release from April 2024 achieves 77.7% on VQAv2, 61.7% on GQA, and 49.7% on TextVQA, outpacing the earlier versions of the model. Model inputs and outputs Inputs Image**: The model takes an image as input, which can be encoded and passed to the model for processing. Outputs Question answer**: Given an image and a question, the model can generate an answer to the question based on the visual information in the image. Capabilities moondream2 demonstrates strong capabilities in visual question answering tasks, where it can interpret the content of an image and provide relevant answers to questions about it. The model has shown particular improvements in handling more complex questions that require a deeper understanding of the visual scene, as evidenced by its performance on the GQA and TextVQA benchmarks. What can I use it for? The moondream2 model can be useful for a variety of applications that involve understanding and interpreting visual information, such as: Visual question answering**: The model can be used to build applications that allow users to ask questions about images and receive relevant answers. Image-based assistants**: The model can be integrated into intelligent assistants to provide visual understanding and question-answering capabilities. Educational applications**: The model could be used in educational tools that help students learn by interacting with visual content and answering questions about it. Things to try One interesting aspect of the moondream2 model is its efficiency, which allows it to run on edge devices. This opens up the possibility of developing applications that can leverage the model's visual understanding capabilities on mobile devices or embedded systems, without requiring a constant connection to a powerful cloud-based infrastructure. Developers could explore using moondream2 in applications that need to provide real-time visual analysis and question-answering capabilities, such as augmented reality or computer vision applications for mobile devices.

Read more

Updated Invalid Date