Retail Object Recognition

Model Overview

This model encodes retail objects to embedding vectors and predicts their labels based on the embedding vectors in the reference space.

In version 2.0, the Retail Object Recognition model both generates embedding vectors in a reference space and provides a classification output to categorize the objects into seen classes, which refer to the classes that have appeared in the training datasets.

Both versions 1.0 and 2.0 are adept at executing few-shot learning, having been trained through the process of metric learning. For a more comprehensive understanding of metric learning, please consult the provided references.

Model Version Summary

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Model Version	Model Architecture	Input resolution	Model Size	Training Number of Classes	Task	Training data - synthetic images	Training data - real images	Decryption Code
Retail Object Recognition v1.0	ResNet101	224x224	46.7M	100	Recognition retail objects based on embedding vectors in reference space	600k, 100 retail subjects	50k, 100 retail subjects, cropped from 6 scenes	nvidia_tlt
Retail Object Recognition v2.0	FAN-Base-Hybrid	224x224	205.8M	315	This task involves a dual-output network structure, which generates an embedding vectors in reference space and a classification output for categorizing the objects into seen classes.	80k synthetic data with improved complexity and variances compaed to training data in v1.0	48k, 315 retail subjects, cropped from 7 scenes (added one more scene from real training data in v1.0)	None

Model Architecture

ResNet

Version 1.0 consists of a traunk and embedded. The trunk is a ResNet101 classification network with its fully connected layers removed. The embedder is a one-layer Perceptron with an input size of 2048 (the output dimension from the averagePool layer of the ResNet101 trunk) and an output size of 2048. Thus the embedding dimension of the Retail Object Recognition model is 2048.

FAN-Base-Hybrid

The Retail Object Recognition model (version 2.0) is composed of three primary components: a trunk, a classification head, and an embedding head.\nThe trunk uses the FAN-Base-Hybrid (Fully Attentional Network) model architecture for feature extraction. The embedding head uses four adaptors with an output size of 448. The adaptor consists of two fully connected layers, GELU activation functions and residual connection. The classifier is derived from the adaptors along with a one-layer perceptron with an output size corresponding to the number of classes.

Training

Version 1.0

The Retail Object Recognition model (version 1.0) was trained with the Triplet Loss network algorithm. The training algorithm optimizes the network to minimize the embedding output distances (cosine similarity) between the positive images and the anchor image while maximizing the distances between the negative images and the anchor image.

The trunk and embedder use different learning rates during training. The embedder uses a smaller learning rate than the trunk for a better fine-tuning effect.

Version 2.0

For the Retail Object Recognition model (version 2.0), the training algorithm optimizes the network to minimize the embedding features and target classes for the objects.\nThe trunk is initialized by the FAN-Base-Hybrid pretrained model and remains frozen during the training process.\nThe embedder is trained using the Triplet Loss algorithm, an approach that enhances the network by reducing the cosine similarity-measured distances between the anchor image and positive images, while concurrently extending the distances between the anchor image and negative images.\nThe classifier was trained through the Cross-entropy algorithm, minimizing the distance between the images and their corresponding classes.\nThe classifier and embedder are jointly trained that can potentially enhances the precision by leveraged on the features from both branches.

Training Data

The training data of the Retail Object Recognition model was cropped from images for Retail Object Detection model training and fine-tuning data (see Retail Object Detection - TRAINING DATA). Thus it is made up of both synthetic data and real data. By mixing the synthetic and real images in the training set, the model is enhanced, allowing it to bridge the gap between simulation and reality. Consequently, the model can learn feature representations from both synthetic and real image sources.

The training data encompasses multiple angles of the retail objects, which equips the model to identify a retail object from any given angle.

Version 1.0

Specifically, the model was trained on a mixture of 0.6 million synthetic images and 50k real images. During the training phase, the triplet loss on the mixture would be optimized. And during the validation phase, the accuracy of the similarity search would be calculated. The reference data for the validation set are synthetic while the query data for the validation set are real images.

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dataset	Total # of images	Training images	Testing images
Synthetic data	600,000	600,000	-
Real data	56,898	53,476	3,422

Version 2.0

The Retail Object Recognition model (version 2.0) trained with a new retail dataset, which total has 315 distinct categories.

Specifically, the model's training involved a combined dataset of more than 80,000 synthetic images and 48,000 real images. In the training phrase, both cross-entropy loss and triple loss are optimized with this composite dataset. In the testing phase, the probabilities for each category are computed. In parallel, a similarity search is conducted, which used synthetic data as the reference and real data as the query.

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Dataset	Total # of images	Training images	Testing images
Synthetic Data	80,872	80,872	-
Real Data	129,012	48,140	58,699

Inference Data Ground-truth Labeling Guidelines

The real training images were cropped from Retail Object Detection datasets with ground-truth bounding-boxes and categories by human labellers. To run inference on your own datasets, you may follow the guidelines below.

