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| Yes\nIf personal data collected for the developme"])</script><script>self.__next_f.push([1,"nt of this AI model, was it minimized to only what was required? | Yes\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Not Applicable \nIs data compliant with data subject requests for data correction or removal, if such a request was made? | Not applicable2f:T16c0,"])</script><script>self.__next_f.push([1,"# Model Overview\n\n## Description:\n\nThe Maxine Eye Contact model redirects eye gaze for video conference applications.\n\n\nThe model estimates the gaze direction of the input eye gaze and synthesizes a redirected gaze using a region of interest around one’s eyes known as an eye patch.\n\n\nThe encoder encodes the image’s contents into latent representations for eye gaze, head pose and environmental conditions.\n\n\nA transformation is applied to these latent representations to align them with the redirection angle provided by the user. The transformed representations when fed to the decoder result in the generation of an output eye patch with eyes redirected in the desired angle.\n\n\nMore information about eye contact can be found in the developer blog [here](https://developer.nvidia.com/blog/improve-human-connection-in-video-conferences-with-nvidia-maxine-eye-contact/).\n\n\n\u003cbr\u003e\n## Terms of use\n\nThe use of NVIDIA Maxine Eye Contact is available as a demonstration of the input and output of the Gaze Estimation transformer model. As such the user may submit a reference \"video\" and download the generated gaze redirected video for evaluation under the terms of the [NVIDIA MAXINE EVALUATION LICENSE AGREEMENT](https://developer.download.nvidia.com/maxine/nvidia-maxine-evaluation-license-24oct2023.pdf).\n\n\n## References(s):\n\n* Rochelle, et al. “[Improve Human Connection in Video Conferences with NVIDIA Maxine Eye Contact](https://developer.nvidia.com/blog/improve-human-connection-in-video-conferences-with-nvidia-maxine-eye-contact/)”, Jan 21, 2023, NVIDIA Technical Blog.\u003cbr\u003e\n\n\n## Model Architecture:\n**Architecture Type:** Convolution Neural Network (CNN) \u003cbr\u003e\n**Network Architecture:** Encoder-Decoder \u003cbr\u003e\nThe network architecture includes a transforming encoder and decoder network. The encoder encodes the image’s contents into latent representations for (a) the image’s non-subject related factors(e.g., environmental lighting, shadows, image white balance and hue, blurriness); (b) subjected-related factors (e.g., skin color, face/eye shape, eye glasses); (c) eye gaze and (d) head pose.\n\n\nA rotation applied to the individual latent factors affects a corresponding change in the appearance of the image to alter the factor. In this application, we apply a rotation to the gaze related latent factor. The transformed latent factors are fed to the decoder to generate the transformed version of the input with a gaze redirected angle.\n\u003cbr\u003e\n\n\n## Input:\n**Input Type(s):** Image: Angle vector\u003cbr\u003e\n**Input Format(s):** Image: Red, Green, Blue (RGB), Angle vector: Radians \u003cbr\u003e\n**Input Parameters:** Input Image:(256 x 64) 2D redirection angle vector [2,1] \u003cbr\u003e\n**Other Properties Related to Input:** Input image of resolution 256 x 64 normalized RGB in range of [0, 1]. Redirection gaze angle is a float32 value representing the pitch and yaw angles in radians. \u003cbr\u003e\n\n\n## Output: \u003cbr\u003e\n**Output Type(s):** Image, Estimated Angle Vector,Landmarks Vector \u003cbr\u003e\n**Output Format:** Image: Red, Green, Blue (RGB), Estimated Angle Vector: Radians, Landmarks Vector: Float32 \u003cbr\u003e\n**Output Parameters:** Image: [256 x64], Estimated Angle Vector[2,1] , Landmarks