• Hardware V100
• Network Type LPRnet
• TAO Version
Configuration of the TAO Toolkit Instance
dockers:
nvidia/tao/tao-toolkit-tf:
v3.21.11-tf1.15.5-py3:
docker_registry: nvcr.io
tasks:
1. augment
2. bpnet
3. classification
4. dssd
5. emotionnet
6. efficientdet
7. fpenet
8. gazenet
9. gesturenet
10. heartratenet
11. lprnet
12. mask_rcnn
13. multitask_classification
14. retinanet
15. ssd
16. unet
17. yolo_v3
18. yolo_v4
19. yolo_v4_tiny
20. converter
v3.21.11-tf1.15.4-py3:
docker_registry: nvcr.io
tasks:
1. detectnet_v2
2. faster_rcnn
nvidia/tao/tao-toolkit-pyt:
v3.21.11-py3:
docker_registry: nvcr.io
tasks:
1. speech_to_text
2. speech_to_text_citrinet
3. text_classification
4. question_answering
5. token_classification
6. intent_slot_classification
7. punctuation_and_capitalization
8. spectro_gen
9. vocoder
10. action_recognition
nvidia/tao/tao-toolkit-lm:
v3.21.08-py3:
docker_registry: nvcr.io
tasks:
1. n_gram
format_version: 2.0
toolkit_version: 3.21.11
published_date: 11/08/2021
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Before LPRnet was supported on TAO I was using a PyTorch version from here
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The repository mentions STN layer which greatly improves the accuracy, however onnx doesn’t supports exporting the STN layer.
Since TAO officially supports LPRnet now I want to know if the STN layer is being used?
Thanks