Training, Evaluation and Inference

CancerNet-SCa models takes as input an image of shape (N, 224, 224, 3) and outputs the softmax probabilities as (N, 2), where N is the number of batches. If using the TF checkpoints, here are some useful tensors:

• input tensor: input_1:0
• logit tensor: probs/MatMul:0
• output tensor: probs/Softmax:0
• label tensor: probs_target:0
• loss tensor: loss/mul:0
• training placeholder tensor: keras_learning_phase:0

Steps for evaluation

1. We provide you with the tensorflow evaluation script, eval.py
2. Locate the tensorflow checkpoint files
3. To evaluate a tf checkpoint:

python eval.py \
    --weightspath models/CancerNet-SCa-A \
    --metaname model.meta \
    --ckptname model-0

4. For more options and information, python eval.py --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.

1. Download a model from the pretrained models section
2. Locate models and image to be inferenced
3. To inference,

python inference.py \
    --weightspath models/CancerNet-SCa-A \
    --metaname model.meta \
    --ckptname model-0 \
    --imagepath assets/ex_malignant.jpg

4. For more options and information, python inference.py --help