Vector [2,1] \u003cbr\u003e\n**Other Properties Related to Output:** Output image is normalized by dividing by 255. Estimated gaze angle is a float32 representing the pitch and yaw angles in radians. Redirected eye landmarks are represented by 12 fiducial points marking the outline of the eyes, each value represented by x and y positions of the point in float32 format. \u003cbr\u003e\n\n## Software Integration:\n0.8.4.0\n\n## Supported Operating System(s):\n* Ubuntu 18.04 \u003cbr\u003e\n* Ubuntu 20.04 \u003cbr\u003e\n* Ubuntu 22.04 \u003cbr\u003e\n* Debian 11 \u003cbr\u003e\n* Rocky 8.7 \u003cbr\u003e\n* Windows 10 \u003cbr\u003e\n* Windows 11 \u003cbr\u003e\n\n## Model Version(s):\n [Maxine- 0.8.4] \u003cbr\u003e\n\n## Supported Hardware Microarchitecture Compatability:\n\n* [Volta] \u003cbr\u003e\n* [Turing] \u003cbr\u003e\n* [Ampere] \u003cbr\u003e\n* [ADA] \u003cbr\u003e\n* [Hopper] \u003cbr\u003e\n\n\n## Training and Evaluation Dataset:\n\n**Data Collection Method by dataset:**\n* Hybrid: Human and Synthetic \u003cbr\u003e\n\n** Labeling Method by dataset:**\n* Automated \u003cbr\u003e\n\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):**\nTraining dataset contains approximately 2 million images of rectangular eye patches of people captured with different environmental conditions, head poses and gaze angles. The dataset contain a combination of real data and synthetic data. \u003cbr\u003e\n\n### Evaluation Dataset:\n\n**Data Collection Method by dataset:** Automated, Human \u003cbr\u003e\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):**\n\nInternally-captured dataset of 65 videos, each between 30 seconds to two (2) minutes of a single person between 1 to 3 feet in front of the camera conducting a video conference. The dataset varies in terms of quality, lighting, head pose, gaze angles and other diversity factors such as race, eye color, and gender. \u003cbr\u003e\n# Inference:\n**Engine:** [TensorRT](https://developer.nvidia.com/tensorrt), [Triton](https://developer.nvidia.com/triton-inference-server) \u003cbr\u003e\n**Test Hardware:**\n* CUDA 11.8 compatible hardware versions of Desktop and Servers.\n\n## Ethical Considerations:\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 supporting 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++ Explainability, Bias, Safety \u0026 Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/)."])</script><script>self.__next_f.push([1,"30:T9cc,"])</script><script>self.__next_f.push([1,"Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Applications \u0026 Domains: | Content Creation and Video communication.\nTypes: | Facial Animation\nIntended Users: | Applications for gaze estimation and/or gaze redirection in video communication and content creation.\nOutput: | Eye patch image and gaze angle\nDescribe how the model works: | The model takes an eye patch image of a person as input. The model estimates the gaze angle of the eyes with respect to the camera. The GAN model redirects the eye patch to make it look at the camera or specified redirection angle. \nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Age (18+), Gender, Ethnicity\nTechnical Limitations: | Model requires both eyes to be visible and within feasible redirection limits. \nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | Throughput, Latency, and other quality metrics including gaze estimation error, gaze redirection error and Learned perceptual image patch similarity.\nPotential Known Risks: | This model may fail to detect the eyes or redirect them accurately towards the camera typically in cases of uneven lighting, glare, occlusion or high angles of redirection. Color of redirected eyes could change slightly due to lighting or bias in the model. \nLicensing: | [NVIDIA Maxine Evaluation License Agreement](https://developer.download.nvidia.com/maxine/nvidia-maxine-evaluation-license-24oct2023.pdf)"])</script><script>self.