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for nvidia transfer learning
IntroductionNVIDIA Transfer Learning Toolkit (TLT) is a simple, easy-to-use training toolkit that requires minimal to zero coding to create vision AI models using the user’s own data. This cheatsheet is created by Ness, version 2020.11.14. |
| | tlt-augment-d /path/to/the/dataset/root | -a /path/to/augmentation/spec/file | -o /path/to/the/augmented/output | [-v] |
tlt-dataset-convert-d DATASET_EXPORT_SPEC | -o OUTPUT_FILENAME | [-f VALIDATION_FOLD] |
| | tlt-trainclassification --gpus <num GPUs> | -k <encoding key> | -r <result directory> | -e <spec file> |
tlt-evaluate classification-e <experiment_spec_file> | -k <key> |
tlt-evaluate detectnet_v2-e <experiment_spec_file> | -m <model_file> | -k <key> | [--use_training_set] |
tlt-prunetlt-prune [-h]
-pm <pretrained_model>
-o <output_file> -k <key>
[-n <normalizer>]
[-eq <equalization_criterion>]
[-pg <pruning_granularity>]
[-pth <pruning threshold>]
[-nf <min_num_filters>]
[-el [<excluded_list>] |
| | tlt-int8-tensorfiletlt-int8-tensorfile {classification, detectnet_v2} [-h]
-e <path to training experiment spec file>
-o <path to output tensorfile>
-m <maximum number of batches to serialize>
[--use_validation_set] |
tlt-exporttlt-export [-h] {classification, detectnet_v2, ssd, dssd, faster_rcnn, yolo, retinanet}
-m <path to the .tlt model file generated by tlt train>
-k <key>
[-o <path to output file>]
[--cal_data_file <path to tensor file>]
[--cal_image_dir <path to the directory images to calibrate the model]
[--cal_cache_file <path to output calibration file>]
[--data_type <Data type for the TensorRT backend during export>]
[--batches <Number of batches to calibrate over>]
[--max_batch_size <maximum trt batch size>]
[--max_workspace_size <maximum workspace size]
[--batch_size <batch size to TensorRT engine>]
[--experiment_spec <path to experiment spec file>]
[--engine_file <path to the TensorRT engine file>]
[--verbose Verbosity of the logger]
[--force_ptq Flag to force PTQ] |
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