Code and exercises for the Deep learning applied to medical images course
- 00_intro_to_keras
- 01_net_from_scrach_blood
- 02_data_augmentation_blood
- 03_transfer_learning_cats_dogs
- 04_object_detection_blood
The class will work all on cloud. Exercises and environment instructions are provide for reference. It is NOT necesary to download or create the environment.
https://github.com/sueiras/training/blob/master/docs/aws.md
https://github.com/sueiras/training/blob/master/docs/install_tensorflow_ubuntu_aws.md
- region: Ireland
- AMI id: ami-8eb287f7
- Name: sueiras-medical-images-02
Only for reference. Not necesary for the course.
1.- Install anaconda python 3.6 last version. All default options.
2.- Start the Anaconda terminal and execute...
# update package managers
conda update conda
# Create environment and install packages
conda create -n tf python=3.6
conda activate tf
conda install graphviz
conda install pandas scikit-learn
conda install -c anaconda jupyter
conda install matplotlib
conda install pillow
pip install Cython
pip install pydot-ng
pip install lxml
pip install --ignore-installed --upgrade tensorflow
Detailed instructions here
For windows users read this
- Activate the previous Anaconda environment. The API basic dependencies are already included.
-
Whit tensorflow 1.7 and 1.8, first solve this bug
-
Download Google Protobuf https://github.com/google/protobuf Windows v3.4.0 release “protoc-3.4.0-win32.zip” and extract
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Move to the dir models\research.
cd <path_to_tensorflow_models>\models\research
- Execute the protobuf compilation
"<path_to_protobuf>\protoc-3.5.1-win32\bin\protoc.exe" --python_out=. .\object_detection\protos\anchor_generator.proto .\object_detection\protos\argmax_matcher.proto .\object_detection\protos\bipartite_matcher.proto .\object_detection\protos\box_coder.proto .\object_detection\protos\box_predictor.proto .\object_detection\protos\eval.proto .\object_detection\protos\faster_rcnn.proto .\object_detection\protos\faster_rcnn_box_coder.proto .\object_detection\protos\grid_anchor_generator.proto .\object_detection\protos\hyperparams.proto .\object_detection\protos\image_resizer.proto .\object_detection\protos\input_reader.proto .\object_detection\protos\keypoint_box_coder.proto .\object_detection\protos\losses.proto .\object_detection\protos\matcher.proto .\object_detection\protos\mean_stddev_box_coder.proto .\object_detection\protos\model.proto .\object_detection\protos\multiscale_anchor_generator.proto .\object_detection\protos\optimizer.proto .\object_detection\protos\pipeline.proto .\object_detection\protos\post_processing.proto .\object_detection\protos\preprocessor.proto .\object_detection\protos\region_similarity_calculator.proto .\object_detection\protos\square_box_coder.proto .\object_detection\protos\ssd.proto .\object_detection\protos\ssd_anchor_generator.proto .\object_detection\protos\string_int_label_map.proto .\object_detection\protos\train.proto
cd <path_to_tensorflow_models>\models\research
set PYTHONPATH=%PYTHONPATH%;C:\<full_path_to_tensorflow_models>\models\research;C:\<full_path_to_tensorflow_models>\models\research\slim
cd <path_to_tensorflow_models>\models\research
python object_detection/builders/model_builder_test.py
cd <path_to_tensorflow_models>\models\research\object_detection\models
[faster_rcnn_inception_v2 (142Mb)](http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_v2_coco_2018_01_28.tar.gz)