Retail Object Detection

Description:

RetailObjectDetection detects retail items within an image; it classifies objects as retail or not.

This model is ready for commercial use.

References:

Citations

Tobin, Josh, et al. \"Domain randomization for transferring deep neural networks from simulation to the real world.\" 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, 2017.
Morrical, Nathan, et al. \"NViSII: A scriptable tool for photorealistic image generation.\" arXiv preprint arXiv:2105.13962 (2021).

Using TAO Pre-trained Models

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

Model Architecture:

Architecture Type: Convolution Neural Network (CNN)
\nNetwork Architecture: EfficientDet, DINO-FAN_base

Input:

Input Type(s): Image
\nInput Format(s): Red, Green, Blue (RGB)
\nInput Parameters: 2D
\nOther Properties Related to Input: RGB Fixed Resolution: 416x416 and 960x544 (W x H); No minimum bit depth, alpha, or gamma.

The logos on retail items were smudged.

Output:

Output Type(s): Label(s)
\nOutput Format: Label: Text String
\nOther Properties Related to Output: Category Label(s): returns a single category.

The logos on retail items were smudged.

Software Integration:

Runtime Engine(s):

TAO - 5.2
DeepStream 6.1 or later

Supported Hardware Architecture(s):

Ampere
Jetson
Hopper
Lovelace
Pascal
Turing
Volta

Supported Operating System(s):

Linux
Linux 4 Tegra

Model Version(s):

trainable_binary_v1.0
deployable_binary_v1.0

Training & Evaluation:

Training Dataset:

Data Collection Method by dataset:

Automatic/Sensors

Labeling Method by dataset:

Human

Properties:
\n320,000 proprietary synthetic images of objects found in retail settings randomizing several simulation domains including:

light types, light intensities
object sizes, orientations, and locations
camera locations
background textures
flying distractors

The background textures are real images sampled from:

Proprietary real images
images taken from a retail checkout counter
HDRI texture maps create by NVIDIA Omniverse

Each synthetic image contains 1 target retail item. This dataset is set up to simulate the diverse environments in the real world and to have the detector learn to extract retail items from noisy backgrounds. The logos on retail items were smudged.

\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

tr:nth-child(odd)]:bg-surface-sunken\">

dataset	total #images	train #images	val #images
Synthetic data	1,500,000	1,425,000	75,000
Real data - checkout counter 45 overhead	107	85	23
Real data - shelf	107	85	22
Real data - conveyor belt	106	84	22
Real data - basket	106	84	22
Real data - checkout counter barcode scanner view	125	100	25
Real data - checkout counter overhead	98	80	18

Fine-tuning Data

This model is fine-tuned on about 600 real proprietary images from 6 different real environments. In each environment, only 1 image per item is collected.

The fine tuning data are captured under random camera heights and field of views. All fine tuning data were collected indoor, having retail items placed at the checkout counter, shelf, baskets, and conveyor belt. The camera is typically set up at approximately 10 feet height, 45-degree angle off the vertical axis and has close field-of-view. This content was chosen to decrease the simulation-to-reality gap of the model trained on synthetic data, and to improve the accuracy and the robustness of the model. The logos on retail items were smudged.

Fine-tuning Data Ground-truth Labeling Guidelines

The fine tuning data are created by labeling ground-truth bounding-boxes and categories by human-labelers. The following guidelines were used while labeling the training data for NVIDIA Retail Detection models. If you are looking to transfer-learn or to fine-tune the models to adapt to your target environment and classes, please follow the guidelines below for better model accuracy.

All objects that fall under the definition of retail items and are larger than the smallest bounding-box limit for the corresponding class (height >= 10px OR width >= 10px) are labeled with the appropriate class label.
Occlusion: For partially occluded objects that are visible approximately 60% or are marked as visible objects with a bounding box around the visible part of the object. These objects are marked as partially occluded. Objects under 60% visibility are not annotated.
Truncation: An object, at the edge of the frame, which is 60% or more visible is marked with the truncation flag.
Each frame is not required to have an object.

Evaluation Dataset:

Data Collection Method by dataset:

Automatic/Sensors

Labeling Method by dataset:

Human

Properties:
\n15,000 proprietary real-world images of objects found in retail settings.

Methodology and KPI

AP50 is calculated using intersection-over-union (IOU) criterion greater than 0.5. The KPI for the evaluation data are reported in the table below. Model is evaluated based on AP50 and AR0.5:0.95. Both AR and AP numbers are based on 100 maximum detections each image. Please note that “unseen items” measurements are irrelevant to the 100-class detection model.