Reference Data Guidelines

Reference data is the database for similarity search during the inference stage for the Retail Object Recognition model. The prediction of the inference images would be decided by the L2 distances of the extracted features. Specifically, the algorithm would select the reference object with the smallest L2 distance to the query object in the reference database by Kmeans, and the predicted class would be the corresponding class of the selected reference object.

Therefore, to achieve the highest accuracy for retail object recognition, the reference data needs to be as close to the inference data as possible, regarding the background, occlusion, object orientations, etc.

For instance, if you decide that you only want to infer the retail objects with the front face, then you can collect the front side of the retail objects only as reference data. On the other hand, if you want the Retail Object Recognition model to recognize the objects with whatever angles presented, then more orientations of the retail objects need to be collected in the reference dataset.

Generally 20-30 images/class for reference data is of the highest efficiency. However, it would be definitely better to collect more reference examples, say 100 images/class.

Below are the guidelines for the specific conditions of the images:

All objects should take at least 70% of the frame.
Noisy background: images with noises in the backgrounds are fine.
Occlusion: objects occluded by distractions such as hands are fine. But at least 60% of the object should be visible.
Truncation: objects truncated by the edge of the frame with visibility of >= 60% are fine.
Each image should be assigned to a specific class. This model does not accommodate the case when multiple classes are classified to some “other”/”unknown” class.

Query Data Guidelines

Same as the reference data guidelines.

Notice that the Retail Object Recognition model can never correctly classify the retail object if the class is not in the reference dataset.

To get the most accurate predictions, you should avoid challenging the Retail Object Recognition model with some bad views, such as the top of a soda can (as this view can be the same across many different retail objects).

Performance

Evaluation Data

Here, we present the evaluation results of Retail Object Recognition models, including versions 1.0, 2.0, and an unreleased version 1.1. The purpose of version 1.1 is to serve as a benchmark for comparing the model architecture potentials of v1.0 and v2.0. It is important to note that all test Key Performance Indicator (KPI) data are proprietary and derived from the test KPI data of Retail Object Detection model. For more information of the test dataset, please check Retail Object Detection - TRAINING AND TEST DATA.

Methodology and KPI

The performance of Retail Object Recognition models is measured using Accuracy, which is the proportion of correct predictions (all classes) made by the model out of all predictions.

Accuracy of the Classification Head

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Model	Model Architecture	Training dataset description	Test dataset description	Accuracy
Retail Object Recognition - v1.0	ResNet 101	A mixture of 60k synthetic images and 53k real images, 100 retail subjects. Real images are obtained from 6 scenes.	3,422 images, 100 retail subjects, 6 scenes in total	0.8453
Retail Object Recognition - v1.1 (not released)	ResNet 101	A mixture of 80k synthetic images and 48kk real images, 315 retail subjects. Real images are obtained from 7 scenes (added one more scene based on v1.0 model training dataset)	58,699 images, 315 retail subjects, 7 scenes in total (added one more scene based on v1.0 model test dataset)	0.7038
Retail Object Recognition - v2.0	FAN-Base-Hybrid	Same as v1.1	Same as v1.1	0.8797

Accuracy of the Embedding Head.

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# of test images/class	# of images/class in reference database	FAN-Base-Hybrid Accuracy
180	1	0.7718
180	5	0.8432
180	10	0.8485
180	20	0.8541
180	30	0.8561
180	40	0.8557
180	50	0.8577
180	60	0.8567
180	70	0.8581
180	80	0.8591
180	90	0.8605
180	100	0.8587

Real-time Inference Performance

The inference is run on the provided unpruned model at FP16 precision. The model input resolution is 224x224. 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.

Version 1.0

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model	device	batch size	Latency (ms)	Images per second
Retail Object Recognition - v1.0	Jetson AGX Orin 64GB	1	1.59	627
Retail Object Recognition - v1.0	Jetson AGX Orin 64GB	16	12.83	1247
Retail Object Recognition - v1.0	Jetson AGX Orin 64GB	32	23.61	1356
Retail Object Recognition - v1.0	Tesla A10	1	0.98	1018
Retail Object Recognition - v1.0	Tesla A10	16	5.95	2690
Retail Object Recognition - v1.0	Tesla A10	64	20.61	3106

Version 2.0

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model	device	batch size	Latency (ms)	Images per second
Retail Object Recognition - v2.0	Jetson Orin Nano	4	99.50	40.2
Retail Object Recognition - v2.0	Orin NX 16GB	4	66.89	59.8
Retail Object Recognition - v2.0	AGX Orin 64GB	8	49.69	161
Retail Object Recognition - v2.0	A2	16	99.38	161
Retail Object Recognition - v2.0	T4	8	31.62	253
Retail Object Recognition - v2.0	A30	16	21.80	734
Retail Object Recognition - v2.0	L4	4	6.63	603
Retail Object Recognition - v2.0	L40	8	4.85	1648
Retail Object Recognition - v2.0	A100	64	39.48	1621
Retail Object Recognition - v2.0	H100	64	23.83	2686

How to use this model

Instructions to use unpruned model with TAO

The trainable version of this model can be used for transfer learning via TAO Toolkit, while the deployable versions can be used for inference deployment via DeepStream/TensorRT.

