# Object Detection Finally found Google's release on Object Detection **Pre-requisites:** **Make sure to do the following:** 1. mkdir /tensorflow\_models 2. cd /tensorflow\_models 3. git clone 4. **Follow the installation instructions**, but at least install protobuf-compiler and run protoc **ON EVERY BASH SHELL** 1. Ref: ```python apt-get install protobuf-compiler cd /tensorflow_models/models/research protoc object_detection/protos/*.proto --python_out=. export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim ``` 1. **Test installation using:** python object\_detection/builders/model\_builder\_test.py 2. Download VOC2007 (smaller in size) and then run 'create\_pascal\_tf\_record' to generate TFRecords ```python cd /tf_files/SSD-Tensorflow wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar tar xvf VOCtrainval_06-Nov-2007.tar cd /tensorflow_models/models/object_detection python create_pascal_tf_record.py --data_dir=/tf_files/SSD-Tensorflow/VOCdevkit \ --year=VOC2007 --set=train --output_path=/tmp/pascal_voc2007_train.record python create_pascal_tf_record.py --data_dir=/tf_files/SSD-Tensorflow/VOCdevkit \ --year=VOC2007 --set=val --output_path=/tmp/pascal_voc2007_val.record # I customized create_tf_record.py and dataset_util.py to work on my custom dataset # This is needed to cutomize any folder tag, ignore difficult tag, provide a new label_map_path etc.. # Manual process: Create a new label_map_path pertaining to the new classification labels # # cp /tf_files/SSD-Tensorflow/MYDEVKIT_2/VOC2007/mydevkit_label_map.pbtxt /tensorflow_models/models/research/object_detection/data/mydevkit_label_map.pbtxt python create_pascal_tf_record.py \ --data_dir=/tf_files/SSD-Tensorflow/MYDEVKIT_2 \ --year=VOC2007 \ --set=train \ --output_path=/tmp/mydev_train.record \ --label_map_path=/tensorflow_models/models/research/object_detection/data/mydevkit_label_map.pbtxt \ --folder_tag=VOC2007 \ --ignore_difficult_tags=True python create_pascal_tf_record.py \ --data_dir=/tf_files/SSD-Tensorflow/MYDEVKIT_2 \ --year=VOC2007 \ --set=val \ --output_path=/tmp/mydev_val.record \ --label_map_path=/tensorflow_models/models/research/object_detection/data/mydevkit_label_map.pbtxt \ --folder_tag=VOC2007 \ --ignore_difficult_tags=True ``` 1. Arrange the directory structure, create EXPORT variables 2. ```python cd /tensorflow_models/models protoc object_detection/protos/*.proto --python_out=. export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim PATH_TO_EVAL_DIR=/tensorflow_models/models/research/object_detection/models/model/eval PATH_TO_TRAIN_DIR=/tensorflow_models/models/research/object_detection/models/model/train PATH_TO_YOUR_PIPELINE_CONFIG=/tensorflow_models/models/research/object_detection/samples/configs/ssd_mobilenet_v1_voc2007.config PATH_TO_MODEL_DIRECTORY=/tensorflow_models/models/research/object_detection/models/model xyz Note: PATH_TO_MODEL_DIRECTORY is actually where the checkpoints will get generated. Shall we change the name to PATH_TO_GENERATED_CHECKPOINT? ``` 3. Generate TF records ```python # I customized create_tf_record.py and dataset_util.py to work on my custom dataset # This is needed to cutomize any folder tag, ignore difficult tag, provide a new label_map_path etc.. # Manual process: Create a new label_map_path pertaining to the new classification labels # # cp /tf_files/SSD-Tensorflow/MYDEVKIT_2/VOC2007/mydevkit_label_map.pbtxt /tensorflow_models/models/object_detection/data/mydevkit_label_map.pbtxt python create_pascal_tf_record.py \ --data_dir=/tf_files/SSD-Tensorflow/MYDEVKIT_2 \ --year=VOC2007 \ --set=train \ --output_path=/tmp/mydev_train.record \ --label_map_path=/tensorflow_models/models/research/object_detection/data/mydevkit_label_map.pbtxt \ --folder_tag=VOC2007 \ --ignore_difficult_tags=True python create_pascal_tf_record.py \ --data_dir=/tf_files/SSD-Tensorflow/MYDEVKIT_2 \ --year=VOC2007 \ --set=val \ --output_path=/tmp/mydev_val.record \ --label_map_path=/tensorflow_models/models/research/object_detection/data/mydevkit_label_map.pbtxt \ --folder_tag=VOC2007 \ --ignore_difficult_tags=True ``` 1. 1. Run training ```python # From the tensorflow/models/ directory python object_detection/train.py \ --logtostderr \ --pipeline_config_path=${PATH_TO_YOUR_PIPELINE_CONFIG} \ --train_dir=${PATH_TO_TRAIN_DIR} ``` Run eval ```python # From the tensorflow/models/ directory python object_detection/eval.py \ --logtostderr \ --pipeline_config_path=${PATH_TO_YOUR_PIPELINE_CONFIG} \ --checkpoint_dir=${PATH_TO_TRAIN_DIR} \ --eval_dir=${PATH_TO_EVAL_DIR} ``` Run tensorboard ```python PATH_TO_MODEL_DIRECTORY=/tensorflow_models/models/research/object_detection/models/model tensorboard --logdir=${PATH_TO_MODEL_DIRECTORY} ``` Exporting a model for inference ```python # From tensorflow/models python object_detection/export_inference_graph.py \ --input_type image_tensor \ --pipeline_config_path ${PATH_TO_YOUR_PIPELINE_CONFIG} \ --checkpoint_path object_detection/models/model/train/model.ckpt-16069 \ --inference_graph_path object_detection/models/model/train/output_inference_graph.pb ``` Running inference on new files ```python # After exporting your checkpoint as a graph, # cd into deep_learning_repo/object_detection # place your test image inside test_images directory (For example: pic1.jpg) python inference.py --path_to_ckpt=models/model/train/output_inference_graph.pb ``` ### Controlling GPU memory Ref: ```python # In tensorflow_models/models/object_detection/trainer.py #gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5, allow_growth=True) gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8) session_config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False, gpu_options=gpu_options) ```