Search Results for "foo_input_dvda.fb2k-component"
Sort By:
Showing 2 open source projects for "foo_input_dvda.fb2k-component"
View related business solutions-
Office Ally: Healthcare Software for Your Medical PracticeService Center by Office Ally is a trusted revenue cycle management platform used by over 65,000 healthcare organizations processing more than 350 million claims annually. With it, providers can verify patient eligibility and benefits, upload and submit claims, correct rejected claims, check claim status, and obtain remits. With multiple claim types and submission options, providers can easily submit claims to any payer from any practice management system. Transactions are secure, ensuring the confidentiality of sensitive patient information. With no needed implementation, providers can quickly and effortlessly streamline their billing processes, increase their financial performance, simplify medical billing, and reduce claim rejections for faster reimbursements.
-
Digital business card + lead capture + contact enrichmentShare digital business cards, capture leads, and enrich validated contact info - at events, in the field, and beyond. Powered by AI and our proprietary data engine, Popl drives growth for companies around the world, turning every handshake into an opportunity.
-
1
OnnxStream
Lightweight inference library for ONNX files, written in C++
...So I decided to write a super small and hackable inference library specifically focused on minimizing memory consumption: OnnxStream. OnnxStream is based on the idea of decoupling the inference engine from the component responsible for providing the model weights, which is a class derived from WeightsProvider. A WeightsProvider specialization can implement any type of loading, caching, and prefetching of the model parameters. -
2
SSD in PyTorch 1.0
High quality, fast, modular reference implementation of SSD in PyTorch
...Multi-GPU training and inference: We use DistributedDataParallel, you can train or test with arbitrary GPU(s), the training schema will change accordingly. Add your own modules without pain. We abstract backbone, Detector, BoxHead, BoxPredictor, etc. You can replace every component with your own code without changing the code base. For example, You can add EfficientNet as the backbone, just add efficient_net.py (ALREADY ADDED) and register it, specific it in the config file, It's done! Smooth and enjoyable training procedure: we save the state of model, optimizer, scheduler, training iter, you can stop your training and resume training exactly from the save point without change your training CMD.
- Previous
- You're on page 1
- Next