Instructions to transfer learning the model with TAO Toolkit

Currently we only support transfer learning for Retail Object Recognition version 1.0. To proceed with this, kindly adhere to the following steps.

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Download and install TAO Toolkit.
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In order to use the model as pre-trained weights for transfer learning, please use the snippet below as a template for the model component of the experiment spec file to train a Metric Learning Recognition model. Below is a code snippt to config the pretrained weights of Retail Object Recognition model v1.0 to Metric Learning Recognition transfer learning in TAO. For more details, check TAO - Metric Learning Recognition Documentation
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model:\n  backbone: resnet_101\n  pretrain_choice: \"\"\n  pretrained_model_path: /path/to/retail_object_recognition.pth\n  input_width: 224\n  input_height: 224\n  feat_dim: 2048\n\ntrain:\n  optim:\n    ...\n  smooth_loss: True\n  num_epochs: 100\n  checkpoint_interval: 5\n  resume_training_checkpoint_path: null\n  batch_size: 16\n  val_batch_size: 16\n  report_accuracy_per_class: True\n\ndataset:\n  train_dataset: /path/to/training/dataset\n  val_dataset: \n    reference: /path/to/reference/dataset\n    query: /path/to/query/dataset\n

To start the training of Metric Learning Recognition model, use following command:

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tao model ml_recog train -e=<train config file>\n

For more information on experiment spec file and training with TAO Toolkit, please refer to the notebook example at TAO Toolkit - Jupyter notebooks - Retail Object Recognition.

Instructions to deploy Retail Object Recognition Model (version 1.0) with TAO Toolkit

The TAO Toolkit also provides support for deploying Retail Object Recognition model v1.0 through TAO-Deploy. The ONNX model can be converted into a TensorRT engine first and then used for inference with a reference database. To proceed with this, please follow the steps below after downloading and installing the TAO Toolkit as instructed in the last section.

Convert the deployable (onnx file) Retail Object Recognition model v1.0 to TensorRT engine. Below is the code snippt for this conversion.

gen_trt_engine:\n  gpu_id: 0\n  onnx_file: /path/to/deployable/retail/object/recognition/v1.0\n  trt_engine: /path/to/converted/trt/engine/file\n  tensorrt:\n    data_type: fp32\n    workspace_size: 1024\n    min_batch_size: 1\n    opt_batch_size: 10\n    max_batch_size: 10\n

To launch the TensorRT conversion, use

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tao deploy ml_recog gen_trt_engine -e=<trt conversion config file>\n

Run Inference with the converted TensorRT engine file. Below is a code snippt to config the pretrained weights of Retail Object Recognition model v1.0 to Metric Learning Recognition transfer learning in TAO.

inference:\n  trt_engine: /path/to/converted/trt/engine/file\n  input_path: /path/to/test/images\n  inference_input_type: image_folder\n  topk: 5\n  ...\n\ndataset:\n  train_dataset: \"\"\n  val_dataset: \n    reference: /path/to/reference/dataset\n    query: \"\"\n  ...\n

To conduct the inference, use following command:

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tao deploy ml_recog inference -e=<inference config file>\n

For more information on experiment spec files and running inference with TAO Toolkit, please refer to the notebook example at TAO Toolkit - Jupyter notebooks - Retail Object Recognition.

Limitations

Very Small Objects

NVIDIA Retail Object Recognition models are trained to classify objects larger than 10x10 pixels. Therefore it may generate poor results when classifying objects that are smaller than 10x10 pixels.

Occluded Objects

When objects are occluded or truncated such that less than 40% of the object is visible, they may not be correctly classified by the Retail Object Detection model. Partial occlusion by hand is acceptable as the model was trained with examples having random occlusions.

Monochrome or Infrared Camera Images

The Retail Object Recognition models are trained on RGB images. Therefore, images captured in a monochrome image or IR camera image may not provide good detection results.

Warped and Blurry Images

The Retail Object Recognition models are not trained on fish-eye lense cameras or moving cameras. Therefore, the models may not perform well for warped images and images that have motion-induced or other blur.

Model versions

Deployable_v1.0: The encrypted onnx file and encrypted etlt file for Retail Object Recognition model, inferencable on DeepStream and TAO Toolkit.
Trainable_v1.0: The checkpoint for Retail Object Recognition model, trainable on TAO Toolkit.
Deployable_v2.0: The decrypted onnx file for Retail Recognition model, inferencable on DeepStream.