Binary-class Retail Item Detection Model

\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\n

tr:nth-child(odd)]:bg-surface-sunken\">

scene	seen items result (AP50)	seen items result (AR MaxDets=100)	unseen items result (AP50)	unseen items result (AR MaxDets=100)
checkout counter 45 degree overhead	0.960	0.791	0.959	0.753
shelf	0.983	0.888	0.978	0.841
conveyor belt	1.000	0.921	0.995	0.887
basket	0.956	0.851	0.959	0.861
checkout counter barcode scanner view	0.858	0.789	0.744	0.655
checkout counter overhead	0.990	0.915	0.993	0.910
overall (mean of all scenes)	0.959	0.859	0.938	0.818

Inference:

Engine: Tensor(RT)
\nTest Hardware:

Jetson AGX Xavier
Xavier NX
Orin
Orin NX
NVIDIA T4
Ampere GPU
A2
A30
L4
T4
DGX H100
DGX A100
DGX H100
L40
JAO 64GB
Orin NX16GB
Orin Nano 8GB

The inference is run on the provided unpruned model at FP16 precision. The model input resolution is 416x416. The inference performance is run using trtexec on Jetson AGX Orin 64GB and A10. The performance shown here is the inference only performance. The end-to-end performance with streaming video data might slightly 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

tr:nth-child(odd)]:bg-surface-sunken\">

model	device	batch size	latency (ms)	images per second
Retail Item Detection (binary)	Jetson AGX Orin 64GB	1	10.43	96
Retail Item Detection (binary)	Jetson AGX Orin 64GB	16	131.79	121
Retail Item Detection (binary)	Jetson AGX Orin 64GB	32	258.44	124
Retail Item Detection (binary)	Tesla A10	1	4.27	234
Retail Item Detection (binary)	Tesla A10	16	44.94	356
Retail Item Detection (binary)	Tesla A10	64	174.46	367

How to use this model

Instructions to use unpruned model with TAO

In order to use these models as pretrained weights for transfer learning, please use the snippet below as template for the model component of the experiment spec file to train a Efficientdet-TF2 model. For more information on the experiment spec file, please refer to the [RetailDetector notebook and the EfficientDdet-TFtf2 TAO doc].

% spec file\nmodel:\n    name: 'efficientdet-d5'\ndata:\n    loader:\n      prefetch_size: 4\n      shuffle_file: True\n    num_classes: 101 # switch to 2 for RetailDetector_binary model\n    image_size: '416x416'\n    max_instances_per_image: 10\n    train_tfrecords:\n       - [train tfrecords]\n    val_tfrecords:\n       - [validation tfrecords]\n    val_json_file: [validation annotation json file path]\ntrain: \n    num_examples_per_epoch: 10000 # change to train set size\n    ...\n\nevaluate:\n    num_samples: 500 # change to test set size\n    label_map: # label map file here\n    ...\n

Instructions to deploy these models with DeepStream

Here is an example of using the Retail Item Embedder together with the Retail Item Detector [TODO: add Retail Item Embedder url here] for an end-to-end video analytic application. To do so, deploy these models with DeepStream SDK. DeepStream SDK is a streaming analytic toolkit to accelerate building AI-based video analytic applications. DeepStream supports direct integration of these models into the deepstream sample app.

To deploy these models with DeepStream 6.2, please follow the instructions below:

Download and install DeepStream SDK. The installation instructions for DeepStream are provided in DeepStream development guide. The config files for the purpose-built models are located in:

/opt/nvidia/deepstream is the default DeepStream installation directory. This path will be different if you are installing in a different directory.

The sample config files are provided in NVIDIA-AI-IOT(TODO: Update the URL when deepstream_tao_apps are merged with???). Assume the repo is cloned under $DS_TAO_APPS_HOME, in $DS_TAO_APPS_HOME/configs/retailDetector_tao,

# Binary-class detector (the primary GIE) inference setting \npgie_retailDetector_binary_config.yml\npgie_retailDetector_binary_config.txt\n

