Models
Color Extraction is a task in computer vision that involves the extraction and analysis of colors from images or videos. The objective of this task is to identify and isolate specific colors or color ranges present in the visual data.
Background Removal is an image processing technique used to separate the main object from the background of a photo. Removing the background helps highlight the product, subject, or character, bringing a professional and aesthetically pleasing look to the image.
The goal of Image to Anime was to create a new version of the image that would possess the same clean lines and evoke the characteristic feel found in anime productions, capturing the unique artistry and aesthetics associated with this style.
Anime backgrounds, also known as anime backgrounds art or anime scenery, refer to the visual elements that form the backdrop of animated scenes in anime. These backgrounds are carefully designed and illustrated to provide the setting, atmosphere, and context for the characters and events within the anime.
ZeroShot Image Classification CLIP is a task in the field of machine learning and image processing, aiming to predict the class or label of an image that has not been previously classified, in a dataset that the model has not been trained on with those classes.
My chatbot model is developed based on SmolLM2-135M-Instruct, with the goal of creating a lightweight, fast and intelligent conversational assistant. By fine-tuning on specialized data, this chatbot is able to understand context better, respond more accurately and be more suitable for each specific use case.
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My chatbot model is developed based on SmolLM2-135M-Instruct, with the goal of creating a lightweight, fast and intelligent conversational assistant. By fine-tuning on specialized data, this chatbot is able to understand context better, respond more accurately and be more suitable for each specific use case.
by @AIOZNetwork

Artwork Image Generator is an artificial intelligence model designed to generate artistic images in various styles.
by @AIOZNetwork

The ViT-S model (Vision Transformer-Small) is a variant of the Vision Transformer architecture, which applies the Transformer model to image recognition tasks. SimPool, short for "Simplified Pooling," is a pooling method designed to aggregate information from the ViT-S model s attention maps and produce a fixed-size representation for downstream tasks.
by @AIOZNetwork

ZeroShot Image Classification CLIP is a task in the field of machine learning and image processing, aiming to predict the class or label of an image that has not been previously classified, in a dataset that the model has not been trained on with those classes.
by @AIOZNetwork

Image Restoration is a compute vision task which restoring from the degraded images to clean images.
by @AIOZNetwork

Prompt Extend is an innovative approach that aims to enhance the capabilities of language models and improve their response generation. It involves extending the initial prompt or query by providing additional context or specifications to guide the model's understanding and generate more accurate and relevant responses.
by @AIOZNetwork


Jak's Woolitize Image Generator
Jak's Woolitize Image Generator is a text to image task that focuses on applying a woolitize texture and appearance to generated images, creating images that convey warmth.
by @AIOZNetwork

The goal of Image to Anime was to create a new version of the image that would possess the same clean lines and evoke the characteristic feel found in anime productions, capturing the unique artistry and aesthetics associated with this style.
by @AIOZNetwork

Extract sections of images from your image by using OpenAI's CLIP and YoloSmall.
by @AIOZNetwork

Donut consists of a vision encoder (Swin Transformer) and a text decoder (BART). Given an image, the encoder first encodes the image into a tensor of embeddings (of shape batch_size, seq_len, hidden_size), after which the decoder autoregressively generates text, conditioned on the encoding of the encoder.
by @AIOZNetwork


Image to Text by Pix2Struct
Pix2Struct is an image encoder - text decoder model that is trained on image-text pairs for various tasks, including image captionning and visual question answering.
by @AIOZNetwork


Document Visual Question Answering
Donut model fine-tuned on DocVQA. It was introduced in the paper OCR-free Document Understanding Transformer by Geewok et al. Donut consists of a vision encoder (Swin Transformer) and a text decoder (BART). Given an image, the encoder first encodes the image into a tensor of embeddings (of shape batch_size, seq_len, hidden_size), after which the decoder autoregressively generates text, conditioned on the encoding of the encoder.
by @AIOZNetwork

Dense Prediction for Vision Transformers is a task focused on applying Vision Transformers (ViTs) to dense prediction problems, such as object detection, semantic segmentation, and depth estimation. Unlike traditional image classification tasks, dense prediction involves making predictions for each pixel or region in an image.
by @AIOZNetwork


Image Restoration by Deblur
CMFNet achieves competitive performance on three tasks: image deblurring, image dehazing and image deraindrop.
by @AIOZNetwork

Chest X-rays classification with ViT is not a specific term or model that I am aware of in the context of pneumonia classification using X-ray images. However, I can provide you with a general overview of pneumonia classification using X-ray images and the role of vision transformers (ViTs) in image analysis tasks.
by @AIOZNetwork
