Search Results for "artificial images"
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Showing 6 open source projects for "artificial images"
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SKUDONET ADC, operates at the application layer, efficiently distributing network load and application load across multiple servers. This not only enhances the performance of your application but also ensures that your web servers can handle more traffic seamlessly.
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Drive amazing product experiences with quality product data.
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CLIP-as-service
Embed images and sentences into fixed-length vectors
CLIP-as-service is a low-latency high-scalability service for embedding images and text. It can be easily integrated as a microservice into neural search solutions. Serve CLIP models with TensorRT, ONNX runtime and PyTorch w/o JIT with 800QPS[*]. Non-blocking duplex streaming on requests and responses, designed for large data and long-running tasks. Horizontally scale up and down multiple CLIP models on single GPU, with automatic load balancing. Easy-to-use. No learning curve, minimalist design... -
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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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DocWire SDK
Award-winning modern data processing in C++17/20
DocWire SDK, a standout C++17/20 data processing tool, has received award from SourceForge and strong backing from Microsoft. It handles nearly 100 file types, empowering efficient text extraction, web data extraction, and document analysis. The upcoming integration of C++17 and C++20 will bring advanced functionalities, particularly in areas like HTTP capabilities and web data extraction. For businesses, the shift to DocWire SDK signifies a leap forward. It promises comprehensive document... -
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macara
A converter for seamless transformation of files, data, and media ...
This application consolidates various scripts, including an AI feature (rembg), into a singular platform. The design of this software is evolutionary, allowing for the seamless integration of additional scripts, menus, or windows as needed. Serving as a versatile tool, it facilitates efficient file management, especially when handling a substantial volume of images, whether sorting by name or other attributes. These scripts are crafted to complement generative art AI technologies like Dall-e... -
Save time, money and your sanity. Deluxe Payment Exchange+ (DPX+) is our integrated payments solution that streamlines and automates your accounts payable (AP) disbursements. DPX+ ensures secure payments and offers suppliers alternate ways to receive funds, including mailed checks, ACH, virtual credit cards, debit cards, or eCheck payments. By simply integrating with your existing accounting software like QuickBooks®, you’ll implement efficient payment solutions for AP with ease—without costly development fees or untimely delays.
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5
licas
Framework for intelligent service-based networks. Mobile compatible.
... and a problem solver provides a range of algorithms. These can also be used separately, but advanced programming and configuration is required. A free All-in-One GUI allows you to manage services and function, and add your own modules. Example Apps are included and the images illustrate the functionality. The open source p2p server requires only the HTTP address and optional passwords. Small jar file sizes and mobile compatible. -
6
Age and Gender Estimation
Keras implementation of a CNN network for age and gender estimation
Keras implementation of a CNN network for age and gender estimation. This is a Keras implementation of a CNN for estimating age and gender from a face image [1, 2]. In training, the IMDB-WIKI dataset is used. Because the face images in the UTKFace dataset is tightly cropped (there is no margin around the face region), faces should also be cropped in demo.py if weights trained by the UTKFace dataset is used. Please set the margin argument to 0 for tight cropping. You can evaluate a trained model...
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