Key Parameters in pgie_retailDetector_100_tao_config.yml

property:\n  gpu-id: 0\n  net-scale-factor: 1\n  offsets: 0;0;0\n  model-color-format: 0\n  tlt-model-key: nvidia_tlt\n  tlt-encoded-model: ../../models/retailDetector/retailDetector_100.etlt\n  model-engine-file: ../../models/retailDetector/retailDetector_100.etlt_b1_gpu0_fp16.engine\n  labelfile-path:    ../../models/retailDetector/retailDetector_100_labels.txt\n  network-input-order: 1\n  infer-dims: 3;416;416\n  maintain-aspect-ratio: 1\n  batch-size: 1\n  ## 0=FP32, 1=INT8, 2=FP16 mode\n  network-mode: 2\n  num-detected-classes: 100\n  interval: 0\n  cluster-mode: 3\n  output-blob-names: num_detections;detection_boxes;detection_scores;detection_classes\n  parse-bbox-func-name: NvDsInferParseCustomEfficientDetTAO\n  custom-lib-path: ../../post_processor/libnvds_infercustomparser_tao.so\n#Use the config params below for NMS clustering mode\nclass-attrs-all:\n  pre-cluster-threshold: 0.5\n

In order to decode the bounding box information from the EfficientDet output tensor, the custom parser function and library have to be specified. To inference the model, please run:

cd $DS_TAO_APPS_HOME/configs/retailDetector_tao\n$DS_TAO_APPS_HOME/apps/tao_detection/ds-tao-detection -c retailDetector_100_config.txt -i file://$DS_TAO_APPS_HOME/samples/streams/retailDetector_h264.mp4\n

The \"Deploying to DeepStream\" chapter of TAO User Guide provides more details.

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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

Ethical Considerations:

NVIDIA Retail Object Detection model detects retail items. However, no additional information such as people and other distractors in the background are inferred. Training and evaluation dataset mostly consists of North American content. An ideal training and evaluation dataset would additionally include content from other geographies.

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Promise and the Explainability, Bias, Safety & Security, and Privacy Subcards.

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(deployable_retail_object_detection_binary_v2.2.2.3)","nvidia/tao/retail_object_detection","key","nvidia/tao/models/retail_object_detection","resourceType","models","resourceId","retail_object_detection","shortDescription","DINO (DETR with Improved DeNoising Anchor Boxes) based object detection network to detect retail objects on a checkout counter.","# Retail Object Detection\n\n## Description:\nRetailObjectDetection detects retail items within an image; it classifies objects as retail or not. \n\nThis model is ready for commercial use. \n\n## References:\n\n### Citations \n- Tobin, Josh, et al. \"Domain randomization for transferring deep neural networks from simulation to the real world.\" 2017 *IEEE/RSJ international conference on intelligent robots and systems (IROS)*. IEEE, 2017.\n- Morrical, Nathan, et al. \"NViSII: A scriptable tool for photorealistic image generation.\" *arXiv preprint arXiv:2105.13962 (2021)*.\n\n### Using TAO Pre-trained Models \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-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 - [PoseClassificationNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:poseclassificationnet)\n - [Facial Landmark](https://ngc.nvidia.com/catalog/models/nvidia:tao:fpenet)\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 - [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\n## Model Architecture: \n**Architecture Type:** Convolution Neural Network (CNN)
\n**Network Architecture:** EfficientDet, DINO-FAN_base
\n\n## Input:\n**Input Type(s):** Image
\n**Input Format(s):** Red, Green, Blue (RGB)
\n**Input Parameters:** 2D
\n**Other Properties Related to Input:** RGB Fixed Resolution: 416x416 and 960x544 (W x H); No minimum bit depth, alpha, or gamma.
\n\nThe logos on retail items were smudged.\n\n

\n\n## Output:\n**Output Type(s):** Label(s)
\n**Output Format:** Label: Text String
\n**Other Properties Related to Output:** Category Label(s): returns a single category.
\n\nThe logos on retail items were smudged.\n\n

\n\n## Software Integration:\n**Runtime Engine(s):** \n* TAO - 5.2
\n* DeepStream 6.1 or later
\n\n**Supported Hardware Architecture(s):**
\n* Ampere
\n* Jetson
\n* Hopper
\n* Lovelace
\n* Pascal
\n* Turing
\n* Volta
\n\n**Supported Operating System(s):**
\n* Linux
\n* Linux 4 Tegra
\n\n## Model Version(s): \n- trainable_binary_v1.0\n- deployable_binary_v1.0\n\n## Training & Evaluation: \n\n## Training Dataset:\n**Data Collection Method by dataset:**
\n* Automatic/Sensors
\n\n**Labeling Method by dataset:**
\n* Human
\n\n**Properties:**
\n320,000 proprietary synthetic images of objects found in retail settings randomizing several simulation domains including:\n- light types, light intensities\n- object sizes, orientations, and locations\n- camera locations\n- background textures\n- flying distractors\n\nThe background textures are real images sampled from:\n\n- Proprietary real images \n- images taken from a retail checkout counter \n- HDRI texture maps create by NVIDIA Omniverse\n\nEach synthetic image contains 1 target retail item. This dataset is set up to simulate the diverse environments in the real world and to have the detector learn to extract retail items from noisy backgrounds. The logos on retail items were smudged.\n\n

