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Training, Evaluation and Inference

The networks take as input an image of shape (N, 512, 512, 3) and output the softmax probabilities as (N, 3), where N is the number of images. For the TensorFlow checkpoints, here are some useful tensors:

  • input tensor: Placeholder:0
  • label tensor: Placeholder_1:0
  • logits tensor: resnet_model/final_dense:0
  • output confidence tensor: softmax_tensor:0
  • output prediction tensor: ArgMax:0
  • loss tensor: add:0
  • training tensor: is_training:0

Steps for training

  1. We provide you with the TensorFlow training script, run_covidnet_ct.py
  2. Locate the TensorFlow checkpoint files (location of pretrained model)
  3. To train from a pretrained model:
python run_covidnet_ct.py train \
    --model_dir models/COVID-Net_CT-2_L \
    --meta_name model.meta \
    --ckpt_name model

For more options and information, python run_covid_ct.py train --help

Steps for testing

  1. We provide you with the TensorFlow testing script, run_covidnet_ct.py
  2. Locate the TensorFlow checkpoint files
  3. To evaluate a TensorFlow checkpoint:
python run_covidnet_ct.py val \
    --model_dir models/COVID-Net_CT-2_L \
    --meta_name model.meta \
    --ckpt_name model \
    --plot_confusion

For more options and information, python run_covid_ct.py val --help

Steps for inference

DISCLAIMER: Do not use this prediction for self-diagnosis. You should check with your local authorities for the latest advice on seeking medical assistance.

A special inference notebook is included which provides inference code and Grad-CAM visualizations. This is the easiest way to run inference and see visual results.

Inference may also be run using the main script via the following steps:

  1. Download a model from the pretrained models section
  2. Locate models and CT image to be tested
  3. To run inference,
python run_covidnet_ct.py infer \
    --model_dir models/COVID-Net_CT-2_L \
    --meta_name model.meta \
    --ckpt_name model \
    --image_file assets/ex-covid-ct.png

For more options and information, python run_covid_ct.py infer --help