Image Classification

Download CIFAR10 Dataset

python demo/download_data.py \
--data_url=https://s3-us-west-2.amazonaws.com/lambdalabs-files/cifar10.tar.gz \
--data_dir=~/demo/data

Train ResNet32 from scratch on CIFAR10

python demo/image/image_classification.py \
--mode=train \
--model_dir=~/demo/model/resnet32_cifar10 \
--network=resnet32 \
--augmenter=cifar_augmenter \
--batch_size_per_gpu=256 --epochs=100 \
train_args \
--learning_rate=0.5 --optimizer=momentum \
--piecewise_boundaries=50,75,90 \
--piecewise_lr_decay=1.0,0.1,0.01,0.001 \
--dataset_meta=~/demo/data/cifar10/train.csv

Evaluation

python demo/image/image_classification.py \
--mode=eval \
--model_dir=~/demo/model/resnet32_cifar10 \
--network=resnet32 \
--augmenter=cifar_augmenter \
--batch_size_per_gpu=128 --epochs=1 \
eval_args \
--dataset_meta=~/demo/data/cifar10/eval.csv

Inference

python demo/image/image_classification.py \
--mode=infer \
--model_dir=~/demo/model/resnet32_cifar10 \
--network=resnet32 \
--augmenter=cifar_augmenter \
--gpu_count=1 --batch_size_per_gpu=1 --epochs=1 \
infer_args \
--callbacks=infer_basic,infer_display_image_classification \
--test_samples=~/demo/data/cifar10/test/appaloosa_s_001975.png,~/demo/data/cifar10/test/domestic_cat_s_001598.png,~/demo/data/cifar10/test/rhea_s_000225.png,~/demo/data/cifar10/test/trucking_rig_s_001216.png

Hyper-Parameter Tuning

python demo/image/image_classification.py \
--mode=tune \
--model_dir=~/demo/model/resnet32_cifar10 \
--network=resnet32 \
--augmenter=cifar_augmenter \
--batch_size_per_gpu=128 \
tune_args \
--train_dataset_meta=~/demo/data/cifar10/train.csv \
--eval_dataset_meta=~/demo/data/cifar10/eval.csv \
--tune_config=source/tool/resnet32_cifar10_tune_coarse.yaml

python demo/image_classification.py \
--mode=tune \
--model_dir=~/demo/model/resnet32_cifar10 \
--network=resnet32 \
--augmenter=cifar_augmenter \
--batch_size_per_gpu=128 \
tune_args \
--train_dataset_meta=~/demo/data/cifar10/train.csv \
--eval_dataset_meta=~/demo/data/cifar10/eval.csv \
--tune_config=source/tool/resnet32_cifar10_tune_fine.yaml

Evaluate Pre-trained model

curl https://s3-us-west-2.amazonaws.com/lambdalabs-files/cifar10-resnet32-20180824.tar.gz | tar xvz -C ~/demo/model

python demo/image/image_classification.py \
--mode=eval \
--model_dir=~/demo/model/cifar10-resnet32-20180824 \
--network=resnet32 \
--augmenter=cifar_augmenter \
--batch_size_per_gpu=128 --epochs=1 \
eval_args \
--dataset_meta=~/demo/data/cifar10/eval.csv

Export

python demo/image/image_classification.py \
--mode=export \
--model_dir=~/demo/model/cifar10-resnet32-20180824 \
--network=resnet32 \
--augmenter=cifar_augmenter \
--gpu_count=1 --batch_size_per_gpu=1 --epochs=1 \
export_args \
--export_dir=export \
--export_version=1 \
--input_ops=input_image \
--output_ops=output_classes

Serve

docker run --runtime=nvidia -p 8501:8501 \
--name tfserving_classification \
--mount type=bind,source=model_dir/export,target=/models/classification \
-e MODEL_NAME=classification -t tensorflow/serving:latest-gpu &

python client/image_classification_client.py --image_path=path_to_image