\n\n| dataset | total #images | train #images | val #images |\n| ------------------------------------------------- | ------------- | ------------- | ----------- |\n| Synthetic data | 1,500,000 | 1,425,000 | 75,000 |\n| Real data - checkout counter 45 overhead | 107 | 85 | 23 |\n| Real data - shelf | 107 | 85 | 22 |\n| Real data - conveyor belt | 106 | 84 | 22 |\n| Real data - basket | 106 | 84 | 22 |\n| Real data - checkout counter barcode scanner view | 125 | 100 | 25 |\n| Real data - checkout counter overhead | 98 | 80 | 18 |\n\n#### Fine-tuning Data\n\nThis model is fine-tuned on about 600 real proprietary images from 6 different real environments. In each environment, only 1 image per item is collected. \n\nThe fine tuning data are captured under random camera heights and field of views. All fine tuning data were collected indoor, having retail items placed at the checkout counter, shelf, baskets, and conveyor belt. The camera is typically set up at approximately 10 feet height, 45-degree angle off the vertical axis and has close field-of-view. This content was chosen to decrease the simulation-to-reality gap of the model trained on synthetic data, and to improve the accuracy and the robustness of the model. The logos on retail items were smudged.\n\n

\n\n#### Fine-tuning Data Ground-truth Labeling Guidelines\n\nThe fine tuning data are created by labeling ground-truth bounding-boxes and categories by human-labelers. The following guidelines were used while labeling the training data for NVIDIA Retail Detection models. If you are looking to transfer-learn or to fine-tune the models to adapt to your target environment and classes, please follow the guidelines below for better model accuracy.\n\n1. All objects that fall under the definition of retail items and are larger than the smallest bounding-box limit for the corresponding class (height >= 10px OR width >= 10px) are labeled with the appropriate class label.\n2. Occlusion: For partially occluded objects that are visible approximately 60% or are marked as visible objects with a bounding box around the visible part of the object. These objects are marked as partially occluded. Objects under 60% visibility are not annotated.\n3. Truncation: An object, at the edge of the frame, which is 60% or more visible is marked with the truncation flag.\n4. Each frame is not required to have an object.\n\n## Evaluation Dataset:\n**Data Collection Method by dataset:**
\n* Automatic/Sensors
\n\n**Labeling Method by dataset:**
\n* Human
\n\n**Properties:**
\n15,000 proprietary real-world images of objects found in retail settings.\n\n#### Methodology and KPI\n\nAP50 is calculated using intersection-over-union (IOU) criterion greater than 0.5. The KPI for the evaluation data are reported in the table below. Model is evaluated based on AP50 and AR0.5:0.95. Both AR and AP numbers are based on 100 maximum detections each image. Please note that “unseen items” measurements are irrelevant to the 100-class detection model.\n\n#### Binary-class Retail Item Detection Model\n\n| scene | seen items result (AP50) | seen items result (AR MaxDets=100) | unseen items result (AP50) | unseen items result (AR MaxDets=100) |\n| ------------------------------------- | ------------------------ | ---------------------------------- | --------------------------- | ------------------------------------- |\n| checkout counter 45 degree overhead | 0.960 | 0.791 | 0.959 | 0.753 |\n| shelf | 0.983 | 0.888 | 0.978 | 0.841 |\n| conveyor belt | 1.000 | 0.921 | 0.995 | 0.887 |\n| basket | 0.956 | 0.851 | 0.959 | 0.861 |\n| checkout counter barcode scanner view | 0.858 | 0.789 | 0.744 | 0.655 |\n| checkout counter overhead | 0.990 | 0.915 | 0.993 | 0.910 |\n| **overall (mean of all scenes)** | **0.959** | **0.859** | **0.938** | **0.818** |\n\n\n## Inference:\n**Engine:** Tensor(RT)
\n**Test Hardware:**