__next_f.push([1,"31:T201a,"])</script><script>self.__next_f.push([1,"NVIDIA Maxine Eye Contact NIM uses gRPC APIs for inferencing requests.\n\nA NGC API Key is required to download the appropriate models and resources when starting the NIM. Pass the value of the API Key to the `docker run` command in the next section as the `NGC_API_KEY` environment variable as indicated.\n\nIf you are not familiar with how to create the `NGC_API_KEY` environment variable, the simplest way is to export it in your terminal:\n\n```bash\nexport NGC_API_KEY=\u003cPASTE_API_KEY_HERE\u003e\n```\n\nRun one of the following commands to make the key available at startup:\n\n```bash\n# If using bash\necho \"export NGC_API_KEY=\u003cvalue\u003e\" \u003e\u003e ~/.bashrc\n\n# If using zsh\necho \"export NGC_API_KEY=\u003cvalue\u003e\" \u003e\u003e ~/.zshrc\n```\n\nOther, more secure options include saving the value in a file, so that you can retrieve with `cat $NGC_API_KEY_FILE`, or using a [password manager](https://www.passwordstore.org/).\n\nTo pull the NIM container image from NGC, first authenticate using the NVIDIA Container Registry with the following command:\n\n```bash\necho \"$NGC_API_KEY\" | docker login nvcr.io --username '$oauthtoken' --password-stdin\n```\n\nUse `$oauthtoken` as the username and `NGC_API_KEY` as the password. The `$oauthtoken` username is a special keyword that indicates that you will authenticate with an API Key and not a user name and password.\n\n\nThe following command launches a container with the gRPC service.\n\n```bash\ndocker run -it --rm --name=maxine-eye-contact-nim \\\n --net host \\\n --runtime=nvidia \\\n --gpus all \\\n --shm-size=8GB \\\n -e NGC_API_KEY=$NGC_API_KEY \\\n -e MAXINE_MAX_CONCURRENCY_PER_GPU=1 \\\n -e NIM_MANIFEST_PROFILE=7f0287aa-35d0-11ef-9bba-57fc54315ba3 \\\n -e NIM_HTTP_API_PORT=9000 \\\n -e NIM_GRPC_API_PORT=50051 \\\n -p 9000:9000 \\\n -p 50051:50051 \\\n nvcr.io/nim/nvidia/maxine-eye-contact:latest\n```\nPlease note, the flag --gpus all is used to assign all available GPUs to the docker container.\nTo assign specific GPU to the docker container (in case of multiple GPUs available in your machine) use `--gpus '\"device=0,1,2...\"'`\n\nIf the command runs successfully, you get a response similar to the following.\n\n```bash\n+------------------------+---------+--------+\n| Model | Version | Status |\n+------------------------+---------+--------+\n| GazeRedirectionKey68 | 1 | READY |\n| maxine_nvcf_eyecontact | 1 | READY |\n+------------------------+---------+--------+\n\nI0903 10:35:41.663046 47 metrics.cc:808] Collecting metrics for GPU 0: GPU Name\nI0903 10:35:41.663361 47 metrics.cc:701] Collecting CPU metrics\nI0903 10:35:41.663588 47 tritonserver.cc:2385]\n+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n| Option | Value |\n+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n| server_id | triton |\n| server_version | 2.35.0 |\n| server_extensions | classification sequence model_repository model_repository(unload_dependents) schedule_policy model_configuration system_shared_memory cuda_shared_memory binary_tensor |\n| | _data parameters statistics trace logging |\n| model_repository_path[0] | /opt/maxine/models |\n| model_control_mode | MODE_NONE |\n| strict_model_config | 0 |\n| rate_limit | OFF |\n| pinned_memory_pool_byte_size | 268435456 |\n| cuda_memory_pool_byte_size{0} | 67108864 |\n| min_supported_compute_capability | 6.0 |\n| strict_readiness | 1 |\n| exit_timeout | 30 |\n| cache_enabled | 0 |\n+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n\nI0903 10:35:41.664874 47 grpc_server.cc:2445] Started GRPCInferenceService at 0.0.0.0:8001\nI0903 10:35:41.665204 47 http_server.cc:3555] Started HTTPService at 0.0.0.0:8000\nI0903 10:35:41.706437 47 http_server.cc:185] Started Metrics Service at 0.0.0.0:8002\nMaxine GRPC Service: Listening to 0.0.0.0:8004\n\n```\n\nBy default Maxine Eye Contact gRPC service is hosted on port `8004`. You will have to use this port for inferencing requests.