dpt-hybrid-midas
Maintainer: Intel
64
👁️
| 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
The dpt-hybrid-midas model is a Dense Prediction Transformer (DPT) model trained on 1.4 million images for monocular depth estimation. It was introduced in the paper Vision Transformers for Dense Prediction and builds upon the Vision Transformer (ViT) backbone, adding a neck and head for depth estimation. The "hybrid" version of the model, as stated in the paper, uses the ViT-hybrid as its backbone and takes some activations from the backbone. This model was created and released by Intel.
Model inputs and outputs
Inputs
- Images: The model takes a single image as input, which is preprocessed and encoded into a sequence of patch embeddings.
Outputs
- Depth map: The model outputs a depth map, which is an estimate of the depth or distance of each pixel in the input image from the camera.
Capabilities
The dpt-hybrid-midas model can be used for zero-shot monocular depth estimation, where a single image is used to predict the depth of the scene. This can be useful in a variety of computer vision applications, such as autonomous driving, 3D reconstruction, and augmented reality.
What can I use it for?
You can use the raw dpt-hybrid-midas model for zero-shot monocular depth estimation on your own images. The model hub also provides fine-tuned versions of the model for specific tasks that may be of interest to you.
Things to try
One interesting thing to try with the dpt-hybrid-midas model is to experiment with different types of input images, such as outdoor scenes, indoor spaces, or even synthetic images. The model's performance may vary depending on the characteristics of the input data, and testing it on a diverse set of images can help you understand its strengths and limitations.
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
↗️
dpt-large
158
The dpt-large model, also known as MiDaS 3.0, is a Dense Prediction Transformer (DPT) model trained by Intel on 1.4 million images for monocular depth estimation. The DPT model uses the Vision Transformer (ViT) as its backbone and adds a neck and head on top for the depth estimation task. This model was introduced in the paper Vision Transformers for Dense Prediction by Ranftl et al. (2021). The model card was written in collaboration between the Hugging Face team and Intel. The dpt-large model is similar to other object detection and depth estimation models like the detr-resnet-50 model from Facebook, which also uses a transformer-based architecture for object detection. However, the dpt-large model is specifically focused on the task of monocular depth estimation. Model inputs and outputs Inputs RGB image Outputs Depth estimation map for the input image Capabilities The dpt-large model is capable of performing zero-shot monocular depth estimation on input images. This means you can use the raw pre-trained model to predict depth maps without any fine-tuning. The model has been trained on a large dataset of 1.4 million images, giving it the ability to generalize to a wide variety of scenes and objects. What can I use it for? You can use the dpt-large model for various applications that require monocular depth estimation, such as: 3D scene reconstruction Augmented reality and virtual reality Autonomous driving and robotics Computational photography The model can be fine-tuned on specific datasets or tasks to further improve its performance for your particular use case. You can find fine-tuned versions of the dpt-large model on the Hugging Face model hub. Things to try One interesting thing to try with the dpt-large model is to compare its performance on different types of scenes and objects. For example, you could try depth estimation on indoor scenes, outdoor landscapes, and images with a variety of objects and textures. This can help you understand the model's strengths and limitations, and identify areas where further fine-tuning or model improvements may be beneficial. Another interesting experiment would be to combine the dpt-large model with other computer vision models, such as object detection or semantic segmentation, to create more comprehensive scene understanding pipelines. The depth information provided by the dpt-large model could be a valuable input for these downstream tasks.
Updated Invalid Date
📈
ldm3d
48
The ldm3d model, developed by Intel, is a Latent Diffusion Model for 3D that can generate both image and depth map data from a given text prompt. This allows users to create RGBD images from text prompts. The model was fine-tuned on a dataset of RGB images, depth maps, and captions, and validated through extensive experiments. Intel has also developed an application called DepthFusion, which uses the ldm3d model's img2img pipeline to create immersive and interactive 360-degree-view experiences. The ldm3d model builds on research presented in the LDM3D paper, which was accepted to the IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) in 2023. Intel has also released several new checkpoints for the ldm3d model, including ldm3d-4c with higher quality results, ldm3d-pano for panoramic images, and ldm3d-sr for upscaling. Model inputs and outputs Inputs Text prompt**: The ldm3d model takes a text prompt as input, which is used to generate the RGBD image. Outputs RGBD image**: The model outputs an RGBD (RGB + depth) image that corresponds to the given text prompt. Capabilities The ldm3d model is capable of generating high-quality, interactive 3D content from text prompts. This can be particularly useful for applications in the entertainment and gaming industries, as well as architecture and design. The model's ability to generate depth maps alongside the RGB images allows for the creation of immersive, 360-degree experiences using the DepthFusion application. What can I use it for? The ldm3d model can be used to create a wide range of 3D content, from static images to interactive experiences. Potential use cases include: Game and application development**: Generate 3D assets and environments for games, virtual reality experiences, and other interactive applications. Architectural and design visualization**: Create photorealistic 3D models of buildings, interiors, and landscapes based on textual descriptions. Entertainment and media production**: Develop 3D assets and environments for films, TV shows, and other media productions. Educational and training applications**: Generate 3D models and environments for educational purposes, such as virtual field trips or interactive learning experiences. Things to try One interesting aspect of the ldm3d model is its ability to generate depth information alongside the RGB image. This opens up possibilities for creating more immersive and interactive experiences, such as: Exploring the generated 3D scene from different perspectives using the depth information. Integrating the RGBD output into a virtual reality or augmented reality application for a truly immersive experience. Using the depth information to enable advanced rendering techniques, such as real-time lighting and shadows, for more realistic visuals. Experimenting with different text prompts and exploring the range of 3D content the ldm3d model can generate can help uncover its full potential and inspire new and innovative applications.
