Pablodawson
Models by this creator
segment-anything-automatic
3
The segment-anything-automatic model, created by pablodawson, is a version of the Segment Anything Model (SAM) that can automatically generate segmentation masks for all objects in an image. SAM is a powerful AI model developed by Meta AI Research that can produce high-quality object masks from simple input prompts like points or bounding boxes. Similar models include segment-anything-everything and the official segment-anything model. Model inputs and outputs The segment-anything-automatic model takes an image as its input and automatically generates segmentation masks for all objects in the image. The model supports various input parameters to control the mask generation process, such as the resize width, the number of crop layers, the non-maximum suppression thresholds, and more. Inputs image**: The input image to generate segmentation masks for. resize_width**: The width to resize the image to before running inference (default is 1024). crop_n_layers**: The number of layers to run mask prediction on crops of the image (default is 0). box_nms_thresh**: The box IoU cutoff used by non-maximal suppression to filter duplicate masks (default is 0.7). crop_nms_thresh**: The box IoU cutoff used by non-maximal suppression to filter duplicate masks between different crops (default is 0.7). points_per_side**: The number of points to be sampled along one side of the image (default is 32). pred_iou_thresh**: A filtering threshold between 0 and 1 using the model's predicted mask quality (default is 0.88). crop_overlap_ratio**: The degree to which crops overlap (default is 0.3413333333333333). min_mask_region_area**: The minimum area of a mask region to keep after postprocessing (default is 0). stability_score_offset**: The amount to shift the cutoff when calculating the stability score (default is 1). stability_score_thresh**: A filtering threshold between 0 and 1 using the stability of the mask under changes to the cutoff (default is 0.95). crop_n_points_downscale_factor**: The factor to scale down the number of points-per-side sampled in each layer (default is 1). Outputs Output**: A URI to the generated segmentation masks for the input image. Capabilities The segment-anything-automatic model can automatically generate high-quality segmentation masks for all objects in an image, without requiring any manual input prompts. This makes it a powerful tool for tasks like image analysis, object detection, and image editing. The model's strong zero-shot performance allows it to work well on a variety of image types and scenes. What can I use it for? The segment-anything-automatic model can be used for a wide range of applications, including: Image analysis**: Automatically detect and segment all objects in an image for further analysis. Object detection**: Use the generated masks to identify and locate specific objects within an image. Image editing**: Leverage the precise segmentation masks to selectively modify or remove objects in an image. Automation**: Integrate the model into image processing pipelines to automate repetitive segmentation tasks. Things to try Some interesting things to try with the segment-anything-automatic model include: Experiment with the various input parameters to see how they affect the generated masks, and find the optimal settings for your specific use case. Combine the segmentation masks with other computer vision techniques, such as object classification or instance segmentation, to build more advanced image processing applications. Explore using the model for creative applications, such as image compositing or digital artwork, where the precise segmentation capabilities can be valuable. Compare the performance of the segment-anything-automatic model to similar models, such as segment-anything-everything or the official segment-anything model, to find the best fit for your needs.
Updated 5/10/2024
panohead
1
panohead is a geometry-aware 3D full-head synthesis model that can generate 360-degree head images. It was created by pablodawson. This model is similar to other panoramic and face restoration models like sdxl-panoramic, sdxl-panorama, gfpgan, and stable-diffusion. Model inputs and outputs The panohead model takes a single input - a face image. It then generates a 360-degree 3D mesh of the full head in the PLY format. This allows for the creation of immersive, geometry-aware head models that can be used in various applications. Inputs Face Image**: The input face image to be used for generating the 3D head model. Outputs 3D Head Model**: The generated 3D mesh of the full head in the PLY format. Capabilities The panohead model is capable of generating realistic and detailed 3D head models from a single face image. The generated models capture the geometry and shape of the head, allowing for 360-degree visualization and interaction. What can I use it for? The panohead model can be used for a variety of applications, such as virtual avatars, immersive experiences, and 3D facial modeling. The generated 3D head models can be integrated into game engines, virtual reality experiences, or used for advanced facial animation and analysis. Things to try One interesting thing to try with the panohead model is to experiment with different input face images and observe how the generated 3D head models vary in terms of geometry, detail, and realism. You can also try integrating the generated PLY files into 3D software or game engines to create more immersive and interactive experiences.
Updated 5/10/2024