References

Citations

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Hoffer, Elad, and Nir Ailon. \"Deep metric learning using triplet network.\" International workshop on similarity-based pattern recognition. Springer, Cham, 2015.
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Na, Shi, Liu Xumin, and Guan Yong. \"Research on k-means clustering algorithm: An improved k-means clustering algorithm.\" 2010 Third International Symposium on intelligent information technology and security informatics. Ieee, 2010.
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Daquan Zhou, Zhiding Yu, Enze Xie, Chaowei Xiao, Anima Anandkumar, Jiashi Feng and Jose M. Alvarez. \"Understanding The Robustness in Vision Transformers\". International Conference on Machine Learning (ICML). 2022
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Technical blogs

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License

License to use these models is covered by the Model EULA. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of these licenses.

Ethical Considerations

NVIDIA Retail Object Recognition model classifies retail objects. However, no additional information such as people and other distractors in the background are inferred.\nTraining and evaluation dataset mostly consists of North American content. An ideal training and evaluation dataset would additionally include content from other geographies.\nNVIDIA’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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For a more comprehensive understanding of metric learning, please consult the provided references.\n\n## Model Version Summary \n\n| Model Version | Model Architecture | Input resolution | Model Size | Training Number of Classes | Task | Training data - synthetic images | Training data - real images | Decryption Code |\n| -------------------------- | ------------------ | ---------------- | ---------- | -------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------ | --------------- |\n| Retail Object Recognition v1.0 | ResNet101 | 224x224 | 46.7M | 100 | Recognition retail objects based on embedding vectors in reference space | 600k, 100 retail subjects | 50k, 100 retail subjects, cropped from 6 scenes | nvidia_tlt |\n| Retail Object Recognition v2.0 | FAN-Base-Hybrid | 224x224 | 205.8M | 315 | This task involves a dual-output network structure, which generates an embedding vectors in reference space and a classification output for categorizing the objects into seen classes. | 80k synthetic data with improved complexity and variances compaed to training data in v1.0 | 48k, 315 retail subjects, cropped from 7 scenes (added one more scene from real training data in v1.0) | None |\n\n\n## Model Architecture \n\n### ResNet\nVersion 1.0 consists of a traunk and embedded. The trunk is a ResNet101 classification network with its fully connected layers removed. The embedder is a one-layer Perceptron with an input size of 2048 (the output dimension from the averagePool layer of the ResNet101 trunk) and an output size of 2048. Thus the embedding dimension of the Retail Object Recognition model is 2048.\n\n### FAN-Base-Hybrid\nThe Retail Object Recognition model (version 2.0) is composed of three primary components: a trunk, a classification head, and an embedding head. \nThe trunk uses the [FAN-Base-Hybrid](https://arxiv.org/pdf/2204.12451.pdf) (Fully Attentional Network) model architecture for feature extraction. The embedding head uses four adaptors with an output size of 448. The adaptor consists of two fully connected layers, GELU activation functions and residual connection. The classifier is derived from the adaptors along with a one-layer perceptron with an output size corresponding to the number of classes. \n\n## Training \n\n### Version 1.0\nThe Retail Object Recognition model (version 1.0) was trained with the Triplet Loss network algorithm. The training algorithm optimizes the network to minimize the embedding output distances (cosine similarity) between the positive images and the anchor image while maximizing the distances between the negative images and the anchor image. \n\nThe trunk and embedder use different learning rates during training. The embedder uses a smaller learning rate than the trunk for a better fine-tuning effect.\n\n### Version 2.0\nFor the Retail Object Recognition model (version 2.0), the training algorithm optimizes the network to minimize the embedding features and target classes for the objects.\nThe trunk is initialized by the FAN-Base-Hybrid pretrained model and remains frozen during the training process.\nThe embedder is trained using the Triplet Loss algorithm, an approach that enhances the network by reducing the cosine similarity-measured distances between the anchor image and positive images, while concurrently extending the distances between the anchor image and negative images.\nThe classifier was trained through the Cross-entropy algorithm, minimizing the distance between the images and their corresponding classes.\nThe classifier and embedder are jointly trained that can potentially enhances the precision by leveraged on the features from both branches. \n\n### Training Data \nThe training data of the Retail Object Recognition model was cropped from images for Retail Object Detection model training and fine-tuning data (see [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection) - TRAINING DATA). Thus it is made up of both synthetic data and real data. By mixing the synthetic and real images in the training set, the model is enhanced, allowing it to bridge the gap between simulation and reality. Consequently, the model can learn feature representations from both synthetic and real image sources. \n\nThe training data encompasses multiple angles of the retail objects, which equips the model to identify a retail object from any given angle.\n\n#### Version 1.0\nSpecifically, the model was trained on a mixture of 0.6 million synthetic images and 50k real images. During the training phase, the triplet loss on the mixture would be optimized. And during the validation phase, the accuracy of the similarity search would be calculated. The reference data for the validation set are synthetic while the query data for the validation set are real images. \n\n\n| dataset | Total # of images | Training images | Testing images |\n| -------------- | ----------------- | --------------- | -------------- |\n| Synthetic data | 600,000 | 600,000 | \\- |\n| Real data | 56,898 | 53,476 | 3,422 |\n\n

