readme: ---
  language: en
  license: gpl-3.0
  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 mod