Search Results for "batch image processor"
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Showing 12 open source projects for "batch image processor"
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GenAI Processors
GenAI Processors is a lightweight Python library
...The library offers built-in processors for classic turn-based Gemini calls as well as Live API streaming, so you can mix “batch” and real-time interactions in the same graph. It leans on Python’s asyncio to coordinate concurrency, handle network I/O, and juggle background compute threads without blocking. -
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OpenAI Go
The official Go library for the OpenAI API
...It enables developers to integrate OpenAI’s models and features into Go applications with a clean and idiomatic interface. The library provides support for a wide range of API endpoints including chat completions, assistants, embeddings, image generation, audio processing, and batch jobs. It includes built-in tools for handling authentication, managing API requests, and parsing structured responses. The repository also offers examples to help developers quickly set up projects and test different API calls. Designed for reliability and ease of use, it is maintained to stay aligned with the evolving OpenAI API specifications. -
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Cookbook (Google Gemini)
Examples and guides for using the Gemini API
...The repository covers a wide range of Gemini capabilities, including text, images, video, speech, robotics, and multimodal interactions. It highlights newly introduced features such as Gemini 2.5 models (Flash and Pro), Gemini’s native image generation, Veo for video generation, robotics-focused reasoning models, and Lyria for TTS and music generation. The Cookbook also includes tutorials on advanced API workflows such as grounding answers with external tools, batch-mode request handling, and live multimodal interactivity with LiveAPI. Designed as a hands-on resource, it helps developers quickly explore Gemini’s potential while serving as a reference for integrating cutting-edge multimodal AI into applications. -
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Imagen - Pytorch
Implementation of Imagen, Google's Text-to-Image Neural Network
Implementation of Imagen, Google's Text-to-Image Neural Network that beats DALL-E2, in Pytorch. It is the new SOTA for text-to-image synthesis. Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pre-trained T5 model (attention network). It also contains dynamic clipping for improved classifier-free guidance, noise level conditioning, and a memory-efficient unit design. It appears neither CLIP nor prior... -
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PyTorch Geometric
Geometric deep learning extension library for PyTorch
...These packages come with their own CPU and GPU kernel implementations based on C++/CUDA extensions. We do not recommend installation as root user on your system python. Please setup an Anaconda/Miniconda environment or create a Docker image. We provide pip wheels for all major OS/PyTorch/CUDA combinations. -
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Image Binder
Image Binder - Images-To-PDF Conversion
Introduction Welcome to Image Binder, a versatile application crafted for effortlessly converting and binding multiple images into a single PDF document. Whether you're organizing your image collection or creating professional portfolios, this tool provides a seamless solution with features designed to enhance your experience. -
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iJEPA
Official codebase for I-JEPA
i-JEPA (Image Joint-Embedding Predictive Architecture) is a self-supervised learning framework that predicts missing high-level representations rather than reconstructing pixels. A context encoder sees visible regions of an image and predicts target embeddings for masked regions produced by a slowly updated target encoder, focusing learning on semantics instead of texture. -
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QOR
Libraries written in Go abstracting features needed for business apps
QOR is architected from the ground up to accelerate development and deployment of Content Management Systems, E-commerce Systems, and Business Applications. QOR is comprised of modules that abstract common features for such systems, such as a configurable and flexible back office, a content publishing system, a media library, and much more. We built QOR at The Plant to accelerate development for our clients, it is at the core of our engineering strategy, and we maintain it on an ongoing... -
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YOLOV3 Pytorch
This is a source code for yolo3-pytorch
...The repository provides a complete workflow for users who want to train their own object detector with VOC-style data or use pretrained weights. It includes utilities for annotation conversion, anchor generation, image prediction, video prediction, batch prediction, FPS measurement, heatmap output, and mAP evaluation. The project added multi-GPU training, target count statistics, learning rate scheduling with step and cosine options, and optimizer selection between Adam and SGD. It also includes adaptive learning rate adjustment based on batch size, image cropping, many configurable parameters, and expanded comments for easier study. ... -
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YOLOV4 Pytorch
This is a source code for YoloV4-pytorch that can be used to train you
...The repository is structured around practical workflows, including training, prediction, evaluation, anchor generation, model configuration, and dataset annotation utilities. It supports VOC-style datasets and includes scripts for prediction, mAP evaluation, FPS testing, video prediction, batch prediction, and heatmap generation. The project added multi-GPU training, seed settings for reproducible results, adaptive learning rate behavior based on batch size, and both step and cosine learning rate schedules. It also supports Adam and SGD optimizer choices, image cropping, adjustable parameters, and extensive code comments. ... -
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Faster-Rcnn
This is a pytorch implementation library of faster-rcnn
...The project also includes learning rate scheduling through step and cosine methods, optimizer choices between Adam and SGD, adaptive learning rate behavior based on batch size, image cropping, FPS testing, video prediction, and batch prediction. It is a practical reference for users who want a more classical two-stage detector workflow in PyTorch. -
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emojipacks
CLI to bulk upload emojis to your Slack
CLI to bulk upload emojis to your Slack! Note you must have node and npm installed. If you don't, go to nodejs.org and follow the install instructions there. A file named batchUploadExample.sh is provided for your reference. Edit this file change the subdomain, email, and password parameters to your own and save it as batchUpload.sh or something similar. It optionally takes an argument for the directory path that contains your yaml files. If this is not provided './packs' will be used by...
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