\n\n\n#### Version 2.0\nThe Retail Object Recognition model (version 2.0) trained with a new retail dataset, which total has 315 distinct categories.\n\nSpecifically, the model's training involved a combined dataset of more than 80,000 synthetic images and 48,000 real images. In the training phrase, both cross-entropy loss and triple loss are optimized with this composite dataset. In the testing phase, the probabilities for each category are computed. In parallel, a similarity search is conducted, which used synthetic data as the reference and real data as the query. \n\n\n| Dataset | Total # of images | Training images | Testing images|\n|--|--|--|--|\n|Synthetic Data| 80,872 | 80,872 | -\n|Real Data | 129,012 | 48,140 | 58,699 \n\n

\n\n### Inference Data Ground-truth Labeling Guidelines\nThe real training images were cropped from Retail Object Detection datasets with ground-truth bounding-boxes and categories by human labellers. To run inference on your own datasets, you may follow the guidelines below.\n\n#### Reference Data Guidelines\n\nReference data is the database for similarity search during the inference stage for the Retail Object Recognition model. The prediction of the inference images would be decided by the L2 distances of the extracted features. Specifically, the algorithm would select the reference object with the smallest L2 distance to the query object in the reference database by Kmeans, and the predicted class would be the corresponding class of the selected reference object. \n\nTherefore, to achieve the highest accuracy for retail object recognition, the reference data needs to be as close to the inference data as possible, regarding the background, occlusion, object orientations, etc.\n\nFor instance, if you decide that you only want to infer the retail objects with the front face, then you can collect the front side of the retail objects only as reference data. On the other hand, if you want the Retail Object Recognition model to recognize the objects with whatever angles presented, then more orientations of the retail objects need to be collected in the reference dataset.\n\nGenerally 20-30 images/class for reference data is of the highest efficiency. However, it would be definitely better to collect more reference examples, say 100 images/class. \n\nBelow are the guidelines for the specific conditions of the images:\n1. All objects should take at least 70% of the frame. \n2. Noisy background: images with noises in the backgrounds are fine.\n3. Occlusion: objects occluded by distractions such as hands are fine. But at least 60% of the object should be visible.\n4. Truncation: objects truncated by the edge of the frame with visibility of >= 60% are fine.\n5. Each image should be assigned to a specific class. This model does not accommodate the case when multiple classes are classified to some “other”/”unknown” class.\n\n#### Query Data Guidelines\n\nSame as the reference data guidelines. \n\nNotice that the Retail Object Recognition model can never correctly classify the retail object if the class is not in the reference dataset.\n\nTo get the most accurate predictions, you should avoid challenging the Retail Object Recognition model with some bad views, such as the top of a soda can (as this view can be the same across many different retail objects).\n\n## Performance\n### Evaluation Data \n\nHere, we present the evaluation results of Retail Object Recognition models, including versions 1.0, 2.0, and an unreleased version 1.1. The purpose of version 1.1 is to serve as a benchmark for comparing the model architecture potentials of v1.0 and v2.0. It is important to note that all test Key Performance Indicator (KPI) data are proprietary and derived from the test KPI data of [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection) model. For more information of the test dataset, please check [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection) - TRAINING AND TEST DATA.\n\n### Methodology and KPI\n\nThe performance of Retail Object Recognition models is measured using Accuracy, which is the proportion of correct predictions (all classes) made by the model out of all predictions.\n\nAccuracy of the Classification Head\n| Model | Model Architecture | Training dataset description | Test dataset description | Accuracy |\n| ------------------------------------------- | ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | -------- |\n| Retail Object Recognition - v1.0 | ResNet 101 | A mixture of 60k synthetic images and 53k real images, 100 retail subjects. Real images are obtained from 6 scenes. | 3,422 images, 100 retail subjects, 6 scenes in total | 0.8453 |\n| Retail Object Recognition - v1.1 (not released) | ResNet 101 | A mixture of 80k synthetic images and 48kk real images, 315 retail subjects. Real images are obtained from 7 scenes (added one more scene based on v1.0 model training dataset) | 58,699 images, 315 retail subjects, 7 scenes in total (added one more scene based on v1.0 model test dataset) | 0.7038 |\n| Retail Object Recognition - v2.0 | FAN-Base-Hybrid | Same as v1.1 | Same as v1.1 | 0.8797 |\n \nAccuracy of the Embedding Head.