Showing 8 open source projects for "image processor pro"

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  • 1
    Depth Pro

    Depth Pro

    Sharp Monocular Metric Depth in Less Than a Second

    Depth Pro is a foundation model for zero-shot metric monocular depth estimation, producing sharp, high-frequency depth maps with absolute scale from a single image. Unlike many prior approaches, it does not require camera intrinsics or extra metadata, yet still outputs metric depth suitable for downstream 3D tasks. Apple highlights both accuracy and speed: the model can synthesize a ~2.25-megapixel depth map in around 0.3 seconds on a standard GPU, enabling near real-time applications. ...
    Downloads: 6 This Week
    Last Update:
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  • 2
    FLUX.2

    FLUX.2

    Official inference repo for FLUX.2 models

    FLUX.2 is a state-of-the-art open-weight image generation and editing model released by Black Forest Labs aimed at bridging the gap between research-grade capabilities and production-ready workflows. The model offers both text-to-image generation and powerful image editing, including editing of multiple reference images, with fidelity, consistency, and realism that push the limits of what open-source generative models have achieved. It supports high-resolution output (up to ~4 megapixels),...
    Downloads: 24 This Week
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  • 3
    Anything to NotebookLM

    Anything to NotebookLM

    Multi-source content processor for NotebookLM

    Qiaomu Anything to NotebookLM is a Claude Code skill that turns many types of source material into structured NotebookLM-ready outputs. It is built for users who want to convert articles, web pages, videos, PDFs, office files, podcasts, images, and search results into more usable study or presentation formats. The project uses natural-language commands, so the user can ask for a podcast, slide deck, mind map, report, quiz, flashcards, or infographic without manually building the workflow. It...
    Downloads: 1 This Week
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  • 4
    Qwen3-Omni

    Qwen3-Omni

    Qwen3-omni is a natively end-to-end, omni-modal LLM

    ...It uses a Thinker-Talker architecture with a Mixture-of-Experts (MoE) design, early text-first pretraining, and mixed multimodal training to support strong performance across all modalities without sacrificing text or image quality. The model supports 119 text languages, 19 speech input languages, and 10 speech output languages. It achieves state-of-the-art results: across 36 audio and audio-visual benchmarks, it hits open-source SOTA on 32 and overall SOTA on 22, outperforming or matching strong closed-source models such as Gemini-2.5 Pro and GPT-4o. ...
    Downloads: 0 This Week
    Last Update:
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  • 5
    Jittor

    Jittor

    Jittor is a high-performance deep learning framework

    Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just in time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement...
    Downloads: 1 This Week
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  • 6
    ViralMint

    ViralMint

    Open-source AI video pipeline, fully automated with MCP

    ViralMint is a free, open-source desktop app for short-form video — scout viral trends, clip from 1,800+ sources, and generate AI videos, motion graphics and voiceovers for cents.Released April 2026 under the AGPL-3.0 license, ViralMint runs as a free desktop app for macOS, Windows and Linux. It scouts trending videos across YouTube, TikTok, Douyin, Reddit and Google Trends, scores each for virality and channel-outlier breakouts, and downloads from 1,800+ video sites via yt-dlp. It...
    Downloads: 6 This Week
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  • 7
    BerryNet

    BerryNet

    Deep learning gateway on Raspberry Pi and other edge devices

    This project turns edge devices such as Raspberry Pi into an intelligent gateway with deep learning running on it. No internet connection is required, everything is done locally on the edge device itself. Further, multiple edge devices can create a distributed AIoT network. At DT42, we believe that bringing deep learning to edge devices is the trend towards the future. It not only saves costs of data transmission and storage but also makes devices able to respond according to the events...
    Downloads: 0 This Week
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  • 8
    IMAGINE

    IMAGINE

    Biological image viewer and processor

    Detection, enumeration, and sizing of biological organisms by image analysis.
    Downloads: 1 This Week
    Last Update:
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