Transfer Learning Toolkit¶
Introduction¶
CV Model Zoo¶
Conv AI Model Zoo¶
Running TLT in the Cloud¶
CV Applications¶
- Offline Data Augmentation
- Optimizing the Training Pipeline
- Data Annotation Format
- Image Classification
- Object Detection
- Instance Segmentation
- Semantic Segmentation
- Gaze Estimation
- Emotion Classification
- HeartRate Estimation
- Facial Landmarks Estimation
- Gesture Recognition
- Body Pose Estimation
- Multitask Image Classification
- Preparing the Input Data Structure
- Creating an Experiment Spec File - Specification File for Multitask Classification
- Training the model
- Evaluating the Model
- Generating Confusion Matrix
- Running Inference on a Model
- Pruning the Model
- Re-training the Pruned Model
- Exporting the model
- Deploying to DeepStream
- Character Recognition
Conversational AI Applications¶
Deploying to Inference SDKs¶
More Information¶
- Release Notes
- Frequently Asked Questions
- Troubleshooting Guide
- Support Information
- Acknowledgements
- nitime
- OpenSSL
- JsonCpp
- Python
- libcurl
- OpenCV
- zlib
- TensorFlow
- Keras
- PyTorch
- ssd_keras
- Yamale
- PyCUDA
- protobuf
- onnx
- PIL
- PyYAML
- addict
- argcomplete
- bto3
- cryptography
- docker
- dockerpty
- gRPC
- h5py
- jupyter
- numba
- numpy
- pandas
- posix_ipc
- prettytable
- arrow
- PyJWT
- requests
- retrying
- seaborn
- scikit-image
- scikit-learn
- semver
- Shapely
- simplejson
- six
- python-tabulate
- toposort
- tqdm
- uplink
- xmltodict
- recordclass
- cocoapi
- mpi4py
- Open MPI
- lazy_object_proxy
- onnxruntime
- pytorch-lightning