AI Models - Computer Vision, Conversational AI, and More

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Many AI applications have common needs: classification, object detection, language translation, text-to-speech, recommender engines, sentiment analysis, and more. When developing applications with these capabilities, it is much faster to start with a model that is pre-trained and then tune it for a specific use case. The NGC catalog offers pre-trained models for a variety of common AI tasks that are optimized for NVIDIA Tensor Core GPUs, and can be easily re-trained by updating just a few layers, saving valuable time.

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Bi3D Proximity Segmentation

Model

Bi3D is a binary depth classification network that is used to classify the depth of objects at a given distance.

ESS DNN Stereo Disparity

Model

ESS is a DNN that estimates disparity for a stereo image pair and returns a continuous disparity map for the given left image.

TTS En Multispeaker FastPitch HiFiGAN

Model

This collection contains two models: 1) Multi-speaker FastPitch (around 50M parameters) trained on HiFiTTS with over 291.6 hours of english speech and 10 speakers. 2) HiFiGAN trained on mel spectrograms produced by the Multi-speaker FastPitch in (1).

STT En Conformer-CTC Large

Model

Conformer-CTC-Large model for English Automatic Speech Recognition, Trained on NeMo ASRSET

STT En Conformer-Transducer Large

Model

Conformer-Transducer-Large model for English Automatic Speech Recognition, Trained on NeMo ASRSET

NER En Bert

Model

Named Entity Recognition model with BERT

PeopleSemSegnet

Model

Semantic segmentation of persons in an image.

Riva ASR English Inverse Normalization Grammar

Model

Base English grammar

DashCamNet

Model

4 class object detection network to detect cars in an image.

RIVA Conformer ASR Hindi

Model

Hindi Conformer ASR model trained on ASR set 1.0

Riva ASR Russian LM

Model

Base Russian 4-gram LM

English Tagger-based Inverse Text Normalization

Model

English single-pass tagger-based model for inverse text normalization based on bert-base-uncased, trained on 2 mln sentences from Google Text Normalization Dataset, achieves 3.75% WER on Google default test set

Efficient Geometry-aware 3D Generative Adversarial Networks

Model

Pretrained EG3D Models for FFHQ, AFHQ, and Shapenet Cars

TAO Pretrained Object Detection

Model

Pretrained weights to facilitate transfer learning using TAO Toolkit.

SSL En Conformer Large

Model

Self-Supervised Learning (SSL) checkpoints for Conformer Large model. These are similar to w2v-Conformer model and can be fine-tuned for Automatic Speech Recognition (ASR).

STT En Conformer-CTC XLarge

Model

Conformer-CTC-XLarge model for English Automatic Speech Recognition, Trained on NeMo ASRSET

STT En Conformer-Transducer XLarge

Model

Conformer-Transducer-XLarge model for English Automatic Speech Recognition, trained on NeMo ASRSET

SSL En Conformer XLarge

Model

Self-Supervised Learning (SSL) checkpoints for Conformer XLarge model. These are similar to w2v-Conformer model and can be fine-tuned for Automatic Speech Recognition (ASR).

PeopleNet

Model

3 class object detection network to detect people in an image.

Riva ASR Mandarin LM

Model

Base Mandarin 4-gram LM

TrafficCamNet

Model

4 class object detection network to detect cars in an image.

PeopleSegNet

Model

1 class instance segmentation network to detect and segment instances of people in an image.

LPDNet

Model

Object Detection network to detect license plates in an image of a car.

Riva TTS English US Auxiliary Files

Model

Contains files used in rmir creation

Riva TTS English Normalization Grammar

Model

Base English grammar