Software Environment
- Windows 10
- Anaconda for python
- TensorFlow-GPU
- CUDA v9.0
- cuDNN v7.0
Setup Application
- Open Anaconda prompt
- Type “conda create -n tensorflow1 pip python=3.5”
- Type “activate tensorflow1”
- Type “pip install –ignore-installed –upgrade tensorflow-gpu”
- Type “conda install -c anaconda protobuf”
- Type “pip install pillow”
- Type “pip install lxml”
- Type “pip install Cython”
- Type “pip install jupyter”
- Type “pip install matplotlib”
- Type “pip install pandas”
- Type “pip install opencv-python”
Configuration PYTHONPATH
Reusing the same environment with a new Object Detection
Command
(tensorflow1) C:\> set PYTHONPATH=D:\master_project\SSD\models;D:\master_project\SSD\models\research;D:\master_project\SSD\models\research\slim
(tensorflow1) C:\> set PYTHONPATH=D:\jebat\FRCNN_XRAY\models;D:\jebat\FRCNN_XRAY\models\research;D:\jebat\FRCNN_XRAY\models\research\slim
set PATH=%PATH%;PYTHONPATH;D:\NVIDIA\cuda\bin
cd D:\master_project\SSD\models\research
protoc –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\losses.proto .\object_detection\protos\matcher.proto .\object_detection\protos\mean_stddev_box_coder.proto .\object_detection\protos\model.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 .\object_detection\protos\keypoint_box_coder.proto .\object_detection\protos\multiscale_anchor_generator.proto .\object_detection\protos\graph_rewriter.proto
python setup.py build
python setup.py install
cd D:\master_project\SSD\models\research\object_detection
python xml_to_csv.py
python generate_tfrecord.py –csv_input=images\train_labels.csv –image_dir=images\train –output_path=train.record
python generate_tfrecord.py –csv_input=images\test_labels.csv –image_dir=images\test –output_path=test.record
python train.py –logtostderr –train_dir=training/ –pipeline_config_path=training/faster_rcnn_inception_v2_pets.config
python train.py –logtostderr –train_dir=training/ –pipeline_config_path=training/ssd_inception_v2_coco.config
python export_inference_graph.py –input_type image_tensor –pipeline_config_path training/faster_rcnn_inception_v2_pets.config –trained_checkpoint_prefix training/model.ckpt-200000 –output_directory inference_graph
Login – ID7vs-j6g-6u5
Image Classification using Convolutional Neural Networks in Keras
CNN
R-CNN
SSD MOBILENET
FASTER RCNN INCEPTION RESNET
SSD INCEPTION
SSD RESNET
untuk kat computer bok,
Tukar ke D
activate tensorflow_gpu
Ref:
https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
https://towardsdatascience.com/build-your-first-deep-learning-classifier-using-tensorflow-dog-breed-example-964ed0689430
https://github.com/matterport/