\n- Jetson AGX Xavier\n- Xavier NX\n- Orin\n- Orin NX\n- NVIDIA T4\n- Ampere GPU\n- A2\n- A30\n- L4\n- T4\n- DGX H100\n- DGX A100\n- DGX H100\n- L40\n- JAO 64GB\n- Orin NX16GB\n- Orin Nano 8GB\n\nThe inference is run on the provided unpruned model at FP16 precision. The model input resolution is 416x416. The inference performance is run using [trtexec](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#trtexec-ovr) on Jetson AGX Orin 64GB and A10. The performance shown here is the inference only performance. The end-to-end performance with streaming video data might slightly vary depending on other bottlenecks in the hardware and software.\n\n| model | device | batch size | latency (ms) | images per second |\n| --------------------------------- | -------------------- | ---------- | ------------ | ----------------- |\n| Retail Item Detection (binary) | Jetson AGX Orin 64GB | 1 | 10.43 | 96 |\n| Retail Item Detection (binary) | Jetson AGX Orin 64GB | 16 | 131.79 | 121 |\n| Retail Item Detection (binary) | Jetson AGX Orin 64GB | 32 | 258.44 | 124 |\n| Retail Item Detection (binary) | Tesla A10 | 1 | 4.27 | 234 |\n| Retail Item Detection (binary) | Tesla A10 | 16 | 44.94 | 356 |\n| Retail Item Detection (binary) | Tesla A10 | 64 | 174.46 | 367 |\n\n## How to use this model \n### Instructions to use unpruned model with TAO\n\nIn order to use these models as pretrained weights for transfer learning, please use the snippet below as template for the model component of the experiment spec file to train a Efficientdet-TF2 model. For more information on the experiment spec file, please refer to the [RetailDetector notebook and the EfficientDdet-TFtf2 TAO doc].\n\n```\n% spec file\nmodel:\n name: 'efficientdet-d5'\ndata:\n loader:\n prefetch_size: 4\n shuffle_file: True\n num_classes: 101 # switch to 2 for RetailDetector_binary model\n image_size: '416x416'\n max_instances_per_image: 10\n train_tfrecords:\n - [train tfrecords]\n val_tfrecords:\n - [validation tfrecords]\n val_json_file: [validation annotation json file path]\ntrain: \n num_examples_per_epoch: 10000 # change to train set size\n ...\n\nevaluate:\n num_samples: 500 # change to test set size\n label_map: # label map file here\n ...\n```\n### Instructions to deploy these models with DeepStream\n\nHere is an example of using the Retail Item Embedder together with the Retail Item Detector [TODO: add Retail Item Embedder url here] for an end-to-end video analytic application. To do so, deploy these models with [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk). DeepStream SDK is a streaming analytic toolkit to accelerate building AI-based video analytic applications. DeepStream supports direct integration of these models into the deepstream sample app.\n\nTo deploy these models with [DeepStream 6.2](https://deepstreamsdk.gitlab-master-pages.nvidia.com/docs-template/text/DS_ref_app_deepstream.html?highlight=attach), please follow the instructions below:\n\n[Download](https://developer.nvidia.com/deepstream-sdk) and install DeepStream SDK. The installation instructions for DeepStream are provided in [DeepStream development guide](https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html). The config files for the purpose-built models are located in:\n\n/opt/nvidia/deepstream is the default DeepStream installation directory. This path will be different if you are installing in a different directory.\n\nThe sample config files are provided in NVIDIA-AI-IOT(TODO: Update the URL when deepstream_tao_apps are merged with???). Assume the repo is cloned under `$DS_TAO_APPS_HOME`, in `$DS_TAO_APPS_HOME/configs/retailDetector_tao`,\n\n```bash\n# Binary-class detector (the primary GIE) inference setting \npgie_retailDetector_binary_config.yml\npgie_retailDetector_binary_config.txt\n```\n\nKey Parameters in `pgie_retailDetector_100_tao_config.yml`\n\n```prototxt\nproperty:\n gpu-id: 0\n net-scale-factor: 1\n offsets: 0;0;0\n model-color-format: 0\n tlt-model-key: nvidia_tlt\n tlt-encoded-model: ../../models/retailDetector/retailDetector_100.etlt\n model-engine-file: ../../models/retailDetector/retailDetector_100.etlt_b1_gpu0_fp16.engine\n labelfile-path: ../../models/retailDetector/retailDetector_100_labels.txt\n network-input-order: 1\n infer-dims: 3;416;416\n maintain-aspect-ratio: 1\n batch-size: 1\n ## 0=FP32, 1=INT8, 2=FP16 mode\n network-mode: 2\n num-detected-classes: 100\n interval: 0\n cluster-mode: 3\n output-blob-names: num_detections;detection_boxes;detection_scores;detection_classes\n parse-bbox-func-name: NvDsInferParseCustomEfficientDetTAO\n custom-lib-path: ../../post_processor/libnvds_infercustomparser_tao.so\n#Use the config params below for NMS clustering mode\nclass-attrs-all:\n pre-cluster-threshold: 0.5\n```\n\nIn order to decode the bounding box information from the EfficientDet output tensor, the custom parser function and library have to be specified. To inference the model, please run:\n\n```bash\ncd $DS_TAO_APPS_HOME/configs/retailDetector_tao\n$DS_TAO_APPS_HOME/apps/tao_detection/ds-tao-detection -c retailDetector_100_config.txt -i file://$DS_TAO_APPS_HOME/samples/streams/retailDetector_h264.mp4\n```\nThe \"Deploying to DeepStream\" chapter of [TAO User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html) provides more details.