Welcome to annolid’s documentation!¶
- Annolid User Guide
- Install Annolid
- Install Detectron2 locally
- Install Detectron2 on Google Colab
- Optional: Install older version of Pytorch for YOLACT
- Extract desired number of frames from a video based on optical flow
- Display optical flow while extracting frames with –show_flow=True
- Save all the frames as images
- Select frames randomly by reservoir sampling
- Extract all the key frames from a video used by the compression methods
- Track animals and Auto labeling
- Output CSV format
- Config keypoint connection rules, events, and instances
- Threshold based object segmenation
- Convert WMV format to mp4 format using ffmpeg
- Save the extracted frames to a user selected output directory
- How to track multiple objects in the video?
- How to convert coco annonation format to YOLOV5 format?
- How to train a custom YOLOV5 model?
- How to track objects in a video with the trained model?
- How to convert labelme labeled dataset to COCO format?
- How to train a YOLACT model with a custom dataset?
- How to evaluate a video based on a trained model?
- Convert the tracking results csv file to Glitter2 csv format
- Convert the keypoint annotations to labelme format