\n| # of test images/class | # of images/class in reference database | FAN-Base-Hybrid Accuracy \n|-- |-- |--|\n|180 | 1 | 0.7718\n|180 | 5 | 0.8432\n|180 | 10 | 0.8485\n|180 | 20 | 0.8541\n|180 | 30 | 0.8561\n|180 | 40 | 0.8557\n|180 | 50 | 0.8577\n|180 | 60 | 0.8567\n|180 | 70 | 0.8581\n|180 | 80 | 0.8591\n|180 | 90 | 0.8605\n|180 | 100| 0.8587\n\n### Real-time Inference Performance \nThe inference is run on the provided unpruned model at FP16 precision. The model input resolution is 224x224. The inference performance is run using [trtexec](https://github.com/NVIDIA/TensorRT/tree/master/samples/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#### Version 1.0\n\n| model | device | batch size | Latency (ms) | Images per second |\n| -------------------------------- | -------------------- | ---------- | ------------ | ----------------- |\n| Retail Object Recognition - v1.0 | Jetson AGX Orin 64GB | 1 | 1.59 | 627 |\n| Retail Object Recognition - v1.0 | Jetson AGX Orin 64GB | 16 | 12.83 | 1247 |\n| Retail Object Recognition - v1.0 | Jetson AGX Orin 64GB | 32 | 23.61 | 1356 |\n| Retail Object Recognition - v1.0 | Tesla A10 | 1 | 0.98 | 1018 |\n| Retail Object Recognition - v1.0 | Tesla A10 | 16 | 5.95 | 2690 |\n| Retail Object Recognition - v1.0 | Tesla A10 | 64 | 20.61 | 3106 |\n\n\n#### Version 2.0\n| model | device | batch size | Latency (ms) | Images per second |\n| -------------------------------- | ---------------- | ---------- | ------------ | ----------------- |\n| Retail Object Recognition - v2.0 | Jetson Orin Nano | 4 | 99.50 | 40.2 |\n| Retail Object Recognition - v2.0 | Orin NX 16GB | 4 | 66.89 | 59.8 |\n| Retail Object Recognition - v2.0 | AGX Orin 64GB | 8 | 49.69 | 161 |\n| Retail Object Recognition - v2.0 | A2 | 16 | 99.38 | 161 |\n| Retail Object Recognition - v2.0 | T4 | 8 | 31.62 | 253 |\n| Retail Object Recognition - v2.0 | A30 | 16 | 21.80 | 734 |\n| Retail Object Recognition - v2.0 | L4 | 4 | 6.63 | 603 |\n| Retail Object Recognition - v2.0 | L40 | 8 | 4.85 | 1648 |\n| Retail Object Recognition - v2.0 | A100 | 64 | 39.48 | 1621 |\n| Retail Object Recognition - v2.0 | H100 | 64 | 23.83 | 2686 |\n\n## How to use this model \n### Instructions to use unpruned model with TAO\nThe trainable version of this model can be used for transfer learning via [TAO Toolkit](https://docs.nvidia.com/tao/tao-toolkit/), while the deployable versions can be used for inference deployment via DeepStream/TensorRT.\n\n### Instructions to transfer learning the model with TAO Toolkit\n\nCurrently we only support transfer learning for Retail Object Recognition version 1.0. To proceed with this, kindly adhere to the following steps.\n\n1. [Download](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html) and install TAO Toolkit.\n\n2. In order to use the model as pre-trained weights for transfer learning, please use the snippet below as a template for the model component of the experiment spec file to train a `Metric Learning Recognition` model. Below is a code snippt to config the pretrained weights of Retail Object Recognition model v1.0 to Metric Learning Recognition transfer learning in TAO. For more details, check [TAO - Metric Learning Recognition Documentation](https://docs.nvidia.com/tao/tao-toolkit/text/metric_learning_recognition/index.html)\n\n```\nmodel:\n backbone: resnet_101\n pretrain_choice: \"\"\n pretrained_model_path: /path/to/retail_object_recognition.pth\n input_width: 224\n input_height: 224\n feat_dim: 2048\n\ntrain:\n optim:\n ...\n smooth_loss: True\n num_epochs: 100\n checkpoint_interval: 5\n resume_training_checkpoint_path: null\n batch_size: 16\n val_batch_size: 16\n report_accuracy_per_class: True\n\ndataset:\n train_dataset: /path/to/training/dataset\n val_dataset: \n reference: /path/to/reference/dataset\n query: /path/to/query/dataset\n```\n\n3. To start the training of `Metric Learning Recognition` model, use following command:\n\n```bash\ntao model ml_recog train -e=\n```\n\n4. For more information on experiment spec file and training with TAO Toolkit, please refer to the notebook example at [TAO Toolkit](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/resources/tao-getting-started) - Jupyter notebooks - Retail Object Recognition.\n\n\n### Instructions to deploy Retail Object Recognition Model (version 1.0) with TAO Toolkit\n\nThe TAO Toolkit also provides support for deploying Retail Object Recognition model v1.0 through [TAO-Deploy](https://docs.nvidia.com/tao/tao-toolkit/text/metric_learning_recognition/metric_learning_recognition.html#deploying-the-model). The ONNX model can be converted into a [TensorRT](https://developer.nvidia.com/tensorrt) engine first and then used for inference with a reference database. To proceed with this, please follow the steps below after downloading and installing the TAO Toolkit as instructed in the last section.