\n\n### Technical blogs \n\n- [Access the latest in Vision AI development workflows with NVIDIA TAO Toolkit 5.0](https://developer.nvidia.com/blog/access-the-latest-in-vision-ai-model-development-workflows-with-nvidia-tao-toolkit-5-0/)\n- [Improve accuracy and robustness of vision ai models with vision transformers and NVIDIA TAO](https://developer.nvidia.com/blog/improve-accuracy-and-robustness-of-vision-ai-apps-with-vision-transformers-and-nvidia-tao/)\n- [Train like a ‘pro’ without being an AI expert using TAO AutoML](https://developer.nvidia.com/blog/training-like-an-ai-pro-using-tao-automl/)\n- [Create Custom AI models using NVIDIA TAO Toolkit with Azure Machine Learning ](https://developer.nvidia.com/blog/creating-custom-ai-models-using-nvidia-tao-toolkit-with-azure-machine-learning/)\n- [Developing and Deploying AI-powered Robots with NVIDIA Isaac Sim and NVIDIA TAO](https://developer.nvidia.com/blog/developing-and-deploying-ai-powered-robots-with-nvidia-isaac-sim-and-nvidia-tao/)\n- Learn endless ways to adapt and supercharge your AI workflows with TAO - [Whitepaper](https://developer.nvidia.com/tao-toolkit-usecases-whitepaper/1-introduction)\n- [Customize Action Recognition with TAO and deploy with DeepStream](https://developer.nvidia.com/blog/developing-and-deploying-your-custom-action-recognition-application-without-any-ai-expertise-using-tao-and-deepstream/)\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\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- [TAO documentation](https://docs.nvidia.com/tao/tao-toolkit/index.html)\n- Read the [TAO getting Started](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, please refer to the discussions on [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17)\n- Deploy your models for video analytics application using DeepStream. Learn more about [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk)\n- Deploy your models in [Riva](https://developer.nvidia.com/riva) for ConvAI use case.\n\n## Ethical Considerations:\nNVIDIA Retail Object Detection model detects retail items. However, no additional information such as people and other distractors in the background are inferred. Training and evaluation dataset mostly consists of North American content. An ideal training and evaluation dataset would additionally include content from other geographies. \n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Promise and the Explainability, Bias, Safety & Security, and Privacy Subcards.","displayName","Retail Object Detection",["D",1724043826262],"publisher","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/retail_object_detection","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",314107341,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","| Field | Response |\n| -- | -- |\n|Participation considerations from adversely impacted groups [(protected classes)](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | Unknown |\n| Measures taken to mitigate against unwanted bias: | No measures taken to mitigate against unwanted bias.|","explainability","| Field | Response |\n| -- | -- |\n| Intended Application(s) & Domain(s): | Detecting retail objects |\n| Model Type: | Object Detection |\n| Intended Users: | Developers working in smart spaces, retail, and industrial applications with people. |\n| Output: | Bounding Box(es), Category Label(s), Confidence Score(s) |\n| Describe how the model works: | Detects retail items within an image and classifies object as retail item or not. |\n| Technical Limitations: | Model should not be used in low-light, low contrast lighting conditions, with warped or motion-induced images, or very small and very large objects. Model is not as accurate with monochrome or infrared images. Model accuracy may vary with occlusion or truncation. