Can we make computer vision like our eyes?
$ python main.py --config=config/dataset/pascal_voc.cfg
--app=dataset
--dataset_name=voc
$ FILE_PATH = ~/dataset/test.jpg
$ python main.py --config=config/vgg/vgg_16.cfg
--gpu = True
--app=classifier
--task=classify
--file=${FILE_PATH} or --file_url=${FILE_PATH}
DATASET_DIR=cache/dataset/imagenet
LOG_DIR=cache/log/vgg_16
python train_classifier.py --model_name=vgg_16
--log_dir=${LOG_DIR}
--dataset_dir=${DATASET_DIR}
--dataset_name=imagenet
--dataset_split_name=train
$ DATASET_DIR=cache/dataset/imagenet
$ LOG_DIR=cache/log/vgg_16
$ CHECKPOINT_PATH=cache/weight/vgg_16.ckpt
$ python train_classifier.py --model_name=vgg_16
--log_dir=${LOG_DIR}
--dataset_dir=${DATASET_DIR}
--dataset_name=imagenet
--dataset_split_name=train
--checkpoint_path=${CHECKPOINT_PATH}
From deepcv/
$ protoc model/detection/protos/*.proto --python_out=.
$ FILE_PATH = ~/dataset/test.jpg
$ python main.py --config=config/ssd/ssd_v1.cfg \
--app=detector \
--task=detect \
--file=$FILE_PATH
$ PIPELINE_CONFIG_PATH = config/train_detection/faster_rcnn_resnet101_voc007.config
$ python main.py --log_dir=cache/log/faster_rcnn/resnet/voc007
--pipeline_config_path = ${PIPELINE_CONFIG_PATH}
$ LOGDIR = cache/log/ssd/mobilenet_v1/pet
$tensorboard --logdir=${LOGDIR}
Model | TF-Slim File | Checkpoint | Top-1 Accuracy | Top-5 Accuracy |
---|---|---|---|---|
Inception V1 | Code | inception_v1_2016_08_28.tar.gz | 69.8 | 89.6 |
Inception V2 | Code | inception_v2_2016_08_28.tar.gz | 73.9 | 91.8 |
Inception V3 | Code | inception_v3_2016_08_28.tar.gz | 78.0 | 93.9 |
Inception V4 | Code | inception_v4_2016_09_09.tar.gz | 80.2 | 95.2 |
Inception-ResNet-v2 | Code | inception_resnet_v2_2016_08_30.tar.gz | 80.4 | 95.3 |
ResNet 50 | Code | resnet_v1_50_2016_08_28.tar.gz | 75.2 | 92.2 |
ResNet 101 | Code | resnet_v1_101_2016_08_28.tar.gz | 76.4 | 92.9 |
ResNet 152 | Code | resnet_v1_152_2016_08_28.tar.gz | 76.8 | 93.2 |
ResNet V2 200 | Code | TBA | 79.9* | 95.2* |
VGG 16 | Code | vgg_16_2016_08_28.tar.gz | 71.5 | 89.8 |
VGG 19 | Code | vgg_19_2016_08_28.tar.gz | 71.1 | 89.8 |
Choose the right MobileNet model to fit your latency and size budget. The size of the network in memory and on disk is proportional to the number of parameters. The latency and power usage of the network scales with the number of Multiply-Accumulates (MACs) which measures the number of fused Multiplication and Addition operations. These MobileNet models have been trained on the ILSVRC-2012-CLS image classification dataset. Accuracies were computed by evaluating using a single image crop.
Model Checkpoint | Million MACs | Million Parameters | Top-1 Accuracy | Top-5 Accuracy |
---|---|---|---|---|
MobileNet_v1_1.0_224 | 569 | 4.24 | 70.7 | 89.5 |
MobileNet_v1_1.0_192 | 418 | 4.24 | 69.3 | 88.9 |
MobileNet_v1_1.0_160 | 291 | 4.24 | 67.2 | 87.5 |
MobileNet_v1_1.0_128 | 186 | 4.24 | 64.1 | 85.3 |
MobileNet_v1_0.75_224 | 317 | 2.59 | 68.4 | 88.2 |
MobileNet_v1_0.75_192 | 233 | 2.59 | 67.4 | 87.3 |
MobileNet_v1_0.75_160 | 162 | 2.59 | 65.2 | 86.1 |
MobileNet_v1_0.75_128 | 104 | 2.59 | 61.8 | 83.6 |
MobileNet_v1_0.50_224 | 150 | 1.34 | 64.0 | 85.4 |
MobileNet_v1_0.50_192 | 110 | 1.34 | 62.1 | 84.0 |
MobileNet_v1_0.50_160 | 77 | 1.34 | 59.9 | 82.5 |
MobileNet_v1_0.50_128 | 49 | 1.34 | 56.2 | 79.6 |
MobileNet_v1_0.25_224 | 41 | 0.47 | 50.6 | 75.0 |
MobileNet_v1_0.25_192 | 34 | 0.47 | 49.0 | 73.6 |
MobileNet_v1_0.25_160 | 21 | 0.47 | 46.0 | 70.7 |
MobileNet_v1_0.25_128 | 14 | 0.47 | 41.3 | 66.2 |