A program that trains ImageNet and test results.
Contains the implementation of the ResNeXt and Inceotion_Resnet_v2.
Contains a pre-training ResNeXt.
train model:
usage: train_model.py [-h] [--batch_size BATCH_SIZE]
[--pretrained_model PRETRAINED_MODEL] [--epochs EPOCHS]
[--CUDA_VISIBLE_DEVICES CUDA_VISIBLE_DEVICES] [--epoch_size EPOCH_SIZE]
[--lr_schedule_file LR_SCHEDULE_FILE]
[--model_name MODEL_NAME] [--display_step DISPLAY_STEP]
[--evaluate_step EVALUATE_STEP]
[--logs_base_dir LOGS_BASE_DIR]
[--models_base_dir MODELS_BASE_DIR]
[--nrof_class NROF_CLASS] [--finetuning FINETUNING]
[--dic_loc DIC_LOC] [--only_weight ONLY_WEIGHT]
[--model_def MODEL_DEF]
optional arguments:
-h, --help show this help message and exit
--batch_size BATCH_SIZE
Number of images to process in a batch.
--pretrained_model PRETRAINED_MODEL
Load a pretrained model before training starts.
--epochs EPOCHS Number of epochs to run.
--CUDA_VISIBLE_DEVICES CUDA_VISIBLE_DEVICES
CUDA VISIBLE DEVICES
--epoch_size EPOCH_SIZE
Number of batches per epoch.
--lr_schedule_file LR_SCHEDULE_FILE
File containing the learning rate schedule
--model_name MODEL_NAME
--display_step DISPLAY_STEP
Step size showing the training situation
--evaluate_step EVALUATE_STEP
Step size showing verification
--logs_base_dir LOGS_BASE_DIR
Directory where to write event logs.
--models_base_dir MODELS_BASE_DIR
Directory where to write trained models and
checkpoints.
--nrof_class NROF_CLASS
Number of class category.
--finetuning FINETUNING
Whether finetuning.
--dic_loc DIC_LOC Subclass dictionary location.
--only_weight ONLY_WEIGHT
Whether only load pretrained model's weight.
--model_def MODEL_DEF
Model definition. Points to a module containing the
definition of the inference graph.
test model:
usage: test_model.py [-h] [--pretrained_model PRETRAINED_MODEL]
[--CUDA_VISIBLE_DEVICES CUDA_VISIBLE_DEVICES]
[--model_name MODEL_NAME] [--data_dir DATA_DIR]
[--nrof_class NROF_CLASS] [--crop_type CROP_TYPE] [--k K]
[--model_def MODEL_DEF]
optional arguments:
-h, --help show this help message and exit
--pretrained_model PRETRAINED_MODEL
Load a pretrained model before training starts.
--CUDA_VISIBLE_DEVICES CUDA_VISIBLE_DEVICES
CUDA VISIBLE DEVICES
--model_name MODEL_NAME
--data_dir DATA_DIR Where to save the data.
--nrof_class NROF_CLASS
Number of class category.
--crop_type CROP_TYPE
Crop type for test.
--k K k of random k crop.
--model_def MODEL_DEF
Model definition. Points to a module containing the
definition of the inference graph.