Visual ChangeNet-Segmentation Model Card - MVTec-AD (Research-only)

Model Overview

The model described in this model card is designed for the segmentation of industrial defects using the MVTec-AD dataset. The inputs consist of a pair of co-registered images, namely a golden image and a test image, both obtained from the MVTec-AD dataset. These images capture the same component, that is, a transistor. The model aims to output a binary segmentation change map that highlights the presence of defects in the post-change test image by comparing it to the corresponding golden image.

Model Architecture

Visual ChangeNet is a state of the art transformer-based change detection model. Visual ChangeNet is based on the Siamese Network, which is a class of neural network architectures containing two or more identical subnetworks. The training algorithm works by updating the parameters across all the sub-networks in tandem. In TAO, Visual ChangeNet supports two images as input where the end goal is to either classify or segment the change between the \"golden or reference\" image and the \"test\" image. TAO supports the FAN backbone network for both Visual ChangeNet architectures. For more details about training FAN backbones, see the Pre-trained FAN based ImageNet Classification. In TAO, two different types of change detection networks are supported:

Visual ChangeNet-Segmentation - for segmentation of change between two input images.
Visual ChangeNet-Classification - for classification of change between two input images.

Visual ChangeNet-Segmentation is specifically intended for change segmentation. In this model card, the Visual ChangeNet-Segmentation model is leveraged to demonstrate industrial defect detection using MVTec-AD dataset. The model uses a pretrained FAN backbone, trained on NVImageNet dataset, and then fine-tunes on the MVTec-AD dataset for the transistor class.

Training

This model was trained using the visual_changenet entrypoint in TAO. The training algorithm optimizes the network to minimize the cross-entropy loss for every pixel of the mask.

Training Data

Visual ChangeNet-Segmentation model was trained on open-source MVTec-AD dataset. The MVTec Anomaly dataset provides images of 15 different objects. For each object, the dataset contains non-defective images (golden images) and defective images with a pixel perfect segmentation mask of the defect region.

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Dataset	No. of Images
MVTec-AD (transistor)	303

The dataset is randomly split into 212 training and 91 validation splits. The dataset is additionally pre-processed to prepare in the correct format as expected by the Visual ChangeNet-Segmentation model. For more details on the dataset pre-processing, see the Visual ChangeNet-Segmentation Notebook (MVTec).

The following is a sample image showing a changed scenario:

\n $\"transistor$ \n

Performance

Evaluation Data

The model performance was evaluated on a validation dataset, which had a total of 91 images.

Methodology and KPI

The performance of the Visual ChangeNet-Segmentation model is mainly measured using overall accuracy and precision, recall and IoU score for the change class.

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Model	Model Architecture	Testing Images	Precision	Recall	IoU	F1	Overall Accuracy
Visual ChangeNet-Segmentation	Siamese Network	91	94.5	89.8	86.15	92	99.57

Real-Time Inference Performance

The inference is run on the provided unpruned model at FP16 precision. The inference performance is run using trtexec on Jetson AGX Xavier, Xavier NX, Orin, Orin NX and NVIDIA T4, and Ampere GPUs. The Jetson devices are running at Max-N configuration for maximum GPU frequency. The performance shown here is the inference only performance. The end-to-end performance with streaming video data might vary depending on other bottlenecks in the hardware and software.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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Platform	BS	FPS
Orin Nano 8GB	16	15.19
Orin NX 16GB	16	21.92
AGX Orin 64GB	16	55.07
A2	16	36.02
T4	16	59.7
L4	8	131.48
A30	16	204.12
L40	8	364
A100	32	435.18
H100	32	841.68

How to Use this Model

These models need to be used with NVIDIA hardware and software. For hardware, the models can run on any NVIDIA GPU including NVIDIA Jetson devices. These models can only be used with Train Adapt Optimize (TAO) Toolkit, or TensorRT.

The primary use case intended for these models is for Visual ChangeNet-Segmentation using RGB component level images. The model is a Siamese Network, which outputs binary change maps denoting pixel-level change between the two images, denoting the defects.