\n\nWe have provided a sample client script file in our GitHub repo.\nThe script could be used to invoke the Docker container using the following instructions.\n\nDownload the Maxine Eye Contact Python client code by cloning the [NIM Client Repository](https://github.com/NVIDIA-Maxine/nim-clients):\n\n```bash\ngit clone https://github.com/NVIDIA-Maxine/nim-clients.git\ncd nim-clients/eye-contact\n```\n\nInstall the dependencies for the Maxine Eye Contact gRPC client:\n\n```bash\nsudo apt-get install python3-pip\npip install -r requirements.txt\n```\nGo to scripts directory\n```bash\ncd scripts\n```\n\nRun the command to send gRPC request\n```bash\npython eye-contact.py --target \u003ctarget_ip:port\u003e --input \u003cinput file path\u003e --output \u003coutput file path along with file name\u003e\n```\n\nTo view details of command line arguments run this command\n```bash\npython eye-contact.py -h\n```\nYou will get a response similar to the following.\n```bash\nusage: eye-contact.py [-h] [--target TARGET] [--input INPUT] [--output OUTPUT]\n\nProcess mp4 video files using gRPC and apply Gaze-redirection.\n\noptions:\n -h, --help show this help message and exit\n --target TARGET The target gRPC server address.\n --input INPUT The path to the input video file.\n --output OUTPUT The path for the output video file.\n```\n\nFor more details on getting started with this NIM including configuring using parameters, visit the [NVIDIA Maxine Eye Contact NIM Docs](https://docs.nvidia.com/nim/maxine/eye-contact/latest/index.html).\n"])</script><script>self.__next_f.push([1,"32:T767,## Getting Started\n\nNVIDIA Maxine Eye Contact NIM uses gRPC APIs for inferencing requests. Following instructions demonstrate the usage of Maxine Eye Contact NIM model using Python client.\n\n### Prerequisites\n\nYou will need a system with `git` and `Python 3.10+` installed.\n\n### Download the NVIDIA Maxine Eye Contact Python client\n\nDownload code by cloning the [gRPC Client Repository](https://github.com/NVIDIA-Maxine/nim-clients.git):\n\n```bash\ngit clone https://github.com/NVIDIA-Maxine/nim-clients.git\ncd nim-clients/eye-contact/\n```\n\nInstall the dependencies for the NVIDIA Maxine Eye Contact Python client:\n\n```bash\nsudo apt-get install python3-pip\npip install -r requirements.txt\n```\n\n### Run Python Client\n\n#### Navigate to the scripts directory \n```bash\ncd scripts\n```\n\n#### Send gRPC request\n\n```bash\npython eye-contact.py --use-ssl \\\n--target grpc.nvcf.nvidia.com:443 \\\n--function-id 15c6f1a0-3843-4cde-b5bc-803a4966fbb6 \\\n--api-key \u003c%- apiKey %\u003e \\\n--input \u003cinput file path\u003e \\\n--output \u003coutput file path and the file name\u003e\n```\n\n#### Note the requirements for input file:\n\n- The supported file type is mp4.\n- The size limit for input file is 1 GB. \n- The duration limit for input file is 3 min.\n- It can take up to 10 mins to process large input files.\n\n#### Command line arguments\n- `--use-ssl` - Flag to control SSL/TLS encryption enablement.\n- `--target` - The gRPC proxy host. It's value is `grpc.nvcf.nvidia.com:443` for this service.\n- `--api-key` - NGC API Key to authenticate user.\n- `--function-id` - Function ID for the feature. \n- `--input` - Input file to apply effect, default value is `../assets/sample_input.mp4`\n- `--output` - Output file name and path, default is current directory (scripts) with name output.mp4 \n\nRefer this [documentation](https://docs.nvidia.com/nim/maxine/eye-contact/latest/index.html) for more information.