## What is Transfer Learning Toolkit? <a name="introduction" class="anchor"></a>

[Transfer Learning Toolkit](https://developer.nvidia.com/transfer-learning-toolkit) (TLT) is a python based AI toolkit for taking purpose-built pre-trained AI models and customizing them with your own data. TLT adapts popular network architectures and backbones to your data, allowing you to train, fine tune, prune and export highly optimized and accurate AI models for edge deployment.

The pre-trained models accelerate the AI training process and reduce costs associated with large scale data collection, labeling, and training models from scratch. Transfer learning with pre-trained models can be used for AI applications in smart cities, retail, healthcare, industrial inspection and more.

Build end-to-end services and solutions for transforming pixels and sensor data to actionable insights using TLT, [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) and [TensorRT](https://developer.nvidia.com/tensorrt). The models are suitable for object detection, classification and segmentation.

### Semantic Segmentation Using TLT <a name="semantic_segmentation_using_tlt" class="anchor"></a>

Semantic segmentation assigns every pixel in an image to a class label. Semantic segmentation does image classification at pixel level. Unlike instance segmentation which can label individual instances belonging to a class, semantic segmentation clubs all instances of a class to same label. This model card contains pretrained weights that may be used as a starting point with the following semantic segmentation networks in Transfer Learning Toolkit (TLT) to facilitate transfer learning.

Following semantic segmentation architecture are supported:

- UNet

The pre-trained weights are trained on a subset of the Google OpenImages dataset. Following backbones are supported with these UNet networks.

- resnet10/resnet18/resnet34/resnet50/resnet101
- vgg16/vgg19

To see the full list of all the backbones, scroll over to the version history tab.

***Note:*** These are unpruned models with just the feature extractor weights, and may not be used without re-training in an semantic segmentation application

### Training Semantic Segmentation Models Using TLT <a name="running_object_detection_models_using_tlt" class="anchor"></a>

The semantic segmentation apps in TLT expect mask data as encoded images with every pixel assigned to the class label. TLT provides a simple command line interface to train a deep learning model for semantic segmentation.

The models in this model area are only compatible with Transfer Learning Toolkit. For more information about using this model with TLT, please view the instructions [here](https://docs.nvidia.com/metropolis/TLT/tlt-user-guide/text/tlt_launcher.html) to install the TLT Launcher CLI and use with the [semantic segmentation](https://docs.nvidia.com/metropolis/TLT/tlt-user-guide/text/semantic_segmentation/index.html) trainer in TLT.

Before running the container, use docker pull to ensure an up-to-date image is installed. Once the pull is complete, you can run the container image.

1. Install the NGC CLI from `ngc.nvidia.com`

2. Configure the NGC CLI using the following command

```sh
ngc config set
```

3. To view all the backbones that are supported by Instance segmentation architecture in TLT:

   ```sh
   ngc registry model list nvidia/tlt_semantic_segmentation:*
   ```
4. Download the model:

   ```sh
   ngc registry model download-version nvidia/tlt_semantic_segmentation:<template> --dest <path>
   ```

#### Instructions to run the sample notebook

1. Get the NGC API key from the SETUP tab on the left. Please store this key for future use. Detailed instructions can be found [here](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html#generating-api-key)

2. Configure the NGC command line interface using the command mentioned below and follow the prompts.

   ```sh
   ngc config set
   ```
3. Download the sample notebooks from NGC using the command below

   ```sh
   ngc registry resource download-version "nvidia/tlt_cv_samples:v1.0.2"
   ```
4. Invoke the jupyter notebook using the following command

   ```sh
   jupyter notebook --ip 0.0.0.0 --port 8888 --allow-root
   ```
5. Open an internet browser and type in the following URL to start running the notebooks when running on a local machine.

   ```sh
   http://0.0.0.0:8888
   ```

   If you wish to run view the notebook from a remote client, please modify the URL as follows:

   ```sh
   http://a.b.c.d:8888
   ```

   Where, the `a.b.c.d` is the IP address of the machine running the container.