These models are intended for training and fine-tune using the TAO Toolkit and your datasets for image comparison. High-fidelity models can be trained on new use cases. A Jupyter Notebook is available as a part of the TAO container and can be used for re-training. The Notebook can be found at: Visual ChangeNet-Segmentation Notebook (MVTec).

The models are also intended for edge deployment using TensorRT.

Input

Two input images that meet the following characteristics:

Golden Image: RGB Image of dimensions: 256 X 256 X 3 (H x W x C)

Test Image: RGB Image of dimensions: 256 X 256 X 3 (H x W x C)

Channel Ordering of the Input: NCHW, where N = Batch Size, C = number of channels (3), H = Height of images (256), W = Width of the images (256)

Output

Segmentation change map with the same spatial resolution as the input images: 256 X 256 X 2 (H x W x C), where C = number of output change classes.

Input Image

The following is a sample image for golden and test images along with ground-truth segmentation change map side-by-side denoting the defect:

\n $\"transistor$ \n

Using the Model with TAO

To use these models as pretrained weights for transfer learning, use the the following snippet of code as a template for the model and train components of the experiment spec file to train a Siamese Network model. For more information on the experiment spec file, see the TAO Toolkit User Guide - Visual ChangeNet-Segmentation.

model:\n  backbone:\n    type: \"fan_small_12_p4_hybrid\"\n    pretrained_backbone_path: null\nevaluate:\n    model_path: \"???\"\n

Limitations

Expecting Co-Registered Remote Sensing Imagery

The Visual ChangeNet-Segmentation Network model was trained on pair-wise co-registered images for the transistor class and might not perform well for mis-aligned image pairs or for a different object types.

Model Versions

trainable_v1.0 - FAN-Hybrid Base Visual ChangeNet-Segmentation model MVTec trainable.
deployable_v1.0 - FAN-Hybrid Base Visual ChangeNet-Segmentation model MVTec deployable to deepstream.

References

Using TAO Pre-Trained Models

Get TAO Container
Get other purpose-built models from the NGC model registry:\n
- TrafficCamNet
- PeopleNet
- PeopleNet
- PeopleNet-Transformer
- DashCamNet
- FaceDetectIR
- VehicleMakeNet
- VehicleTypeNet
- PeopleSegNet
- PeopleSemSegNet
- License Plate Detection
- License Plate Recognition
- Gaze Estimation
- Facial Landmark
- Heart Rate Estimation
- Gesture Recognition
- Emotion Recognition
- FaceDetect
- 2D Body Pose Estimation
- ActionRecognitionNet
- ActionRecognitionNet
- PoseClassificationNet
- People ReIdentification
- PointPillarNet
- CitySegFormer
- Retail Object Detection
- Retail Object Embedding
- Optical Inspection
- Optical Character Detection
- Optical Character Recognition
- PCB Classification
- PeopleSemSegFormer
- LPDNet
- License Plate Recognition
- Gaze Estimation
- Facial Landmark
- Heart Rate Estimation
- Gesture Recognition
- Emotion Recognition
- FaceDetect
- 2D Body Pose Estimation
- ActionRecognitionNet
- ActionRecognitionNet
- PoseClassificationNet
- People ReIdentification
- PointPillarNet
- CitySegFormer
- Retail Object Detection
- Retail Object Embedding
- Optical Inspection
- Optical Character Detection
- Optical Character Recognition
- PCB Classification
- PeopleSemSegFormer
\n

Technical Blogs

Learn how to transform Industrial Defect Detection with NVIDIA TAO and Vision AI Models
Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - Part 1 | Part 2
Learn how to train real-time License plate detection and recognition app with TAO and DeepStream.
Model accuracy is extremely important, learn how you can achieve state of the art accuracy for classification and object detection models using TAO
Learn how to train Instance segmentation model using MaskRCNN with TAO
Read the technical tutorial on how PeopleNet model can be trained with custom data using Transfer Learning Toolkit
Learn how to train and deploy real-time intelligent video analytics apps and services using DeepStream SDK

License

License to use this model is covered by CC-BY-NC-SA-4.0. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of these licenses.

