File	Architecture	Task
`CLS_colony_counting_bw_model.pth`	EfficientNet-B0 (PyTorch)	Binary well classification backbone
`CLS_colony_counting_lr_head.joblib`	Logistic Regression (scikit-learn)	Binary classifier head (positive / negative)
`CLS_colony_counting_count_model_smoothl1.pth`	EfficientNet-B0 (PyTorch)	Colony count regression

Stage 1 — Binary Well Classification

Classifies each cropped well image as positive (colony growth present) or negative (no growth).

Backbone: EfficientNet-B0 fine-tuned end-to-end, 1280-d average-pool features
Head: Logistic Regression (class_weight="balanced")
Output: binary label + probability of positive (p_pos) + two confidence scores (MSP, entropy)

Stage 2 — Colony Count Regression

Predicts the colony count for positive wells. Zero-count wells are handled by Stage 1.

Backbone: EfficientNet-B0 with a single regression output neuron
Loss: SmoothL1 (Huber loss)
Uncertainty: Monte Carlo dropout (20 forward passes at inference)
Output: predicted count (clamped to [0, 100]) + standard deviation across MC samples

Usage

Download the models using the companion script in the colony_counting repository:

bash

git clone https://github.com/Center-for-Living-Systems/colony_counting.git
cd colony_counting
conda env create -f environment.yml
conda activate colony_counting
pip install -e .
python download_models.py

Then run the full pipeline:

jupyter notebook notebooks/run_full_pipeline.ipynb

Set PLATE_IMAGE_FOLDER and OUTPUT_DIR in the config cell — model paths are auto-discovered from trained_models/.

Input Format

Plate images: grayscale TIFF, uint16, 1944 × 2592 px, one per 96-well plate
Well crops: 96 PNG images per plate (~154 × 154 px), extracted by the pipeline's well detection step

Training Data

Models were trained on 96-well plate images from yeast colony growth experiments. Labels were collected using a custom Streamlit labelling interface and split at the plate level to minimise data leakage.

License

BSD 3-Clause — see LICENSE.

Citation

If you use these models, please cite the colony_counting repository:

Center for Living Systems, University of Chicago.
Colony Counting Pipeline. https://github.com/Center-for-Living-Systems/colony_counting

Center-for-Living-Systems-uchicago/colony-counting

Colony Counting Models

Models