### Other TLT Pre-trained Models <a name="other_tlt_pretrained_models" class="anchor"></a>

- Get [TLT Object Detection](https://ngc.nvidia.com/catalog/models/nvidia:tlt_pretrained_object_detection) pre-trained models for **YOLOV3, YOLOV4, FasterRCNN, SSD, DSSD, and RetinaNet** architectures from NGC model registry
- Get [TLT DetectNet_v2 Object Detection](https://ngc.nvidia.com/catalog/models/nvidia:tlt_pretrained_detectnet_v2) pre-trained models for **DetectNet_v2** architecture from NGC model registry
- Get [TLT classification](https://ngc.nvidia.com/catalog/models/nvidia:tlt_pretrained_classification) pre-trained models from NGC model registry
- Get Purpose-built models from NGC model registry:
  - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tlt_peoplenet)
  - [TrafficCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tlt_trafficcamnet)
  - [DashCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tlt_dashcamnet)
  - [FaceDetectIR](https://ngc.nvidia.com/catalog/models/nvidia:tlt_facedetectir)
  - [VehicleMakeNet](https://ngc.nvidia.com/catalog/models/nvidia:tlt_vehiclemakenet)
  - [VehicleTypeNet](https://ngc.nvidia.com/catalog/models/nvidia:tlt_vehicletypenet)

### License <a name="license" class="anchor"></a>

This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, please visit this [link](http://creativecommons.org/licenses/by/4.0/),  or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

### Technical blogs <a name="technical_blogs" class="anchor"></a>

- Read the 2 part blog on training and optimizing 2D body pose estimation model with TLT - [Part 1](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tlt-part-1)  |  [Part 2](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tlt-part-2)
- Learn how to train [real-time License plate detection and recognition app](https://developer.nvidia.com/blog/creating-a-real-time-license-plate-detection-and-recognition-app) with TLT and DeepStream.
- Model accuracy is extremely important, learn how you can achieve [state of the art accuracy for classification and object detection models](https://developer.nvidia.com/blog/preparing-state-of-the-art-models-for-classification-and-object-detection-with-tlt/) using TLT
- Learn how to train [Instance segmentation model using MaskRCNN with TLT](https://developer.nvidia.com/blog/training-instance-segmentation-models-using-maskrcnn-on-the-transfer-learning-toolkit/)
- Learn how to improve INT8 accuracy using [Quantization aware training(QAT) with TLT](https://developer.nvidia.com/blog/improving-int8-accuracy-using-quantization-aware-training-and-the-transfer-learning-toolkit/)
- Read the technical tutorial on how [PeopleNet model can be trained with custom data using Transfer Learning Toolkit](https://devblogs.nvidia.com/training-custom-pretrained-models-using-tlt/)
- Learn how to [train and deploy real-time intelligent video analytics apps and services using DeepStream SDK](https://devblogs.nvidia.com/building-iva-apps-using-deepstream-5.0/)

### Suggested reading <a name="suggested_reading" class="anchor"></a>

- More information on about Transfer learning toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/transfer-learning-toolkit)
- Read the [TLT getting Started](https://docs.nvidia.com/tlt/tlt-user-guide/text/tlt_quick_start_guide.html) guide and [release notes](https://docs.nvidia.com/tlt/tlt-user-guide/text/release_notes.html).
- If you have any questions or feedback, please refer to the discussions on [TLT Developer Forums](https://devtalk.nvidia.com/default/board/417/transfer-learning-toolkit/)
- Deploy your model on the edge using DeepStream. Learn more about [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk)

### Ethical AI <a name="ethical_ai" class="anchor"></a>

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.


tlt_semantic_segmentation

Pretrained weights to facilitate transfer learning using Transfer Learning Toolkit.

TLT Pretrained Semantic Segmentation

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