Ethical Considerations

NVIDIA Visual ChangeNet-Segmentation model detects changes between pair-wise images. 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.

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The inputs consist of a pair of co-registered images, namely a golden image and a test image, both obtained from the MVTec-AD dataset. These images capture the same component, that is, a transistor. The model aims to output a binary segmentation change map that highlights the presence of defects in the post-change test image by comparing it to the corresponding golden image.\n\n## Model Architecture \n\n\nVisual ChangeNet is a state of the art transformer-based change detection model. Visual ChangeNet is based on the Siamese Network, which is a class of neural network architectures containing two or more identical subnetworks. The training algorithm works by updating the parameters across all the sub-networks in tandem. In TAO, Visual ChangeNet supports two images as input where the end goal is to either classify or segment the change between the \"golden or reference\" image and the \"test\" image. TAO supports the [FAN](https://arxiv.org/abs/2204.12451) backbone network for both Visual ChangeNet architectures. For more details about training FAN backbones, see the [Pre-trained FAN based ImageNet Classification](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_fan_classification_imagenet). In TAO, two different types of change detection networks are supported: \n\n* **Visual ChangeNet-Segmentation** - for segmentation of change between two input images.\n* **Visual ChangeNet-Classification** - for classification of change between two input images.\n\nVisual ChangeNet-Segmentation is specifically intended for change segmentation. In this model card, the Visual ChangeNet-Segmentation model is leveraged to demonstrate industrial defect detection using MVTec-AD dataset. The model uses a pretrained FAN backbone, trained on NVImageNet dataset, and then fine-tunes on the MVTec-AD dataset for the transistor class.\n\n## Training \n\nThis model was trained using the `visual_changenet` entrypoint in TAO. The training algorithm optimizes the network to minimize the cross-entropy loss for every pixel of the mask.\n\n### Training Data \n\nVisual ChangeNet-Segmentation model was trained on open-source MVTec-AD dataset. The MVTec Anomaly dataset provides images of 15 different objects. For each object, the dataset contains non-defective images (golden images) and defective images with a pixel perfect segmentation mask of the defect region.\n\n| Dataset | No. of Images |\n|--|--|\n|MVTec-AD (transistor)| 303 | \n\nThe dataset is randomly split into 212 training and 91 validation splits. The dataset is additionally pre-processed to prepare in the correct format as expected by the Visual ChangeNet-Segmentation model. For more details on the dataset pre-processing, see the [Visual ChangeNet-Segmentation Notebook (MVTec)](https://github.com/NVIDIA/tao_tutorials/blob/main/notebooks/tao_launcher_starter_kit/visual_changenet/visual_changenet_segmentation_MVTec.ipynb).\n\nThe following is a sample image showing a changed scenario:\n\n $\"transistor$ \n\n\n## Performance\n\n### Evaluation Data \n\nThe model performance was evaluated on a validation dataset, which had a total of 91 images.\n\n#### Methodology and KPI\n\nThe performance of the Visual ChangeNet-Segmentation model is mainly measured using overall accuracy and precision, recall and IoU score for the change class.\n\n| Model | Model Architecture | Testing Images | Precision | Recall | IoU | F1 | Overall Accuracy |\n|--|--|--|--|--|--|--|--|\n| Visual ChangeNet-Segmentation | Siamese Network | 91 | 94.5 | 89.8 | 86.15 | 92 | 99.57 |\n\n\n### Real-Time Inference Performance \n\nThe inference is run on the provided unpruned model at FP16 precision. The inference performance is run using [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/main/samples/trtexec) on Jetson AGX Xavier, Xavier NX, Orin, Orin NX and NVIDIA T4, and Ampere GPUs. The Jetson devices are running at Max-N configuration for maximum GPU frequency. The performance shown here is the inference only performance. The end-to-end performance with streaming video data might vary depending on other bottlenecks in the hardware and software.