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  • Automate contact and company data extraction Icon
    Automate contact and company data extraction

    Build lead generation pipelines that pull emails, phone numbers, and company details from directories, maps, social platforms. Full API access.

    Generate leads at scale without building or maintaining scrapers. Use 10,000+ ready-made tools that handle authentication, pagination, and anti-bot protection. Pull data from business directories, social profiles, and public sources, then export to your CRM or database via API. Schedule recurring extractions, enrich existing datasets, and integrate with your workflows.
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  • 1
    vJEPA-2

    vJEPA-2

    PyTorch code and models for VJEPA2 self-supervised learning from video

    VJEPA2 is a next-generation self-supervised learning framework for video that extends the “predict in representation space” idea from i-JEPA to the temporal domain. Instead of reconstructing pixels, it predicts the missing high-level embeddings of masked space-time regions using a context encoder and a slowly updated target encoder. This objective encourages the model to learn semantics, motion, and long-range structure without the shortcuts that pixel-level losses can invite. The...
    Downloads: 2 This Week
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  • 2
    YOLOv3

    YOLOv3

    Object detection architectures and models pretrained on the COCO data

    Fast, precise and easy to train, YOLOv5 has a long and successful history of real time object detection. Treat YOLOv5 as a university where you'll feed your model information for it to learn from and grow into one integrated tool. You can get started with less than 6 lines of code. with YOLOv5 and its Pytorch implementation. Have a go using our API by uploading your own image and watch as YOLOv5 identifies objects using our pretrained models. Start training your model without being an...
    Downloads: 116 This Week
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  • 3
    MoCo v3

    MoCo v3

    PyTorch implementation of MoCo v3

    MoCo v3 is a PyTorch reimplementation of Momentum Contrast v3 (MoCo v3), Facebook Research’s state-of-the-art self-supervised learning framework for visual representation learning using ResNet and Vision Transformer (ViT) backbones. Originally developed in TensorFlow for TPUs, this version faithfully reproduces the paper’s results on GPUs while offering an accessible and scalable PyTorch interface. MoCo v3 introduces improvements for training self-supervised ViTs by combining contrastive...
    Downloads: 2 This Week
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  • 4
    Detectron2

    Detectron2

    Next-generation platform for object detection and segmentation

    Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. Can be used as a library to support different projects on top of it. We'll...
    Downloads: 0 This Week
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  • Lightspeed golf course management software Icon
    Lightspeed golf course management software

    Lightspeed Golf is all-in-one golf course management software to help courses simplify operations, drive revenue and deliver amazing golf experiences.

    From tee sheet management, point of sale and payment processing to marketing, automation, reporting and more—Lightspeed is built for the pro shop, restaurant, back office, beverage cart and beyond.
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  • 5
    Dive-into-DL-TensorFlow2.0

    Dive-into-DL-TensorFlow2.0

    Dive into Deep Learning

    This project changes the MXNet code implementation in the original book "Learning Deep Learning by Hand" to TensorFlow2 implementation. After consulting Mr. Li Mu by the tutor of archersama , the implementation of this project has been agreed by Mr. Li Mu. Original authors: Aston Zhang, Li Mu, Zachary C. Lipton, Alexander J. Smola and other community contributors. There are some differences between the Chinese and English versions of this book . This project mainly focuses on TensorFlow2...
    Downloads: 0 This Week
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  • 6
    Deepo

    Deepo

    Set up deep learning environment in a single command line

    ...Their Dockerfile generator that allows you to customize your own environment with Lego-like modules, and automatically resolves the dependencies for you. For users in China who may suffer from slow speeds when pulling the image from the public Docker registry, you can pull deepo images from the China registry mirror by specifying the full path, including the registry, in your docker pull command. This should work and enables Deepo to use the GPU from inside a docker container.
    Downloads: 0 This Week
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