Showing 2 open source projects for "labelme"

View related business solutions
  • PRTG Catches Network Issues Before They Cause Downtime Icon
    PRTG Catches Network Issues Before They Cause Downtime

    Threshold-based alerts flag problems early, so your team can act before users notice, not after.

    Reactive troubleshooting usually means hearing about a problem from frustrated users, not your monitoring tool. PRTG sets threshold-based alerts across devices, servers and applications, notifying your team by email, SMS or push the moment a metric crosses a set limit. That means catching a failing disk or overloaded server before it becomes an outage and getting time back from firefighting. Start a free trial and set your first alerts today.
    Download 30-Day Trial
  • Veeam Data Platform v13.1 Icon
    Veeam Data Platform v13.1

    Move workloads across hypervisors and clouds with no vendor lock-in. Try VDP free today.

    Try Veeam Data Platform today. Experience the unified platform that's secure by design, portable by default, and proven to recover clean, fast, and anywhere.
    Try Now
  • 1
    labelme Image Polygonal Annotation

    labelme Image Polygonal Annotation

    Image polygonal annotation with Python

    ...Exporting VOC-format dataset for semantic/instance segmentation. (semantic segmentation, instance segmentation). Exporting COCO-format dataset for instance segmentation. (instance segmentation). The first time you run labelme, it will create a config file in ~/.labelmerc. You can edit this file and the changes will be applied the next time that you launch labelme. If you would prefer to use a config file from another location, you can specify this file with the --config flag.
    Downloads: 8 This Week
    Last Update:
    See Project
  • 2
    YOLOX

    YOLOX

    YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5

    ...It aims to bridge the gap between research and industrial communities. Prepare your own dataset with images and labels first. For labeling images, you can use tools like Labelme or CVAT. One more thing worth noting is that you should also implement pull_item and load_anno method for the Mosiac and MixUp augmentations. Except special cases, we always recommend using our COCO pre-trained weights for initializing the model. As YOLOX is an anchor-free detector with only several hyper-parameters, most of the time good results can be obtained with no changes to the models or training settings.
    Downloads: 2 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next