README.md

What is Pants?

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setting up conda env whatispants in specified yolo_train dir

conda create -n whatispants python=3.10.12

conda activate whatispants   

install requirements in conda env

pip install -r requirements.txt --no-cache-dir

Install jupyter lab if not already installed:

pip install jupyterlab==4.2.0

Create kernel based on conda env for Jupyter notebook

ipython kernel install --user --name=whatispants 

Start Jupyter lab

jupyter lab

In Jupyter lab open WhatIsPants.ipynb and select the whatispants kernel in the top-right corner.

After training, to run segmentation inference:

Get the trained model file best.pt from the training output, and then run

yolo segment predict model=best.pt source='test_images/*'

Preparing LVIS

Copy all images into folder to be subsetted:

cp -r ~/datasets/lvis/images datasets/lvis_pants/

Subset only pants labels:

# Training set 
python subset_lvis_pants_labels.py \
  --source_directory "$HOME/datasets/lvis/labels/train2017/" \
  --target_directory "datasets/lvis_pants/labels/train2017/"

# Validation set
python subset_lvis_pants_labels.py \
  --source_directory "$HOME/datasets/lvis/labels/val2017/" \
  --target_directory "datasets/lvis_pants/labels/val2017/"

# Check number of resulting non-empty labels
# Should be 4462 train and 184 val
find datasets/lvis_pants/labels/train2017 -type f -size +0c | wc -l 
find datasets/lvis_pants/labels/val2017 -type f -size +0c | wc -l

Keep only as many pantsless images as there are pantsful images

# Training set
python remove_superfluous_empty_labels.py \
  --labels_directory datasets/lvis_pants/labels/train2017 \
  --images_directory datasets/lvis_pants/images/train2017
  
# Validation set
python remove_superfluous_empty_labels.py \
  --labels_directory datasets/lvis_pants/labels/val2017 \
  --images_directory datasets/lvis_pants/images/val2017

Remove images which have no corresponding label file

# Training set
python delete_labelless_images.py \
  --images_directory datasets/lvis_pants/images/train2017 \
  --labels_directory datasets/lvis_pants/labels/train2017

# Validation set
python delete_labelless_images.py \
  --images_directory datasets/lvis_pants/images/val2017 \
  --labels_directory datasets/lvis_pants/labels/val2017

Captain's Log

2024-05-11: LVIS bad pants labels

We observed that the LVIS dataset contains images with pants where the pants are not annotated. For example: 000000096670.jpg shows a baseball player, and the labels include a baseball, a home base, a bat, and a belt, but no pants.

TO DO:

• 2024-08-17: the notebook works up until "inspect annotations" after "subset data into train..."

  • The inspect annotations bit should probably look at deepfash annotations first
  • We didn't seem to document how to download the LVIS dataset yet
  • lvis.yaml itself seems to contain an embedded script for downloading it

• Google Colab uses python 3.10.12, so we should use that in our conda environment and downgrade some dependencies accordingly:

  ERROR: pip's dependency resolver does not currently take into account all the packages that are installed.
  This behaviour is the source of the following dependency conflicts.
  google-colab 1.0.0 requires ipykernel==5.5.6, but you have ipykernel 6.29.4 which is incompatible.
  notebook 6.5.5 requires jupyter-client<8,>=5.3.4, but you have jupyter-client 8.6.1 which is incompatible.
  tensorflow-metadata 1.15.0 requires protobuf<4.21,>=3.20.3; python_version < "3.11", but you have protobuf 4.25.3 which is incompatible.
  tf-keras 2.15.1 requires tensorflow<2.16,>=2.15, but you have tensorflow 2.16.1 which is incompatible.

• run yolo small and xl model (epochs: 5, 20, 50, 100)

• run yolo test run (yolov8l-seg.pt used)

• find bug in mask2contour - pants are found but not there, color issue?

  • faulty file: WOMEN-Blouses_Shirts-id_00001443-01_4_full_segm.png

• random select 2000 png files from segm dir

• based on selected segm pngs - select image files from images_fullres dir that match segm png filename