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Intersection over Union (IoU), Mean Average Precision (mAP) |\n| Potential Known Risks: | This model may fail to detect a retail object. |\n| Licensing: | [https://developer.download.nvidia.com/licenses/tao_toolkit_21-08_models_eula.pdf?t=eyJscyI6ImdzZW8iLCJsc2QiOiJodHRwczovL3d3dy5nb29nbGUuY29tLyJ9](https://developer.download.nvidia.com/licenses/tao_toolkit_21-08_models_eula.pdf?t=eyJscyI6ImdzZW8iLCJsc2QiOiJodHRwczovL3d3dy5nb29nbGUuY29tLyJ9) |","privacy","| Field | Response |\n| -- | -- |\n| Generatable or reverse engineerable personally-identifiable information (PII)? | None |\n| Protected classes used to create this model? | None |\n| Was consent obtained for any PII used? | Yes |\n| How often is dataset reviewed? | \tBefore Release |\n| Is a mechanism in place to honor data subject right of access or deletion of personal data? | Yes |\n| If PII collected for the development of the model, was it collected directly by NVIDIA? | Yes, PII was collected by NVIDIA. |\n| If PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects?\t| Yes |\n| If PII collected for the development of this AI model, was it minimized to only what was required? | Yes |\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | Yes |\n| Applicable NVIDIA Privacy Policy\t| [https://www.nvidia.com/en-us/about-nvidia/privacy-policy/](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) |","safetyAndSecurity","| Field | Response |\n| -- | -- |\n| Model Application(s): | Object Detection at checkout counters |\n| Describe the life-critical application (if present). | Not Applicable |\n| Use Case Restrictions: | Abide by https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/ |\n| Describe access restrictions (if any): | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |","featureLabels",[],"displayLabels",[342,343,344,345,346,347,348,349,350,351,352,353,354],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_306":390,"_356":391,"_357":392,"_359":360},{"_306":386,"_356":387,"_357":388,"_359":360},{"_306":384,"_356":384,"_357":385,"_359":360},{"_306":382,"_356":382,"_357":383,"_359":360},{"_306":379,"_356":380,"_357":381,"_359":360},{"_306":375,"_356":376,"_357":377,"_359":360},{"_306":373,"_356":373,"_357":374,"_359":360},{"_306":371,"_356":371,"_357":372,"_359":360},{"_306":369,"_356":369,"_357":370,"_359":360},{"_306":366,"_356":366,"_357":367,"_359":360},{"_306":364,"_356":364,"_357":365,"_359":360},{"_306":362,"_356":362,"_357":363,"_359":360},{"_306":355,"_356":355,"_357":358,"_359":360},"Transfer Learning Toolkit","value","filter",{"_361":360,"_356":355},"category","label","field","Transfer Learning",{"_361":360,"_356":362},"TLT",{"_361":360,"_356":364},"TAO Toolkit",{"_361":368,"_356":366},"framework","TAO",{"_361":360,"_356":369},"Smart Infrastructure",{"_361":360,"_356":371},"Smart City",{"_361":360,"_356":373},"Metropolis","pltfm_metropolis",{"_361":378,"_356":376},"platform","DeepStream","pltfm_deepstream",{"_361":378,"_356":380},"Deep Learning",{"_361":360,"_356":382},"CV",{"_361":360,"_356":384},"Computer Vision","soln_computer_vision",{"_361":389,"_356":387},"technology","AI","soln_ai",{"_361":389,"_356":391},{"_120":1372,"_122":1373},"ngcApps",[1325],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1306":1307,"_1308":1309,"_1310":1311,"_1312":1313,"_1314":404,"_152":432,"_1315":1316,"_1317":56,"_1318":99,"_1319":99,"_1320":1321,"_1322":99,"_1323":1324},"amplitude",{"_1303":1304,"_944":-7,"_1305":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1297":1298,"_1299":1300,"_1301":1302},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1786954167721,"1.11.0","defaultSearchSuggestions",{"_1184":182,"_1185":1186,"_1187":1188,"_1189":1190},"announcements",[],"consentState",{"_470":1174,"_1175":99,"_1176":31,"_1177":31,"_1178":1179},"consentConfig",{"_452":453,"_454":455,"_456":457,"_458":459,"_460":461,"_462":463,"_464":465,"_466":467,"_468":469,"_470":471},"showContactAdmin",1786954167722,[],[],[446,447,448,449,450,451],{"_45":60,"_47":71,"_49":115,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":115,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":67,"_49":115,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":115,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":16,"_55":56},{"_45":46,"_47":48,"_49":115,"_51":-5,"_53":-5,"_55":56},"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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They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. 