Showing 2 open source projects for "computer based training"

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    Host LLMs in Production With On-Demand GPUs

    NVIDIA L4 GPUs. 5-second cold starts. Scale to zero when idle.

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    99.99% Uptime for MySQL and PostgreSQL Databases

    Sub-second maintenance. 2x read/write performance. Built-in vector search for AI apps.

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    AutoCrop-Vertical

    AutoCrop-Vertical

    Smart video converter using YOLOv8 and FFmpeg

    AutoCrop-Vertical is a Python-based video processing tool that automatically converts horizontal videos into vertical formats optimized for social media platforms. It uses computer vision techniques and AI models such as YOLOv8 to analyze each frame, detect subjects, and dynamically adjust cropping decisions. Instead of applying a static center crop, the system intelligently tracks people or key objects to preserve visual focus and composition.
    Downloads: 1 This Week
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    Super-résolution via CNN

    Super-résolution via CNN

    Super resolution using a CNN, based on the work of the DGtal team

    Super-resolution using a CNN, based on the work of the DGtal team. First of all, an Nvidia graphics card (neither AMD nor Intel integrated) is highly recommended to parallelize the CNN. You will then need to install CUDA. No CUDA = dozens of times slower. This program will generate "model_epoch_ .pth" files corresponding to the model at epoch n, in a folder saved_model_u t_bs bs_tbs tbs_lr lr, where corresponds to the scale factor, bsthe size of the training batch, tbsthe size of the test batch and lrto the learning rate. ...
    Downloads: 1 This Week
    Last Update:
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