\n\n| Platform | BS | FPS |\n|------------------|-----|--------|\n| Orin Nano 8GB | 16 | 15.19 |\n| Orin NX 16GB | 16 | 21.92 |\n| AGX Orin 64GB | 16 | 55.07 |\n| A2 | 16 | 36.02 |\n| T4 | 16 | 59.7 |\n| L4 | 8 | 131.48 |\n| A30 | 16 | 204.12 |\n| L40 | 8 | 364 |\n| A100 | 32 | 435.18 |\n| H100 | 32 | 841.68 |\n\n## How to Use this Model \n\nThese models need to be used with NVIDIA hardware and software. For hardware, the models can run on any NVIDIA GPU including NVIDIA Jetson devices. These models can only be used with [Train Adapt Optimize (TAO) Toolkit](https://developer.nvidia.com/tao-toolkit), or [TensorRT](https://developer.nvidia.com/tensorrt).\n\nThe primary use case intended for these models is for Visual ChangeNet-Segmentation using RGB component level images. The model is a Siamese Network, which outputs binary change maps denoting pixel-level change between the two images, denoting the defects. \n\nThese models are intended for training and fine-tune using the TAO Toolkit and your datasets for image comparison. High-fidelity models can be trained on new use cases. A Jupyter Notebook is available as a part of the [TAO container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit) and can be used for re-training. The Notebook can be found at: [Visual ChangeNet-Segmentation Notebook (MVTec)](https://github.com/NVIDIA/tao_tutorials/blob/main/notebooks/tao_launcher_starter_kit/visual_changenet/visual_changenet_segmentation_MVTec.ipynb).\n\nThe models are also intended for edge deployment using TensorRT. \n\n### Input\n\nTwo input images that meet the following characteristics:\n\n **Golden Image**: RGB Image of dimensions: 256 X 256 X 3 (H x W x C)\n\n **Test Image**: RGB Image of dimensions: 256 X 256 X 3 (H x W x C)\n\n**Channel Ordering of the Input**: NCHW, where N = Batch Size, C = number of channels (3), H = Height of images (256), W = Width of the images (256)\n\n### Output\n\nSegmentation change map with the same spatial resolution as the input images: 256 X 256 X 2 (H x W x C), where C = number of output change classes. \n\n#### Input Image\n\nThe following is a sample image for golden and test images along with ground-truth segmentation change map side-by-side denoting the defect:\n\n $\"transistor$ \n\n### Using the Model with TAO\n\nTo use these models as pretrained weights for transfer learning, use the the following snippet of code as a template for the `model` and `train` components of the experiment spec file to train a Siamese Network model. For more information on the experiment spec file, see the [TAO Toolkit User Guide - Visual ChangeNet-Segmentation](https://gitlab-master.nvidia.com/tlt/tlt-docs/-/blob/master/docs/text/visual_changenet/visual_changenet_segment.rst).\n\n```yaml\nmodel:\n backbone:\n type: \"fan_small_12_p4_hybrid\"\n pretrained_backbone_path: null\nevaluate:\n model_path: \"???\"\n```\n\n\n## Limitations \n\n### Expecting Co-Registered Remote Sensing Imagery\n\nThe Visual ChangeNet-Segmentation Network model was trained on pair-wise co-registered images for the transistor class and might not perform well for mis-aligned image pairs or for a different object types. \n\n## Model Versions\n\n- **trainable_v1.0** - FAN-Hybrid Base Visual ChangeNet-Segmentation model MVTec trainable.\n- **deployable_v1.0** - FAN-Hybrid Base Visual ChangeNet-Segmentation model MVTec deployable to deepstream.\n\n## References\n\n### Using TAO Pre-Trained Models \n\n- Get [TAO Container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit)\n- Get other purpose-built models from the NGC model registry:\n - [TrafficCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:trafficcamnet)\n - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)\n - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)\n - [PeopleNet-Transformer](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet_transformer)\n - [DashCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:dashcamnet)\n - [FaceDetectIR](https://ngc.nvidia.com/catalog/models/nvidia:tao:facedetectir)\n - [VehicleMakeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehiclemakenet)\n - [VehicleTypeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehicletypenet)\n - [PeopleSegNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplesegnet)\n - [PeopleSemSegNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplesemsegnet)\n - [License Plate Detection](https://ngc.nvidia.com/catalog/models/nvidia:tao:lpdnet)\n - [License Plate Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:lprnet)\n - [Gaze Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:gazenet)\n - [Facial