\n33:T588,This NVIDIA Maxine Eye Contact trial experience allows you to upload video input. NVIDIA will only use your video input to provide this t"])</script><script>self.__next_f.push([1,"rial experience to you and for no other purpose. NVIDIA will not store your video input and will delete it after providing this trial experience to you. For more information about NVIDIA's data processing practices, see NVIDIA's \u003ca href=\"https://www.nvidia.com/en-us/about-nvidia/privacy-policy/\" rel=\"noreferrer\" target=\"_blank\"\u003ePrivacy Policy\u003c/a\u003e. By clicking “Upload New Video,” you consent to the processing of your video input in accordance with NVIDIA’s \u003ca href=\"https://www.nvidia.com/en-us/about-nvidia/privacy-policy/\" rel=\"noreferrer\" target=\"_blank\"\u003ePrivacy Policy\u003c/a\u003e and accept the terms of \u003ca href=\"https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf\" rel=\"noreferrer\" target=\"_blank\"\u003eNVIDIA API Trial Service Terms of Use\u003c/a\u003e. \n\u003cbr /\u003e\n\u003cbr /\u003e\n\u003cb\u003eGOVERNING TERMS\u003c/b\u003e: Your use of this API is governed by the \u003ca href=\"https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf\" rel=\"noreferrer\" target=\"_blank\"\u003eNVIDIA API Trial Service Terms of Use\u003c/a\u003e; and the use of this model is governed by the \u003ca href=\"https://docs.nvidia.com/ai-foundation-models-community-license.pdf\" rel=\"noreferrer\" target=\"_blank\"\u003eNVIDIA AI Foundation Models Community License\u003c/a\u003e.\n34:{\"text\":\"Apply to Self-Host\",\"url\":\"https://www.nvidia.com/en-us/ai/nim-notifyme/\",\"nim_available_override_url\":\"https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/maxine-eye-contact\"}\n38:T201a,"])</script><script>self.__next_f.push([1,"NVIDIA Maxine Eye Contact NIM uses gRPC APIs for inferencing requests.\n\nA NGC API Key is required to download the appropriate models and resources when starting the NIM. Pass the value of the API Key to the `docker run` command in the next section as the `NGC_API_KEY` environment variable as indicated.\n\nIf you are not familiar with how to create the `NGC_API_KEY` environment variable, the simplest way is to export it in your terminal:\n\n```bash\nexport NGC_API_KEY=\u003cPASTE_API_KEY_HERE\u003e\n```\n\nRun one of the following commands to make the key available at startup:\n\n```bash\n# If using bash\necho \"export NGC_API_KEY=\u003cvalue\u003e\" \u003e\u003e ~/.bashrc\n\n# If using zsh\necho \"export NGC_API_KEY=\u003cvalue\u003e\" \u003e\u003e ~/.zshrc\n```\n\nOther, more secure options include saving the value in a file, so that you can retrieve with `cat $NGC_API_KEY_FILE`, or using a [password manager](https://www.passwordstore.org/).\n\nTo pull the NIM container image from NGC, first authenticate using the NVIDIA Container Registry with the following command:\n\n```bash\necho \"$NGC_API_KEY\" | docker login nvcr.io --username '$oauthtoken' --password-stdin\n```\n\nUse `$oauthtoken` as the username and `NGC_API_KEY` as the password. The `$oauthtoken` username is a special keyword that indicates that you will authenticate with an API Key and not a user name and password.\n\n\nThe following command launches a container with the gRPC service.\n\n```bash\ndocker run -it --rm --name=maxine-eye-contact-nim \\\n --net host \\\n --runtime=nvidia \\\n --gpus all \\\n --shm-size=8GB \\\n -e NGC_API_KEY=$NGC_API_KEY \\\n -e MAXINE_MAX_CONCURRENCY_PER_GPU=1 \\\n -e NIM_MANIFEST_PROFILE=7f0287aa-35d0-11ef-9bba-57fc54315ba3 \\\n -e NIM_HTTP_API_PORT=9000 \\\n -e NIM_GRPC_API_PORT=50051 \\\n -p 9000:9000 \\\n -p 50051:50051 \\\n nvcr.io/nim/nvidia/maxine-eye-contact:latest\n```\nPlease note, the flag --gpus all is used to assign all available GPUs to the docker container.