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SSD_Tensorflow-1.x/COMMANDS.md

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Initial commit Originally from https://github.com/balancap/SSD-Tensorflow
5 years ago
# =========================================================================== #
# 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