Landmark](https://ngc.nvidia.com/catalog/models/nvidia:tao:fpenet)\n - [Heart Rate Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:heartratenet)\n - [Gesture Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:gesturenet)\n - [Emotion Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:emotionnet)\n - [FaceDetect](https://ngc.nvidia.com/catalog/models/nvidia:tao:facenet)\n - [2D Body Pose Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:bodyposenet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [PoseClassificationNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:poseclassificationnet)\n - [People ReIdentification](https://ngc.nvidia.com/catalog/models/nvidia:tao:reidentificationnet)\n - [PointPillarNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pointpillarnet)\n - [CitySegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/citysemsegformer)\n - [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection)\n - [Retail Object Embedding](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_recognition)\n - [Optical Inspection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/optical_inspection)\n - [Optical Character Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocdnet)\n - [Optical Character Recognition](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocrnet)\n - [PCB Classification](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pcb_classification)\n - [PeopleSemSegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplesemsegformer)\n - [LPDNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:lpdnet)\n - [License Plate Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:lprnet)\n - [Gaze Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:gazenet)\n - [Facial Landmark](https://ngc.nvidia.com/catalog/models/nvidia:tao:fpenet)\n - [Heart Rate Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:heartratenet)\n - [Gesture Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:gesturenet)\n - [Emotion Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:emotionnet)\n - [FaceDetect](https://ngc.nvidia.com/catalog/models/nvidia:tao:facenet)\n - [2D Body Pose Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:bodyposenet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [PoseClassificationNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:poseclassificationnet)\n - [People ReIdentification](https://ngc.nvidia.com/catalog/models/nvidia:tao:reidentificationnet)\n - [PointPillarNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pointpillarnet)\n - [CitySegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/citysemsegformer)\n - [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection)\n - [Retail Object Embedding](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_recognition)\n - [Optical Inspection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/optical_inspection)\n - [Optical Character Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocdnet)\n - [Optical Character Recognition](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocrnet)\n - [PCB Classification](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pcb_classification)\n - [PeopleSemSegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplesemsegformer)\n\n### Technical Blogs \n\n- Learn how to transform [Industrial Defect Detection with NVIDIA TAO and Vision AI Models](https://developer.nvidia.com/blog/transforming-industrial-defect-detection-with-nvidia-tao-and-vision-ai-models/)\n- Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - [Part 1](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-1) | [Part 2](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-2)\n- 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 TAO and DeepStream.\n- 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-tao-toolkit/) using TAO\n- Learn how to train [Instance segmentation model using MaskRCNN with TAO](https://developer.nvidia.com/blog/training-instance-segmentation-models-using-maskrcnn-on-tao-toolkit/)\n- 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/)\n- 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/)\n\n### Suggested Reading \n\n- More information on about TAO Toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/tao-toolkit)\n- Read the [TAO Quick Start](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html) guide and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html).