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---
language: en
license: mit
library_name: pytorch
tags:
- object-detection
- yolo
- autogenerated-modelcard
model_name: yolov6s
---

# Model Card for yolov6s

<!-- Provide a quick summary of what the model is/does. -->

#  Table of Contents

1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Model Examination](#model-examination)
7. [Environmental Impact](#environmental-impact)
8. [Technical Specifications](#technical-specifications-optional)
9. [Citation](#citation)
10. [Glossary](#glossary-optional)
11. [More Information](#more-information-optional)
12. [Model Card Authors](#model-card-authors-optional)
13. [Model Card Contact](#model-card-contact)
14. [How To Get Started With the Model](#how-to-get-started-with-the-model)


# Model Details

## Model Description

<!-- Provide a longer summary of what this model is. -->

YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance.

- **Developed by:** [More Information Needed]
- **Shared by [Optional]:** [@nateraw](https://hf.co/nateraw)
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Related Models:** [yolov6t](https://hf.co/nateraw/yolov6t), [yolov6n](https://hf.co/nateraw/yolov6n)
    - **Parent Model:** N/A
- **Resources for more information:** The [official GitHub Repository](https://github.com/meituan/YOLOv6)

# Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

## Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

This model is meant to be used as a general object detector.

## Downstream Use [Optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

You can fine-tune this model for your specific task

## Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

Don't be evil.

# Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

This model often classifies objects incorrectly, especially when applied to videos. It does not handle crowds very well.

## Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recomendations.

# Training Details

## Training Data

<!-- 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. -->

[More Information Needed]

## Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

### Preprocessing

[More Information Needed]

### Speeds, Sizes, Times

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

# Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

## Testing Data, Factors & Metrics

### Testing Data

<!-- This should link to a Data Card if possible. -->

[More Information Needed]

### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

## Results

[More Information Needed]

# Model Examination

[More Information Needed]

# Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

# Technical Specifications [optional]

## Model Architecture and Objective

[More Information Needed]

## Compute Infrastructure

[More Information Needed]

### Hardware

[More Information Needed]

### Software

[More Information Needed]

# Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

# Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

# More Information [optional]

Please refer to the [official GitHub Repository](https://github.com/meituan/YOLOv6)

# Model Card Authors [optional]

[@nateraw](https://hf.co/nateraw)

# Model Card Contact

[@nateraw](https://hf.co/nateraw) - please leave a note in the discussions tab here

# How to Get Started with the Model

Use the code below to get started with the model.

<details>
<summary> Click to expand </summary>

[More Information Needed]

</details>