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,[609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630],[494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510],{"_511":604,"_306":604,"_181":182,"_484":605},{"_511":600,"_306":600,"_181":182,"_484":601},{"_511":595,"_306":595,"_181":182,"_484":596},{"_511":591,"_306":591,"_181":182,"_484":592},{"_511":586,"_306":586,"_181":182,"_484":587},{"_511":581,"_306":581,"_181":182,"_484":582},{"_511":573,"_306":573,"_181":182,"_484":574},{"_511":565,"_306":565,"_181":182,"_484":566},{"_511":560,"_306":560,"_181":182,"_484":561},{"_511":555,"_306":555,"_181":182,"_484":556},{"_511":550,"_306":550,"_181":182,"_484":551},{"_511":545,"_306":545,"_181":182,"_484":546},{"_511":540,"_306":540,"_181":182,"_484":541},{"_511":533,"_306":533,"_181":182,"_484":534},{"_511":528,"_306":528,"_181":182,"_484":529},{"_511":521,"_306":521,"_181":182,"_484":522},{"_511":512,"_306":512,"_181":182,"_484":513},"hostName","amazon-adsystem.com",[514,515],{"_81":520,"_517":512,"_518":31,"_181":519},{"_81":516,"_517":512,"_518":31,"_181":519},"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",[523,524],{"_81":527,"_517":521,"_518":31,"_181":526},{"_81":525,"_517":521,"_518":31,"_181":526},"IDE","This domain is owned by Doubleclick (Google). 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","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. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. 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.",[701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725],[672,673,674,675,676],{"_511":586,"_306":586,"_181":182,"_484":696},{"_511":560,"_306":560,"_181":182,"_484":691},{"_511":686,"_306":686,"_181":182,"_484":687},{"_511":681,"_306":681,"_181":182,"_484":682},{"_511":677,"_306":677,"_181":182,"_484":678},"nvidia.partners",[679],{"_81":680,"_517":677,"_518":31,"_181":554},"edge","app.hushly.com",[683],{"_81":684,"_517":681,"_518":31,"_181":685},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[688],{"_81":689,"_517":686,"_518":31,"_181":690},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812],[764,765,766,767],{"_511":779,"_306":779,"_181":182,"_484":780},{"_511":774,"_306":774,"_181":182,"_484":775},{"_511":573,"_306":573,"_181":182,"_484":771},{"_511":686,"_306":686,"_181":182,"_484":768},[769],{"_81":770,"_517":686,"_518":99,"_181":690},"_gh_sess",[772],{"_81":773,"_517":573,"_518":99,"_181":580},"YSC","bizible.com",[776],{"_81":777,"_517":774,"_518":31,"_181":778},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[781],{"_81":782,"_517":779,"_518":31,"_181":783},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_81":854,"_517":632,"_518":31,"_181":855},{"_81":839,"_517":853,"_518":99,"_181":840},{"_81":822,"_517":635,"_518":31,"_181":747},{"_81":822,"_517":735,"_518":31,"_181":747},{"_81":851,"_517":632,"_518":31,"_181":852},{"_81":839,"_517":638,"_518":99,"_181":840},{"_81":849,"_517":632,"_518":31,"_181":850},{"_81":839,"_517":635,"_518":99,"_181":840},{"_81":847,"_517":734,"_518":31,"_181":848},{"_81":839,"_517":756,"_518":99,"_181":840},{"_81":819,"_517":635,"_518":31,"_181":821},{"_81":845,"_517":632,"_518":99,"_181":846},{"_81":843,"_517":632,"_518":31,"_181":844},{"_81":841,"_517":632,"_518":99,"_181":842},{"_81":839,"_517":730,"_518":99,"_181":840},{"_81":837,"_517":632,"_518":31,"_181":838},{"_81":835,"_517":632,"_518":31,"_181":836},{"_81":833,"_517":632,"_518":99,"_181":834},{"_81":831,"_517":739,"_518":31,"_181":832},{"_81":819,"_517":730,"_518":31,"_181":821},{"_81":829,"_517":632,"_518":31,"_181":830},{"_81":827,"_517":632,"_518":99,"_181":828},{"_81":825,"_517":632,"_518":31,"_181":826},{"_81":823,"_517":632,"_518":31,"_181":824},{"_81":822,"_517":632,"_518":31,"_181":747},{"_81":819,"_517":820,"_518":31,"_181":821},{"_81":817,"_517":632,"_518":31,"_181":818},{"_81":815,"_517":632,"_518":99,"_181":816},{"_81":813,"_517":632,"_518":31,"_181":814},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","_session_id","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. 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Name	Size	Updated	Actions
class_map.txt	1.78 KB	March 1, 2025 UTC	curl wget Download
retail_detector_100_int8.txt	122.7 KB	March 1, 2025 UTC	curl wget Download
retail_detector_100.etlt	129.7 MB	March 1, 2025 UTC	curl wget Download