Updated Invalid Date
⛏️
ldm3d-pano
43
The ldm3d-pano model is a new checkpoint released by Intel that extends their existing LDM3D-4c model to enable the generation of panoramic RGBD images from text prompts. This model is part of the LDM3D-VR suite of diffusion models introduced in the LDM3D-VR paper, which aims to enable virtual reality content creation from text. The ldm3d-pano model was fine-tuned on a dataset of panoramic RGB and depth images to add this new capability. Model inputs and outputs Inputs Text prompt**: A natural language description that the model uses to generate a corresponding panoramic RGBD image. Outputs RGB image**: A 1024x512 panoramic RGB image generated from the text prompt. Depth image**: A corresponding 1024x512 panoramic depth map generated from the text prompt. Capabilities The ldm3d-pano model can generate high-quality panoramic RGBD images based on textual descriptions. This allows users to create immersive 360-degree content for virtual reality applications such as gaming, architectural visualization, and digital entertainment. The model combines the text-to-image capabilities of Stable Diffusion with depth estimation to produce photorealistic and spatially-aware 3D environments. What can I use it for? The ldm3d-pano model enables the creation of immersive virtual environments from simple text prompts. This can be useful for a variety of applications, such as: Gaming and entertainment**: Generate custom 360-degree backgrounds, environments, and scenes for video games, virtual worlds, and other interactive experiences. Architectural visualization**: Create photorealistic 3D renderings of building interiors and exteriors for design, planning, and client presentations. Real estate and tourism**: Generate 360-degree panoramic views of properties, landmarks, and locations to showcase in virtual tours and online listings. Education and training**: Produce realistic 3D simulations and virtual environments for educational purposes, such as architectural walkthroughs or historical recreations. Things to try When using the ldm3d-pano model, consider experimenting with different levels of detail and complexity in your text prompts. Try adding specific elements like furniture, lighting, or weather conditions to see how they affect the generated output. You can also explore using the model in combination with other tools, such as inpainting or upscaling, to refine and enhance the final panoramic images.
Updated Invalid Date
🧠
dino-vitb16
96
The dino-vitb16 model is a Vision Transformer (ViT) trained using the DINO self-supervised learning method. Like other ViT models, it takes images as input and divides them into a sequence of fixed-size patches, which are then linearly embedded and processed by transformer encoder layers. The DINO training approach allows the model to learn an effective inner representation of images without requiring labeled data, making it a versatile foundation for a variety of downstream tasks. In contrast to the vit-base-patch16-224-in21k and vit-base-patch16-224 models which were pre-trained on ImageNet-21k in a supervised manner, the dino-vitb16 model was trained using the self-supervised DINO approach on a large collection of unlabeled images. This allows it to learn visual features and representations in a more general and open-ended way, without being constrained to the specific classes and labels of ImageNet. The nsfw_image_detection model is another ViT-based model, but one that has been fine-tuned on a specialized task of classifying images as "normal" or "NSFW" (not safe for work). This demonstrates how the general capabilities of ViT models can be adapted to more specific use cases through further training. Model inputs and outputs Inputs Images**: The model takes images as input, which are divided into a sequence of 16x16 pixel patches and linearly embedded. Outputs Image features**: The model outputs a set of feature representations for the input image, which can be used for various downstream tasks like image classification, object detection, and more. Capabilities The dino-vitb16 model is a powerful general-purpose image feature extractor, capable of capturing rich visual representations from input images. Unlike models trained solely on labeled datasets like ImageNet, the DINO training approach allows this model to learn more versatile and transferable visual features. This makes the dino-vitb16 model well-suited for a wide range of computer vision tasks, from image classification and object detection to image retrieval and visual reasoning. The learned representations can be easily fine-tuned or used as features for building more specialized models. What can I use it for? You can use the dino-vitb16 model as a pre-trained feature extractor for your own image-based machine learning projects. By leveraging the model's general-purpose visual representations, you can build and train more sophisticated computer vision systems with less labeled data and computational resources. For example, you could fine-tune the model on a smaller dataset of labeled images to perform image classification, or use the features as input to an object detection or segmentation model. The model could also be used for tasks like image retrieval, where you need to find similar images in a large database. Things to try One interesting aspect of the dino-vitb16 model is its ability to learn visual features in a self-supervised manner, without relying on labeled data. This suggests that the model may be able to generalize well to a variety of visual domains and tasks, not just those seen during pre-training. To explore this, you could try fine-tuning the model on datasets that are very different from the ones used for pre-training, such as medical images, satellite imagery, or even artistic depictions. Observing how the model's performance and learned representations transfer to these new domains could provide valuable insights into the model's underlying capabilities and limitations. Additionally, you could experiment with using the dino-vitb16 model as a feature extractor for multi-modal tasks, such as image-text retrieval or visual question answering. The rich visual representations learned by the model could complement text-based features to enable more powerful and versatile AI systems.
Updated Invalid Date