\nTo assign specific GPU to the docker container (in case of multiple GPUs available in your machine) use `--gpus '\"device=0,1,2...\"'`\n\nIf the command runs successfully, you get a response similar to the following.\n\n```bash\n+------------------------+---------+--------+\n| Model | Version | Status |\n+------------------------+---------+--------+\n| GazeRedirectionKey68 | 1 | READY |\n| maxine_nvcf_eyecontact | 1 | READY |\n+------------------------+---------+--------+\n\nI0903 10:35:41.663046 47 metrics.cc:808] Collecting metrics for GPU 0: GPU Name\nI0903 10:35:41.663361 47 metrics.cc:701] Collecting CPU metrics\nI0903 10:35:41.663588 47 tritonserver.cc:2385]\n+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n| Option | Value |\n+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n| server_id | triton |\n| server_version | 2.35.0 |\n| server_extensions | classification sequence model_repository model_repository(unload_dependents) schedule_policy model_configuration system_shared_memory cuda_shared_memory binary_tensor |\n| | _data parameters statistics trace logging |\n| model_repository_path[0] | /opt/maxine/models |\n| model_control_mode | MODE_NONE |\n| strict_model_config | 0 |\n| rate_limit | OFF |\n| pinned_memory_pool_byte_size | 268435456 |\n| cuda_memory_pool_byte_size{0} | 67108864 |\n| min_supported_compute_capability | 6.0 |\n| strict_readiness | 1 |\n| exit_timeout | 30 |\n| cache_enabled | 0 |\n+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n\nI0903 10:35:41.664874 47 grpc_server.cc:2445] Started GRPCInferenceService at 0.0.0.0:8001\nI0903 10:35:41.665204 47 http_server.cc:3555] Started HTTPService at 0.0.0.0:8000\nI0903 10:35:41.706437 47 http_server.cc:185] Started Metrics Service at 0.0.0.0:8002\nMaxine GRPC Service: Listening to 0.0.0.0:8004\n\n```\n\nBy default Maxine Eye Contact gRPC service is hosted on port `8004`. You will have to use this port for inferencing requests.\n\nWe have provided a sample client script file in our GitHub repo.\nThe script could be used to invoke the Docker container using the following instructions.\n\nDownload the Maxine Eye Contact Python client code by cloning the [NIM Client Repository](https://github.com/NVIDIA-Maxine/nim-clients):\n\n```bash\ngit clone https://github.com/NVIDIA-Maxine/nim-clients.git\ncd nim-clients/eye-contact\n```\n\nInstall the dependencies for the Maxine Eye Contact gRPC client:\n\n```bash\nsudo apt-get install python3-pip\npip install -r requirements.txt\n```\nGo to scripts directory\n```bash\ncd scripts\n```\n\nRun the command to send gRPC request\n```bash\npython eye-contact.py --target \u003ctarget_ip:port\u003e --input \u003cinput file path\u003e --output \u003coutput file path along with file name\u003e\n```\n\nTo view details of command line arguments run this command\n```bash\npython eye-contact.py -h\n```\nYou will get a response similar to the following.\n```bash\nusage: eye-contact.py [-h] [--target TARGET] [--input INPUT] [--output OUTPUT]\n\nProcess mp4 video files using gRPC and apply Gaze-redirection.\n\noptions:\n -h, --help show this help message and exit\n --target TARGET The target gRPC server address.\n --input INPUT The path to the input video file.\n --output OUTPUT The path for the output video file.\n```\n\nFor more details on getting started with this NIM including configuring using parameters, visit the [NVIDIA Maxine Eye Contact NIM Docs](https://docs.nvidia.com/nim/maxine/eye-contact/latest/index.html).\n"])</script><script>self.__next_f.push([1,"39:T767,## Getting Started\n\nNVIDIA Maxine Eye Contact NIM uses gRPC APIs for inferencing requests. Following instructions demonstrate the usage of Maxine Eye Contact NIM model using Python client.