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.

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

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.

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.

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.

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.

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.

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 by Ranftl et al. (2021). DPT uses the Vision Transformer (ViT) as backbone and adds a neck + head on top for monocular depth estimation. This repository hosts the ""hybrid"" version of the model as stated in the paper. DPT-Hybrid diverges from DPT by using ViT-hybrid as a backbone and taking some activations from the backbone.

CMFNet achieves competitive performance on three tasks: image deblurring, image dehazing and image deraindrop.

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.

CMFNet achieves competitive performance on three tasks: image deblurring, image dehazing and image deraindrop.

S2T is an end-to-end sequence-to-sequence transformer model. It is trained with standard autoregressive cross-entropy loss and generates the transcripts autoregressively.

Image Generator Using SSD 1B is a powerful deep learning model specifically designed for image synthesis and generation.

The task is a powerful tool that generates anime-style images based on text descriptions or prompts.

Longformer uses a combination of a sliding window (local) attention and global attention. Global attention is user-configured based on the task to allow the model to learn task-specific representations.

The Text-to-Sound task involves converting written text into audible speech. It is a technology that utilizes natural language processing and speech synthesis techniques to transform written words into a spoken form. This task plays a crucial role in various applications, such as text-to-speech (TTS) systems, accessibility tools for visually impaired individuals, voice assistants, and automated voice response systems.