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README.md

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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>