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README.md
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---
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language: en
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license: mit
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library_name: pytorch
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tags:
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- object-detection
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- yolo
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- autogenerated-modelcard
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model_name: yolov6s
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---
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# Model Card for yolov6s
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<!-- Provide a quick summary of what the model is/does. -->
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# Table of Contents
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1. [Model Details](#model-details)
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2. [Uses](#uses)
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3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
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4. [Training Details](#training-details)
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5. [Evaluation](#evaluation)
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6. [Model Examination](#model-examination)
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7. [Environmental Impact](#environmental-impact)
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8. [Technical Specifications](#technical-specifications-optional)
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9. [Citation](#citation)
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10. [Glossary](#glossary-optional)
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11. [More Information](#more-information-optional)
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12. [Model Card Authors](#model-card-authors-optional)
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13. [Model Card Contact](#model-card-contact)
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14. [How To Get Started With the Model](#how-to-get-started-with-the-model)
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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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YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance.
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- **Developed by:** [More Information Needed]
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- **Shared by [Optional]:** [@nateraw](https://hf.co/nateraw)
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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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- **Related Models:** [yolov6t](https://hf.co/nateraw/yolov6t), [yolov6n](https://hf.co/nateraw/yolov6n)
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- **Parent Model:** N/A
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- **Resources for more information:** The [official GitHub Repository](https://github.com/meituan/YOLOv6)
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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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This model is meant to be used as a general object detector.
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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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You can fine-tune this model for your specific task
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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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Don't be evil.
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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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This model often classifies objects incorrectly, especially when applied to videos. It does not handle crowds very well.
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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 recomendations.
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# Training Details
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## Training Data
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<!-- This should link to a Data 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
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[More Information Needed]
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### Speeds, Sizes, Times
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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 Data 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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[More Information Needed]
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## Results
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[More Information Needed]
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# Model Examination
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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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[More Information Needed]
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## Compute Infrastructure
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[More Information Needed]
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### Hardware
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[More Information Needed]
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### Software
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[More Information Needed]
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# Citation
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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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[More Information Needed]
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**APA:**
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[More Information Needed]
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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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Please refer to the [official GitHub Repository](https://github.com/meituan/YOLOv6)
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# Model Card Authors [optional]
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[@nateraw](https://hf.co/nateraw)
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# Model Card Contact
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[@nateraw](https://hf.co/nateraw) - please leave a note in the discussions tab here
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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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<details>
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<summary> Click to expand </summary>
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[More Information Needed]
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</details>
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