\n\n1. Convert the deployable (onnx file) Retail Object Recognition model v1.0 to TensorRT engine. Below is the code snippt for this conversion.\n\n```\ngen_trt_engine:\n gpu_id: 0\n onnx_file: /path/to/deployable/retail/object/recognition/v1.0\n trt_engine: /path/to/converted/trt/engine/file\n tensorrt:\n data_type: fp32\n workspace_size: 1024\n min_batch_size: 1\n opt_batch_size: 10\n max_batch_size: 10\n```\n\nTo launch the TensorRT conversion, use\n```bash\ntao deploy ml_recog gen_trt_engine -e=\n```\n\n2. Run Inference with the converted TensorRT engine file. Below is a code snippt to config the pretrained weights of Retail Object Recognition model v1.0 to `Metric Learning Recognition` transfer learning in TAO.\n\n```\ninference:\n trt_engine: /path/to/converted/trt/engine/file\n input_path: /path/to/test/images\n inference_input_type: image_folder\n topk: 5\n ...\n\ndataset:\n train_dataset: \"\"\n val_dataset: \n reference: /path/to/reference/dataset\n query: \"\"\n ...\n```\n\nTo conduct the inference, use following command:\n```bash\ntao deploy ml_recog inference -e=\n```\n\n3. For more information on experiment spec files and running inference with TAO Toolkit, please refer to the notebook example at [TAO Toolkit](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/resources/tao-getting-started) - Jupyter notebooks - Retail Object Recognition.\n\n\n## Limitations \n### Very Small Objects\nNVIDIA Retail Object Recognition models are trained to classify objects larger than 10x10 pixels. Therefore it may generate poor results when classifying objects that are smaller than 10x10 pixels. \n### Occluded Objects\nWhen objects are occluded or truncated such that less than 40% of the object is visible, they may not be correctly classified by the Retail Object Detection model. Partial occlusion by hand is acceptable as the model was trained with examples having random occlusions. \n### Monochrome or Infrared Camera Images \nThe Retail Object Recognition models are trained on RGB images. Therefore, images captured in a monochrome image or IR camera image may not provide good detection results.\n### Warped and Blurry Images\nThe Retail Object Recognition models are not trained on fish-eye lense cameras or moving cameras. Therefore, the models may not perform well for warped images and images that have motion-induced or other blur.\n\n## Model versions\n- Deployable_v1.0: The encrypted onnx file and encrypted etlt file for Retail Object Recognition model, inferencable on DeepStream and TAO Toolkit.\n- Trainable_v1.0: The checkpoint for Retail Object Recognition model, trainable on TAO Toolkit.\n- Deployable_v2.0: The decrypted onnx file for Retail Recognition model, inferencable on DeepStream.\n\n\n## References\n### Citations \n\n- Hoffer, Elad, and Nir Ailon. \"Deep metric learning using triplet network.\" *International workshop on similarity-based pattern recognition*. Springer, Cham, 2015.\n\n- Na, Shi, Liu Xumin, and Guan Yong. \"Research on k-means clustering algorithm: An improved k-means clustering algorithm.\" *2010 Third International Symposium on intelligent information technology and security informatics.* Ieee, 2010.\n\n- Daquan Zhou, Zhiding Yu, Enze Xie, Chaowei Xiao, Anima Anandkumar, Jiashi Feng and Jose M. Alvarez. \"Understanding The Robustness in Vision Transformers\". *International Conference on Machine Learning (ICML).* 2022\n\n### Technical blogs \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 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- 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 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 model on the edge using DeepStream. Learn more about [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk)\n\n## License \nLicense to use these models is covered by the Model EULA. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of these [licenses](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/).\n\n## Ethical Considerations \nNVIDIA Retail Object Recognition model classifies retail objects. However, no additional information such as people and other distractors in the background are inferred.\nTraining and evaluation dataset mostly consists of North American content. An ideal training and evaluation dataset would additionally include content from other geographies.