Feature Extraction
Transformers
Safetensors
custom_code
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- library_name: transformers
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- tags: []
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- # Model Card for Model ID
 
 
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
 
 
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- ### Model Description
 
 
 
 
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- <!-- Provide a longer summary of what this model is. -->
 
 
 
 
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
 
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
 
 
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- ### Model Sources [optional]
 
 
 
 
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- <!-- Provide the basic links for the model. -->
 
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
 
 
 
 
 
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
 
 
 
 
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- **APA:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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+ # Model Overview
 
 
 
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+ ## Description:
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+ This model performs visual feature extraction.
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+ For instance, RADIO generates image embeddings that can be used by a downstream model to classify images.
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+ This model is for research and development only.
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+ ### License/Terms of Use
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+ [License](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf)
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+ ## References:
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+ [Paper](https://arxiv.org/abs/2312.06709)
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+ ## Model Architecture:
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+ **Architecture Type:** Neural Network <br>
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+ **Network Architecture:** Vision Transformer <br>
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+ ## Input:
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+ **Input Type(s):** Image <br>
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+ **Input Format(s):** Red, Green, Blue (RGB) <br>
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+ **Input Parameters:** Two Dimensional (2D) <br>
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+ **Other Properties Related to Input:** Image resolutions up to 2048x2028 in increments of 16 pixels <br>
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+ ## Output:
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+ **Output Type(s):** Embeddings <br>
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+ **Output Format:** Tensor <br>
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+ **Output Parameters:** 2D <br>
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+ **Other Properties Related to Output:** Downstream model required to leverage image features <br>
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+ ## Software Integration:
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+ **Runtime Engine(s):**
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+ * TAO- 24.10 <br>
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+ **Supported Hardware Microarchitecture Compatibility:** <br>
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+ * NVIDIA Ampere <br>
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+ * NVIDIA Blackwell <br>
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+ * NVIDIA Jetson <br>
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+ * NVIDIA Hopper <br>
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+ * NVIDIA Lovelace <br>
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+ * NVIDIA Pascal <br>
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+ * NVIDIA Turing <br>
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+ * NVIDIA Volta <br>
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+ **[Preferred/Supported] Operating System(s):** <br>
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+ * Linux
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+ * Linux 4 Tegra
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+ * QNX
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+ * Windows
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+ ## Model Version(s):
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+ C-RADIO.
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+ **Link:** https://huggingface.co/nvidia/C-RADIO
 
 
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+ # Training, Testing, and Evaluation Datasets:
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+ ## Training Dataset:
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+ NV-CC-Img-Text-Dataset <br>
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+ ** Data Collection Method by dataset <br>
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+ * Automated <br>
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+ ** Labeling Method by dataset <br>
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+ * Not Applicable (no labels are needed) <br>
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+ **Properties:** 700 Million Images <br>
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+ ## Evaluation Dataset:
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+ **Link:** [ImageNet](https://www.image-net.org/) <br>
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+ ** Data Collection Method by dataset <br>
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+ * Automated <br>
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+ ** Labeling Method by dataset <br>
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+ * Human <br>
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+ **Properties:** This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images.<br>
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+ ## Inference:
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+ **Engine:** PyTorch <br>
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+ **Test Hardware:** A100 <br>
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ## Ethical Considerations (For NVIDIA Models Only):
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+ 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.
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+ Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).