# =========================================================================== # # Dataset convert... # =========================================================================== # rm events* graph* model* checkpoint mv events* graph* model* checkpoint ./log DATASET_DIR=/media/paul/DataExt4/PascalVOC/rawdata/VOC2012/trainval/ OUTPUT_DIR=/media/paul/DataExt4/PascalVOC/dataset python tf_convert_data.py \ --dataset_name=pascalvoc \ --dataset_dir=${DATASET_DIR} \ --output_name=voc_2012_train \ --output_dir=${OUTPUT_DIR} CAFFE_MODEL=/media/paul/DataExt4/PascalVOC/training/ckpts/SSD_300x300_VOC0712/VGG_VOC0712_SSD_300x300_iter_120000.caffemodel python caffe_to_tensorflow.py \ --model_name=ssd_300_vgg \ --num_classes=21 \ --caffemodel_path=${CAFFE_MODEL} # =========================================================================== # # VGG-based SSD network # =========================================================================== # DATASET_DIR=/media/paul/DataExt4/PascalVOC/dataset TRAIN_DIR=./logs/ssd_300_vgg_3 CHECKPOINT_PATH=./checkpoints/ssd_300_vgg.ckpt python train_ssd_network.py \ --train_dir=${TRAIN_DIR} \ --dataset_dir=${DATASET_DIR} \ --dataset_name=pascalvoc_2012 \ --dataset_split_name=train \ --model_name=ssd_300_vgg \ --checkpoint_path=${CHECKPOINT_PATH} \ --save_summaries_secs=60 \ --save_interval_secs=600 \ --weight_decay=0.0005 \ --optimizer=adam \ --learning_rate=0.001 \ --learning_rate_decay_factor=0.95 \ --batch_size=32 DATASET_DIR=/media/paul/DataExt4/PascalVOC/dataset TRAIN_DIR=./logs/ssd_300_vgg_3 EVAL_DIR=${TRAIN_DIR}/eval python eval_ssd_network.py \ --eval_dir=${EVAL_DIR} \ --dataset_dir=${DATASET_DIR} \ --dataset_name=pascalvoc_2007 \ --dataset_split_name=test \ --model_name=ssd_300_vgg \ --checkpoint_path=${TRAIN_DIR} \ --wait_for_checkpoints=True \ --batch_size=1 \ --max_num_batches=500 DATASET_DIR=/media/paul/DataExt4/PascalVOC/dataset EVAL_DIR=./logs/ssd_300_vgg_1_eval CHECKPOINT_PATH=./checkpoints/ssd_300_vgg.ckpt CHECKPOINT_PATH=./checkpoints/VGG_VOC0712_SSD_300x300_iter_120000.ckpt CHECKPOINT_PATH=./checkpoints/VGG_VOC0712_SSD_300x300_ft_iter_120000.ckpt python eval_ssd_network.py \ --eval_dir=${EVAL_DIR} \ --dataset_dir=${DATASET_DIR} \ --dataset_name=pascalvoc_2007 \ --dataset_split_name=test \ --model_name=ssd_300_vgg \ --checkpoint_path=${CHECKPOINT_PATH} \ --batch_size=1 \ --max_num_batches=10 DATASET_DIR=/media/paul/DataExt4/PascalVOC/dataset EVAL_DIR=./logs/ssd_300_vgg_1_eval CHECKPOINT_PATH=./checkpoints/VGG_VOC0712_SSD_512x512_ft_iter_120000.ckpt python eval_ssd_network.py \ --eval_dir=${EVAL_DIR} \ --dataset_dir=${DATASET_DIR} \ --dataset_name=pascalvoc_2007 \ --dataset_split_name=test \ --model_name=ssd_512_vgg \ --checkpoint_path=${CHECKPOINT_PATH} \ --batch_size=1 \ --max_num_batches=10 # =========================================================================== # # Fine tune VGG-based SSD network # =========================================================================== # DATASET_DIR=/media/paul/DataExt4/PascalVOC/dataset TRAIN_DIR=/media/paul/DataExt4/PascalVOC/training/logs/ssd_300_vgg_6 CHECKPOINT_PATH=./checkpoints/vgg_16.ckpt python train_ssd_network.py \ --train_dir=${TRAIN_DIR} \ --dataset_dir=${DATASET_DIR} \ --dataset_name=pascalvoc_2012 \ --dataset_split_name=train \ --model_name=ssd_300_vgg \ --checkpoint_path=${CHECKPOINT_PATH} \ --checkpoint_model_scope=vgg_16 \ --checkpoint_exclude_scopes=ssd_300_vgg/conv6,ssd_300_vgg/conv7,ssd_300_vgg/block8,ssd_300_vgg/block9,ssd_300_vgg/block10,ssd_300_vgg/block11,ssd_300_vgg/block4_box,ssd_300_vgg/block7_box,ssd_300_vgg/block8_box,ssd_300_vgg/block9_box,ssd_300_vgg/block10_box,ssd_300_vgg/block11_box \ --save_summaries_secs=60 \ --save_interval_secs=600 \ --weight_decay=0.0005 \ --optimizer=adam \ --learning_rate=0.001 \ --learning_rate_decay_factor=0.94 \ --batch_size=32 DATASET_DIR=/media/paul/DataExt4/PascalVOC/dataset TRAIN_DIR=/media/paul/DataExt4/PascalVOC/training/logs/ssd_300_vgg_13 CHECKPOINT_PATH=./checkpoints/vgg_16.ckpt python train_ssd_network.py \ --train_dir=${TRAIN_DIR} \ --dataset_dir=${DATASET_DIR} \ --dataset_name=pascalvoc_2012 \ --dataset_split_name=train \ --model_name=ssd_300_vgg \ --checkpoint_path=${CHECKPOINT_PATH} \ --checkpoint_model_scope=vgg_16 \ --checkpoint_exclude_scopes=ssd_300_vgg/conv6,ssd_300_vgg/conv7,ssd_300_vgg/block8,ssd_300_vgg/block9,ssd_300_vgg/block10,ssd_300_vgg/block11,ssd_300_vgg/block4_box,ssd_300_vgg/block7_box,ssd_300_vgg/block8_box,ssd_300_vgg/block9_box,ssd_300_vgg/block10_box,ssd_300_vgg/block11_box \ --trainable_scopes=ssd_300_vgg/conv6,ssd_300_vgg/conv7,ssd_300_vgg/block8,ssd_300_vgg/block9,ssd_300_vgg/block10,ssd_300_vgg/block11,ssd_300_vgg/block4_box,ssd_300_vgg/block7_box,ssd_300_vgg/block8_box,ssd_300_vgg/block9_box,ssd_300_vgg/block10_box,ssd_300_vgg/block11_box \ --save_summaries_secs=60 \ --save_interval_secs=600 \ --weight_decay=0.0005 \ --optimizer=adam \ --learning_rate=0.001 \ --learning_rate_decay_factor=0.94 \ --batch_size=32 DATASET_DIR=/media/paul/DataExt4/PascalVOC/dataset TRAIN_DIR=/media/paul/DataExt4/PascalVOC/training/logs/ssd_300_vgg_2 CHECKPOINT_PATH=./checkpoints/vgg_16.ckpt CHECKPOINT_PATH=media/paul/DataExt4/PascalVOC/training/logs/ssd_300_vgg_1/ python train_ssd_network.py \ --train_dir=${TRAIN_DIR} \ --dataset_dir=${DATASET_DIR} \ --dataset_name=pascalvoc_2012 \ --dataset_split_name=train \ --model_name=ssd_300_vgg \ --checkpoint_path=${CHECKPOINT_PATH} \ --save_summaries_secs=60 \ --save_interval_secs=600 \ --weight_decay=0.0005 \ --optimizer=adam \ --learning_rate=0.0005 \ --learning_rate_decay_factor=0.96 \ --batch_size=32 EVAL_DIR=${TRAIN_DIR}/eval python eval_ssd_network.py \ --eval_dir=${EVAL_DIR} \ --dataset_dir=${DATASET_DIR} \ --dataset_name=pascalvoc_2007 \ --dataset_split_name=test \ --model_name=ssd_300_vgg \ --checkpoint_path=${TRAIN_DIR} \ --wait_for_checkpoints=True \ --batch_size=1 # =========================================================================== # # Inception v3 # =========================================================================== # DATASET_DIR=/media/paul/DataExt4/ImageNet/Dataset DATASET_DIR=../datasets/ImageNet TRAIN_DIR=./logs/inception_v3 CHECKPOINT_PATH=/media/paul/DataExt4/ImageNet/Training/ckpts/inception_v3.ckpt CHECKPOINT_PATH=./checkpoints/inception_v3.ckpt python train_image_classifier.py \ --train_dir=${TRAIN_DIR} \ --dataset_dir=${DATASET_DIR} \ --dataset_name=imagenet \ --dataset_split_name=train \ --model_name=inception_v3 \ --checkpoint_path=${CHECKPOINT_PATH} \ --save_summaries_secs=60 \ --save_interval_secs=60 \ --weight_decay=0.00001 \ --optimizer=rmsprop \ --learning_rate=0.00005 \ --batch_size=4 CHECKPOINT_PATH=/media/paul/DataExt4/ImageNet/Training/logs CHECKPOINT_PATH=/media/paul/DataExt4/ImageNet/Training/ckpts/inception_v3.ckpt DATASET_DIR=/media/paul/DataExt4/ImageNet/Dataset python eval_image_classifier.py \ --alsologtostderr \ --checkpoint_path=${CHECKPOINT_PATH} \ --dataset_dir=${DATASET_DIR} \ --dataset_name=imagenet \ --dataset_split_name=validation \ --model_name=inception_v3 # =========================================================================== # # VGG 16 and 19 # =========================================================================== # CHECKPOINT_PATH=/media/paul/DataExt4/ImageNet/Training/ckpts/vgg_19.ckpt DATASET_DIR=/media/paul/DataExt4/ImageNet/Dataset python eval_image_classifier.py \ --alsologtostderr \ --checkpoint_path=${CHECKPOINT_PATH} \ --dataset_dir=${DATASET_DIR} \ --dataset_name=imagenet \ --labels_offset=1 \ --dataset_split_name=validation \ --model_name=vgg_19 CHECKPOINT_PATH=/media/paul/DataExt4/ImageNet/Training/ckpts/vgg_16.ckpt DATASET_DIR=/media/paul/DataExt4/ImageNet/Dataset python eval_image_classifier.py \ --alsologtostderr \ --checkpoint_path=${CHECKPOINT_PATH} \ --dataset_dir=${DATASET_DIR} \ --dataset_name=imagenet \ --labels_offset=1 \ --dataset_split_name=validation \ --model_name=vgg_16 # =========================================================================== # # Xception # =========================================================================== # DATASET_DIR=../datasets/ImageNet DATASET_DIR=/media/paul/DataExt4/ImageNet/Dataset TRAIN_DIR=./logs/xception CHECKPOINT_PATH=./checkpoints/xception_weights_tf_dim_ordering_tf_kernels.ckpt python train_image_classifier.py \ --train_dir=${TRAIN_DIR} \ --dataset_dir=${DATASET_DIR} \ --dataset_name=imagenet \ --dataset_split_name=train \ --model_name=xception \ --labels_offset=1 \ --checkpoint_path=${CHECKPOINT_PATH} \ --save_summaries_secs=600 \ --save_interval_secs=600 \ --weight_decay=0.00001 \ --optimizer=rmsprop \ --learning_rate=0.0001 \ --batch_size=32 python train_image_classifier.py \ --train_dir=${TRAIN_DIR} \ --dataset_dir=${DATASET_DIR} \ --dataset_name=imagenet \ --dataset_split_name=train \ --model_name=xception \ --labels_offset=1 \ --save_summaries_secs=60 \ --save_interval_secs=60 \ --weight_decay=0.00001 \ --optimizer=rmsprop \ --learning_rate=0.00005 \ --batch_size=1 CHECKPOINT_PATH=./checkpoints/xception_weights_tf_dim_ordering_tf_kernels.ckpt CHECKPOINT_PATH=./logs/xception CHECKPOINT_PATH=./checkpoints/xception_weights_tf_dim_ordering_tf_kernels.ckpt DATASET_DIR=../datasets/ImageNet DATASET_DIR=/media/paul/DataExt4/ImageNet/Dataset python eval_image_classifier.py \ --alsologtostderr \ --checkpoint_path=${CHECKPOINT_PATH} \ --dataset_dir=${DATASET_DIR} \ --labels_offset=1 \ --dataset_name=imagenet \ --dataset_split_name=validation \ --model_name=xception \ --max_num_batches=10 CHECKPOINT_PATH=./checkpoints/xception_weights_tf_dim_ordering_tf_kernels.h5 python ckpt_keras_to_tensorflow.py \ --model_name=xception_keras \ --num_classes=1000 \ --checkpoint_path=${CHECKPOINT_PATH} # =========================================================================== # # Dception # =========================================================================== # DATASET_DIR=../datasets/ImageNet DATASET_DIR=/media/paul/DataExt4/ImageNet/Dataset TRAIN_DIR=./logs/dception CHECKPOINT_PATH=./checkpoints/xception_weights_tf_dim_ordering_tf_kernels.ckpt python train_image_classifier.py \ --train_dir=${TRAIN_DIR} \ --dataset_dir=${DATASET_DIR} \ --dataset_name=imagenet \ --dataset_split_name=train \ --model_name=dception \ --labels_offset=1 \ --checkpoint_path=${CHECKPOINT_PATH} \ --save_summaries_secs=60 \ --save_interval_secs=60 \ --weight_decay=0.00001 \ --optimizer=rmsprop \ --learning_rate=0.00005 \ --batch_size=32 python train_image_classifier.py \ --train_dir=${TRAIN_DIR} \ --dataset_dir=${DATASET_DIR} \ --dataset_name=imagenet \ --dataset_split_name=train \ --model_name=dception \ --labels_offset=1 \ --save_summaries_secs=60 \ --save_interval_secs=60 \ --weight_decay=0.00001 \ --optimizer=rmsprop \ --learning_rate=0.00005 \ --batch_size=1 CHECKPOINT_PATH=./checkpoints/xception_weights_tf_dim_ordering_tf_kernels.ckpt CHECKPOINT_PATH=./logs/dception DATASET_DIR=../datasets/ImageNet DATASET_DIR=/media/paul/DataExt4/ImageNet/Dataset python eval_image_classifier.py \ --alsologtostderr \ --checkpoint_path=${CHECKPOINT_PATH} \ --dataset_dir=${DATASET_DIR} \ --labels_offset=1 \ --dataset_name=imagenet \ --dataset_split_name=validation \ --model_name=dception