Explore APISR for Stunning Real-World Super-Resolution with A100 GPU
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by Shaoni Mukherjee
4d ago
In recent years, real-world anime super-resolution (SR) has gained significant popularity. However, many current methodologies rely heavily on techniques developed for photorealistic images. These techniques may not be the best fit for anime content because anime has distinct characteristics, such as hand-drawn lines, vibrant colors, and unique stylistic elements. As a result, these photorealistic-based methods may not fully leverage or accommodate the specific attributes and nuances of anime production, potentially leading to suboptimal results when applied to anime images. This paper takes ..read more
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Run Qwen2: The next gen of LLMs on an NVIDIA A5000 Cloud Server
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by Shaoni Mukherjee
6d ago
Welcome to our tutorial on running Qwen2:7b with Ollama. In this guide, we'll use one of our favorite GPUs, A5000, offered by Paperspace. A5000, powered by NVIDIA and built on ampere architecture, is a powerful GPU known to enhance the performance of rendering, graphics, AI, and computing workloads. A5000 offers 8192 CUDA cores and 24 GB of GDDR6 memory, providing exceptional computational power and memory bandwidth. The A5000 supports advanced features like real-time ray tracing, AI-enhanced workflows, and NVIDIA's CUDA and Tensor cores for accelerated performance. With its robust capabiliti ..read more
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Prompting with DSPy: A New Approach
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by Shaoni Mukherjee
1w ago
The era has come where we are always working on better ways to use and combine language models (LMs). Usually, LMs use fixed "prompt templates" made by trial and error. DSPy is a new method that simplifies this by turning LM pipelines into easy-to-manage text transformation graphs. These graphs use modules that can learn and improve how they prompt, fine-tune, and reason. DSPy includes a tool that optimizes these pipelines for better performance. Studies show that DSPy can quickly create effective LM pipelines, improving performance significantly over traditional methods. It also makes smalle ..read more
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Grounding DINO 1.5: Pushing the Boundaries of Open-Set Object Detection
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by Shaoni Mukherjee
2w ago
In recent years, zero-shot object detection has become a cornerstone of advancements in computer vision. Creating versatile and efficient detectors has been a significant focus on building real-world applications. The introduction of Grounding DINO 1.5 by IDEA Research marks a significant leap forward in this field, particularly in open-set object detection. We will run the demo using Paperspace GPUs, a platform known for offering high-performance computing resources for various applications. These GPUs are designed to meet the needs of machine learning, deep learning, and data analysis and ..read more
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Managing your app's Deployment costs efficiently on Paperspace
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by James Skelton
2w ago
Creating and deploying Deep Learning model serving applications is one of the best and most frequent use cases for Paperspace customers. Managing and running these with Deployment's makes it simpler and faster to spin up your apps than anywhere else on the web. We've talked extensively here on the Paperspace Blog about the utility of the Deployment's product, and we recommend reading more about it here. In this article, we are going to cover all the facets of application maintenance with Paperspace Deployment's. Readers can expect to finish this article with a greater understanding of the fac ..read more
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YOLOv10: Advanced Real-Time End-to-End Object Detection
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by Shaoni Mukherjee
2w ago
Real-time object detection has found modern applications in everything from autonomous vehicles and surveillance systems to augmented reality and robotics. The essence of real-time object detection lies in accurately identifying and classifying multiple objects within an image or a video frame in a fraction of a second. Over the years, numerous algorithms have been developed to enhance the efficiency and accuracy of real-time object detection. The "You Only Look Once" (YOLO) series emerged as a prominent approach due to its speed and performance. The YOLO algorithm revolutionized object dete ..read more
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Finetune Multimodel LLM:IDEFICS 9B using A100
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by Shaoni Mukherjee
3w ago
In this article, we will learn how to run inference using the quantized version of IDEFICS and fine-tune IDEFICS-9b using Paperspace A100 GPU. We will also fine-tune IDEFICS 9B, a variant of the innovative visual language model. This fine-tuning process involves techniques like LoRa, which are specifically designed to enhance the model's performance in certain areas. The A100 GPUs on Paperspace are powerful and versatile tools designed for high-performance computing tasks. Leveraging NVIDIA's Ampere architecture, these GPUs offer exceptional processing power, making them ideal for AI, machine ..read more
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Evaluating the Necessity of Mamba Mechanisms in Visual Recognition Tasks-MambaOut
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by Shaoni Mukherjee
3w ago
Transformers are the backbones to power-up models like BERT, the GPT series, and ViT. However, its attention mechanism has quadratic complexity, making it challenging for long sequences. To tackle this, various token mixers with linear complexity have been developed. Recently, RNN-based models have gained attention for their efficient training and inference on long sequences and have shown promise as backbones for large language models. Inspired by these capabilities, researchers have explored using Mamba in visual recognition tasks, leading to models like Vision Mamba, VMamba, LocalMamba, an ..read more
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IDEFICS2: Multimodal Language Models for the Future
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by Shaoni Mukherjee
1M ago
Introduction IDEFICS (Image-aware Decoder Enhanced à la Flamingo with Interleaved Cross-attentionS) is an open-access version of Deepmind's visual language model, Flamingo. It processes sequences of images and text, producing text outputs, and can answer questions about images, describe visual content, and create stories based on images. Built with publicly available data and models, IDEFICS matches the original Flamingo on various benchmarks like visual question answering, image captioning, and image classification. Available in two sizes, 80 billion and 9 billion parameters, it also comes i ..read more
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PaliGemma: A lightweight open vision-language model (VLM)
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by Shaoni Mukherjee
1M ago
Google recently introduced a new light weight vision-model PaliGemma. This model was released on the 14 May 2024 and has multimodal capabilities. A vision-language model (VLM) is an advanced type of artificial intelligence that integrates visual and textual data to perform tasks that require understanding and generating both images and language. These models combine techniques from computer vision and natural language processing, enabling them to analyze images, generate descriptive captions, answer questions about visual content, and even engage in complex visual reasoning. VLMs can und ..read more
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