\n- If you have any questions or feedback, see the discussions on [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17)\n- Deploy your model on the edge using DeepStream. Learn more about [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk)\n\n## License \n\nLicense to use this model is covered by CC-BY-NC-SA-4.0. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of these [licenses](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en).\n\n\n## Ethical Considerations \n\nNVIDIA Visual ChangeNet-Segmentation model detects changes between pair-wise images. 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.","Visual ChangeNet Segmentation - MvTEC",["D",1702361804771],"/api/image/nvidia/tao/models/visual_changenet_segmentation_mvtec","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png",651624255,"bias","explainability","privacy","safetyAndSecurity",[],[286,287,288,289,290,291,292],[],[],[],{"_91":144,"_128":145,"_129":305,"_131":132},{"_91":303,"_128":303,"_129":304,"_131":132},{"_91":301,"_128":301,"_129":302,"_131":132},{"_91":299,"_128":299,"_129":300,"_131":132},{"_91":297,"_128":297,"_129":298,"_131":132},{"_91":92,"_128":92,"_129":296,"_131":132},{"_91":293,"_128":293,"_129":294,"_131":132},"TAO Toolkit",{"_133":295,"_128":293},"framework",{"_133":132,"_128":92},"Optical Inspection",{"_133":132,"_128":297},"Industrial Inspection",{"_133":132,"_128":299},"Deep Learning",{"_133":132,"_128":301},"CV",{"_133":132,"_128":303},{"_133":137,"_128":145},{"_176":1475,"_178":1476},"ngcApps",[1428],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1409":1410,"_1411":1412,"_1413":1414,"_1415":1416,"_1417":317,"_208":341,"_1418":1419,"_1420":51,"_1421":155,"_1422":155,"_1423":1424,"_1425":155,"_1426":1427},"amplitude",{"_1406":1407,"_1013":-7,"_1408":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1400":1401,"_1402":1403,"_1404":1405},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1288":235,"_1289":1290,"_1291":1292,"_1293":1294},"announcements",[],"consentState",{"_378":1278,"_1279":155,"_1280":26,"_1281":26,"_1282":1283},"consentConfig",{"_360":361,"_362":363,"_364":365,"_366":367,"_368":369,"_370":371,"_372":373,"_374":375,"_376":377,"_378":379},"showContactAdmin",1784119650040,[],[],[355,356,357,358,359],{"_40":54,"_42":65,"_44":171,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":63,"_44":171,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":59,"_44":171,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":171,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":171,"_46":-5,"_48":-5,"_50":51},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563],[402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418],{"_419":525,"_91":525,"_90":235,"_392":526},{"_419":520,"_91":520,"_90":235,"_392":521},{"_419":516,"_91":516,"_90":235,"_392":517},{"_419":511,"_91":511,"_90":235,"_392":512},{"_419":506,"_91":506,"_90":235,"_392":507},{"_419":494,"_91":494,"_90":235,"_392":495},{"_419":482,"_91":482,"_90":235,"_392":483},{"_419":477,"_91":477,"_90":235,"_392":478},{"_419":466,"_91":466,"_90":235,"_392":467},{"_419":461,"_91":461,"_90":235,"_392":462},{"_419":456,"_91":456,"_90":235,"_392":457},{"_419":451,"_91":451,"_90":235,"_392":452},{"_419":446,"_91":446,"_90":235,"_392":447},{"_419":441,"_91":441,"_90":235,"_392":442},{"_419":436,"_91":436,"_90":235,"_392":437},{"_419":429,"_91":429,"_90":235,"_392":430},{"_419":420,"_91":420,"_90":235,"_392":421},"hostName","amazon-adsystem.com",[422,423],{"_263":428,"_425":420,"_426":26,"_90":427},{"_263":424,"_425":420,"_426":26,"_90":427},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[431,432],{"_263":435,"_425":429,"_426":26,"_90":434},{"_263":433,"_425":429,"_426":26,"_90":434},"IDE","This domain is owned by Doubleclick (Google). 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. 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These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672],[617,618,619,620,621],{"_419":511,"_91":511,"_90":235,"_392":643},{"_419":477,"_91":477,"_90":235,"_392":638},{"_419":631,"_91":631,"_90":235,"_392":632},{"_419":627,"_91":627,"_90":235,"_392":628},{"_419":622,"_91":622,"_90":235,"_392":623},"nvcrm.my.site.com",[624],{"_263":625,"_425":622,"_426":26,"_90":626},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[629],{"_263":630,"_425":627,"_426":26,"_90":460},"edge","github.com",[633,634],{"_263":637,"_425":631,"_426":26,"_90":636},{"_263":635,"_425":631,"_426":26,"_90":636},"logged_in","This domain is owned by Github. 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