Yolov3-for-thyroid-nodule-detection

# YOLOV3 for thyroid nodules detection

We trained Yolov3 on the data set of 1805 b-ultrasound images of thyroid nodules. And the weights are stored in ./pretrained_model .The test interface is offered here.

Introductions

The forward process of Yolov3 will run on the GPU by default.

# Requirements

Python 3.6 or later with the following pip install -r requirements.txt packages:

• numpy
• torch>=1.0
• torchvision
• tensorflow
• pillow
• tqdm
• opencv

Before testing

• The input image format must be 'jpg' or 'png'.

• Store all images to be tested in the same directory</test_path>, and the default path is./test_data .

• Create a directory </store_path> under the project directory to store the test results, and the default path is ./test_result .

Testing

Script: test_my.py

Optional args:

• image_path -path to test images
• store_path -path to test results
• img_size -size of each image dimension(no change recommended here)
• n_cpu -number of CPU threads to use during batch generation
• pretrained_weights -path to pretrained weights
• model_def -path to model definition file
• batch_size -size of each image batch

Run:

python test_my.py --image_path <test_path> --store_path <store_path> You can got result images in output folder.