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SLICE - Semi-supervised Learning approach for Image segmentation of Crop field boundariEs

AIM: Produce accurate field boundary segmentation maps in regions with little to no ground truth labels.

METHOD: Semi-supervised Instance segmentation

TRACKER: Notion link

Usage

  • Perform a Recursive git clone

    git clone --recursive git@github.com:kerner-lab/slice.git

  • Change a code in Transformers library under models folder

    • Path slice/models/transformers/src/transformers/models/sam/image_processing_sam.py

    • Modification

    Line 938 :  (Error)
crop_boxes = crop_boxes.astype(np.float32)

should work with
crop_boxes = np.array(crop_boxes, dtype=np.float32)
    • Perform installation for Transformers library
    pip install -e .

Guidelines

  • Following semantic versioning with Major.Minor.Patch
  • Every small changes will come under patch version. (bugfix, bug)
  • Every feature introduction will change the minor version.
  • Every interface logic change will change the major version.
  • Example of the versioning
    • 2.0.005
      • Major version - 2
      • Minor Version - 0
      • Patch Version - 005
    • Always create a new branch for a feature introduction and raise a pull request accordingly.
    • Releases will be tagged either Pre Release or Latest Release from the main branch only.
    • Git commits will have the changelogs description as the commit message. e.g. 2.0.005 (feature+update)
    • Definition of keywords
      • feature - Any introduction of a new feature
      • bug - With the commited change the system is in a buggy state.
      • bugfix - for any patches applied over a bug.
      • update - general updates like updating readme. (this won't increment any version numbers)
      • experimental - This stays out of the main branch unless the experiment is solidified to create a feature out of it.

Changelogs

0.0.1 (feature + bugfix)
  • Added Segment Anything Project as a submodule.
  • Fixed and issue with the setup_sol.sh file.
0.0.2 (feature)
  • Added code to extract tf record.
  • Added code to generate pseudo masks for the image dataset using SAM.
0.0.3 (bug + bugfix)
  • The conversion code for tf record is not working properly. -> BUG
  • Added script for downloading the model checkpoints.
  • Made path changes in generate_pseudo_masks.sh file.
0.0.4 (feature + bugfix)
  • Conversion for TFrecord
  • Extract images script for inference
  • Output visualization script .ipynb
0.0.5 (feature)
  • Working with the combine patches for display. The patches(masks) are generated as separate files (instances)
0.0.6 (feature)
  • Dataset creating script added to data.ipynb
0.0.7 (feature + bugfix + update)
  • The Dataset loader is now available.
  • A Visualization notebook is available. See infer_and_eval.ipynb
0.1.0 (feature)
  • Added Transformers Library from HuggingFace
  • Recreated Inference Pipeline for better understanding and decoupling it from the HuggingFace internal Pipeline.
  • WIP for training script.