Simbrams
Models by this creator
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segformer-b5-finetuned-ade-640-640
345
The segformer-b5-finetuned-ade-640-640 is a powerful image segmentation model developed by the maintainer simbrams. This model is built on the SegFormer architecture, which utilizes Transformer-based encoders to capture rich contextual information and achieve state-of-the-art performance on a variety of segmentation tasks. The model has been fine-tuned on the ADE20K dataset, enabling it to segment a wide range of objects and scenes with high accuracy. Compared to similar models like swinir, stable-diffusion, gfpgan, and supir, the segformer-b5-finetuned-ade-640-640 model excels at high-resolution, detailed image segmentation tasks, making it a versatile tool for a wide range of applications. Model inputs and outputs The segformer-b5-finetuned-ade-640-640 model takes a single input image and outputs a segmentation mask, where each pixel in the image is assigned a class label. This allows for the identification and localization of various objects, scenes, and structures within the input image. Inputs image**: The input image to be segmented, in the form of a URI. keep_alive**: A boolean flag that determines whether to keep the model alive after the inference is complete. Outputs Output**: An array of segmentation results, where each item represents a segmented region with its class label and coordinates. Capabilities The segformer-b5-finetuned-ade-640-640 model excels at detailed, high-resolution image segmentation. It can accurately identify and localize a wide range of objects, scenes, and structures within an image, including buildings, vehicles, people, natural landscapes, and more. The model's ability to capture rich contextual information and its fine-tuning on the diverse ADE20K dataset make it a powerful tool for various computer vision applications. What can I use it for? The segformer-b5-finetuned-ade-640-640 model can be utilized in a variety of applications, such as autonomous driving, urban planning, content-aware image editing, and scene understanding. For example, the model could be used to segment satellite or aerial imagery to aid in urban planning and infrastructure development. It could also be integrated into photo editing software to enable intelligent, context-aware image manipulation. Things to try One interesting application of the segformer-b5-finetuned-ade-640-640 model could be to combine it with other image processing and generative models, such as segmind-vega, to enable seamless integration of segmentation into more complex computer vision pipelines. Exploring ways to leverage the model's capabilities in creative or industrial projects could lead to novel and impactful use cases.
Updated 9/17/2024
ri
148
The ri model, created by maintainer simbrams, is a Realistic Inpainting model with ControlNET (M-LSD + SEG). It allows for realistic image inpainting, with the ability to control the inpainting process using a segmentation map. This model can be compared to similar models like controlnet-inpaint-test, sks, controlnet-scribble, and controlnet-seg, which also leverage ControlNET for various image manipulation tasks. Model inputs and outputs The ri model takes in an input image, a mask image, and various parameters to control the inpainting process, such as the number of inference steps, the guidance scale, and the image size. The model then generates an output image with the specified inpainted regions. Inputs Image**: The input image to be inpainted. Mask**: The mask image indicating the regions to be inpainted. Prompt**: A text prompt describing the desired inpainting result. Negative prompt**: A text prompt describing undesired content to be avoided in the inpainting. Strength**: The strength or weight of the inpainting process. Image size**: The desired size of the output image. Guidance scale**: The scale of the text guidance during the inpainting process. Scheduler**: The type of scheduler to use for the diffusion process. Seed**: A seed value for the random number generator, allowing for reproducible results. Debug**: A flag to enable debug mode for the model. Blur mask**: A flag to blur the mask before inpainting. Blur radius**: The radius of the blur applied to the mask. Preserve elements**: A flag to preserve elements during the inpainting process. Outputs Output images**: The inpainted output images. Capabilities The ri model is capable of realistic inpainting, allowing users to remove or modify specific regions of an image while preserving the overall coherence and realism of the result. By leveraging ControlNET and segmentation, the model can be directed to focus on specific elements or areas of the image during the inpainting process. What can I use it for? The ri model can be useful for a variety of applications, such as photo editing, content creation, and digital art. Users can use it to remove unwanted objects, repair damaged images, or even create entirely new scenes by inpainting selected regions. The model's ability to preserve elements and control the inpainting process makes it a powerful tool for creative and professional use cases. Things to try With the ri model, users can experiment with different input prompts, mask shapes, and parameter settings to achieve a wide range of inpainting results. For example, you could try inpainting a person in a landscape, removing distracting elements from a photo, or even creating entirely new scenes by combining multiple inpainting steps. The model's flexibility allows for a high degree of creative exploration and customization.
Updated 9/17/2024
sks
1
The sks model, created by simbrams, is a C++ implementation of a sky segmentation model that can accurately segment skies in outdoor images. This model is built using the U-2-Net architecture, which has proven effective for sky segmentation tasks. While the model does not include the "Density Estimation" feature mentioned in the original paper, it still provides high-quality sky masks that can be further refined through post-processing. Model inputs and outputs The sks model takes an image as input and outputs a segmented sky mask. The input image can be resized and contrast adjusted to optimize the model's performance. Additionally, the model can be configured to keep the inference engine alive for faster subsequent inferences. Inputs Image**: The input image for sky segmentation. Contrast**: An integer value to adjust the contrast of the input image, with a default of 100. Keep Alive**: A boolean flag to keep the model's inference engine alive, with a default of false. Outputs Segmented Sky Mask**: An array of URI strings representing the segmented sky regions in the input image. Capabilities The sks model demonstrates strong sky segmentation capabilities, effectively separating the sky from other elements in outdoor scenes. It performs particularly well in scenes with trees, retaining much more detail in the sky mask compared to the original segmentation. However, the model may struggle with some special cloud textures and can occasionally misclassify building elements as sky. What can I use it for? The sks model can be particularly useful for applications that require accurate sky segmentation, such as image editing, atmospheric studies, or even augmented reality applications. By isolating the sky, users can easily apply various effects, adjustments, or overlays to the sky region without affecting the rest of the image. Things to try One interesting aspect of the sks model is the post-processing step, which can further refine the sky mask to improve its accuracy. You may want to experiment with different post-processing techniques to see how they can enhance the model's performance in various outdoor scenarios. Additionally, the model's speed and efficiency are important factors to consider, especially for real-time applications. The maintainer mentions plans to explore more efficient model architectures, such as a real-time model based on a standard U-Net, to improve the model's inference speed on mobile devices.
Updated 5/3/2024