\nNVIDIA’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.","displayName","Retail Object Recognition",["D",1710373709380],"publisher","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/retail_object_recognition","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[254,255,256,257,258,259,260,261,262,263,264,265,266],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_223":302,"_268":303,"_269":304,"_271":272},{"_223":298,"_268":299,"_269":300,"_271":272},{"_223":296,"_268":296,"_269":297,"_271":272},{"_223":294,"_268":294,"_269":295,"_271":272},{"_223":291,"_268":292,"_269":293,"_271":272},{"_223":287,"_268":288,"_269":289,"_271":272},{"_223":285,"_268":285,"_269":286,"_271":272},{"_223":283,"_268":283,"_269":284,"_271":272},{"_223":281,"_268":281,"_269":282,"_271":272},{"_223":278,"_268":278,"_269":279,"_271":272},{"_223":276,"_268":276,"_269":277,"_271":272},{"_223":274,"_268":274,"_269":275,"_271":272},{"_223":267,"_268":267,"_269":270,"_271":272},"Transfer Learning Toolkit","value","filter",{"_273":272,"_268":267},"category","label","field","Transfer Learning",{"_273":272,"_268":274},"TLT",{"_273":272,"_268":276},"TAO Toolkit",{"_273":280,"_268":278},"framework","TAO",{"_273":272,"_268":281},"Smart Infrastructure",{"_273":272,"_268":283},"Smart City",{"_273":272,"_268":285},"Metropolis","pltfm_metropolis",{"_273":290,"_268":288},"platform","DeepStream","pltfm_deepstream",{"_273":290,"_268":292},"Deep Learning",{"_273":272,"_268":294},"CV",{"_273":272,"_268":296},"Computer Vision","soln_computer_vision",{"_273":301,"_268":299},"technology","AI","soln_ai",{"_273":301,"_268":303},{"_108":1282,"_110":1283},"ngcApps",[1235],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1216":1217,"_1218":1219,"_1220":1221,"_1222":1223,"_1224":316,"_140":344,"_1225":1226,"_1227":56,"_1228":87,"_1229":87,"_1230":1231,"_1232":87,"_1233":1234},"amplitude",{"_1213":1214,"_855":-7,"_1215":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1207":1208,"_1209":1210,"_1211":1212},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1786954087396,"1.11.0","defaultSearchSuggestions",{"_1095":170,"_1096":1097,"_1098":1099,"_1100":1101},"announcements",[],"consentState",{"_381":1085,"_1086":87,"_1087":31,"_1088":31,"_1089":1090},"consentConfig",{"_363":364,"_365":366,"_367":368,"_369":370,"_371":372,"_373":374,"_375":376,"_377":378,"_379":380,"_381":382},"showContactAdmin",1786954087400,[],[],[358,359,360,361,362],{"_45":60,"_47":71,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":103,"_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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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. 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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.",[612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636],[583,584,585,586,587],{"_422":497,"_223":497,"_169":170,"_395":607},{"_422":471,"_223":471,"_169":170,"_395":602},{"_422":597,"_223":597,"_169":170,"_395":598},{"_422":592,"_223":592,"_169":170,"_395":593},{"_422":588,"_223":588,"_169":170,"_395":589},"nvidia.partners",[590],{"_79":591,"_428":588,"_429":31,"_169":465},"edge","app.hushly.com",[594],{"_79":595,"_428":592,"_429":31,"_169":596},"*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",[599],{"_79":600,"_428":597,"_429":31,"_169":601},"_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.",[695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723],[675,676,677,678],{"_422":690,"_223":690,"_169":170,"_395":691},{"_422":685,"_223":685,"_169":170,"_395":686},{"_422":484,"_223":484,"_169":170,"_395":682},{"_422":597,"_223":597,"_169":170,"_395":679},[680],{"_79":681,"_428":597,"_429":87,"_169":601},"_gh_sess",[683],{"_79":684,"_428":484,"_429":87,"_169":491},"YSC","bizible.com",[687],{"_79":688,"_428":685,"_429":31,"_169":689},"_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",[692],{"_79":693,"_428":690,"_429":31,"_169":694},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_79":765,"_428":543,"_429":31,"_169":766},{"_79":750,"_428":764,"_429":87,"_169":751},{"_79":733,"_428":546,"_429":31,"_169":658},{"_79":733,"_428":646,"_429":31,"_169":658},{"_79":762,"_428":543,"_429":31,"_169":763},{"_79":750,"_428":549,"_429":87,"_169":751},{"_79":760,"_428":543,"_429":31,"_169":761},{"_79":750,"_428":546,"_429":87,"_169":751},{"_79":758,"_428":645,"_429":31,"_169":759},{"_79":750,"_428":667,"_429":87,"_169":751},{"_79":730,"_428":546,"_429":31,"_169":732},{"_79":756,"_428":543,"_429":87,"_169":757},{"_79":754,"_428":543,"_429":31,"_169":755},{"_79":752,"_428":543,"_429":87,"_169":753},{"_79":750,"_428":641,"_429":87,"_169":751},{"_79":748,"_428":543,"_429":31,"_169":749},{"_79":746,"_428":543,"_429":31,"_169":747},{"_79":744,"_428":543,"_429":87,"_169":745},{"_79":742,"_428":650,"_429":31,"_169":743},{"_79":730,"_428":641,"_429":31,"_169":732},{"_79":740,"_428":543,"_429":31,"_169":741},{"_79":738,"_428":543,"_429":87,"_169":739},{"_79":736,"_428":543,"_429":31,"_169":737},{"_79":734,"_428":543,"_429":31,"_169":735},{"_79":733,"_428":543,"_429":31,"_169":658},{"_79":730,"_428":731,"_429":31,"_169":732},{"_79":728,"_428":543,"_429":31,"_169":729},{"_79":726,"_428":543,"_429":87,"_169":727},{"_79":724,"_428":543,"_429":31,"_169":725},"_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. 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