\n\n### Prerequisites\n\nYou will need a system with `git` and `Python 3.10+` installed.\n\n### Download the NVIDIA Maxine Eye Contact Python client\n\nDownload code by cloning the [gRPC Client Repository](https://github.com/NVIDIA-Maxine/nim-clients.git):\n\n```bash\ngit clone https://github.com/NVIDIA-Maxine/nim-clients.git\ncd nim-clients/eye-contact/\n```\n\nInstall the dependencies for the NVIDIA Maxine Eye Contact Python client:\n\n```bash\nsudo apt-get install python3-pip\npip install -r requirements.txt\n```\n\n### Run Python Client\n\n#### Navigate to the scripts directory \n```bash\ncd scripts\n```\n\n#### Send gRPC request\n\n```bash\npython eye-contact.py --use-ssl \\\n--target grpc.nvcf.nvidia.com:443 \\\n--function-id 15c6f1a0-3843-4cde-b5bc-803a4966fbb6 \\\n--api-key \u003c%- apiKey %\u003e \\\n--input \u003cinput file path\u003e \\\n--output \u003coutput file path and the file name\u003e\n```\n\n#### Note the requirements for input file:\n\n- The supported file type is mp4.\n- The size limit for input file is 1 GB. \n- The duration limit for input file is 3 min.\n- It can take up to 10 mins to process large input files.\n\n#### Command line arguments\n- `--use-ssl` - Flag to control SSL/TLS encryption enablement.\n- `--target` - The gRPC proxy host. It's value is `grpc.nvcf.nvidia.com:443` for this service.\n- `--api-key` - NGC API Key to authenticate user.\n- `--function-id` - Function ID for the feature. \n- `--input` - Input file to apply effect, default value is `../assets/sample_input.mp4`\n- `--output` - Output file name and path, default is current directory (scripts) with name output.mp4 \n\nRefer this [documentation](https://docs.nvidia.com/nim/maxine/eye-contact/latest/index.html) for more information.\n3a:T588,This NVIDIA Maxine Eye Contact trial experience allows you to upload video input. NVIDIA will only use your video input to provide this t"])</script><script>self.__next_f.push([1,"rial experience to you and for no other purpose. NVIDIA will not store your video input and will delete it after providing this trial experience to you. For more information about NVIDIA's data processing practices, see NVIDIA's \u003ca href=\"https://www.nvidia.com/en-us/about-nvidia/privacy-policy/\" rel=\"noreferrer\" target=\"_blank\"\u003ePrivacy Policy\u003c/a\u003e. By clicking “Upload New Video,” you consent to the processing of your video input in accordance with NVIDIA’s \u003ca href=\"https://www.nvidia.com/en-us/about-nvidia/privacy-policy/\" rel=\"noreferrer\" target=\"_blank\"\u003ePrivacy Policy\u003c/a\u003e and accept the terms of \u003ca href=\"https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf\" rel=\"noreferrer\" target=\"_blank\"\u003eNVIDIA API Trial Service Terms of Use\u003c/a\u003e. \n\u003cbr /\u003e\n\u003cbr /\u003e\n\u003cb\u003eGOVERNING TERMS\u003c/b\u003e: Your use of this API is governed by the \u003ca href=\"https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf\" rel=\"noreferrer\" target=\"_blank\"\u003eNVIDIA API Trial Service Terms of Use\u003c/a\u003e; and the use of this model is governed by the \u003ca href=\"https://docs.nvidia.com/ai-foundation-models-community-license.pdf\" rel=\"noreferrer\" target=\"_blank\"\u003eNVIDIA AI Foundation Models Community License\u003c/a\u003e.\n37:{\"dockerRun\":\"$38\",\"dockerTermsOfUse\":\"By running the below commands, you accept the \u003ca href=\\\"https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA AI Enterprise Terms of Use\u003c/a\u003e and the \u003ca href=\\\"https://docs.nvidia.com/ai-foundation-models-community-license.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA Community Models 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