
# Model Card for yolov6t

<!-- 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:** [yolov6s](https://hf.co/nateraw/yolov6s), [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>
{"language": "en", "license": "gpl-3.0", "library_name": "pytorch", "tags": ["object-detection", "yolo", "autogenerated-modelcard"], "model_name": "yolov6t"}
pytorch object-detection yolo autogenerated-modelcard en arxiv:1910.09700 license:gpl-3.0 region:us
TAGS
#pytorch #object-detection #yolo #autogenerated-modelcard #en #arxiv-1910.09700 #license-gpl-3.0 #region-us 


# Model Card for yolov6t



#  Table of Contents

1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training Details
5. Evaluation
6. Model Examination
7. Environmental Impact
8. Technical Specifications
9. Citation
10. Glossary
11. More Information
12. Model Card Authors
13. Model Card Contact
14. How To Get Started With the Model


# Model Details

## Model Description



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

- Developed by: 
- Shared by [Optional]: @nateraw
- Model type: 
- Language(s) (NLP): 
- License: 
- Related Models: yolov6s, yolov6n
    - Parent Model: N/A
- Resources for more information: The official GitHub Repository

# Uses



## Direct Use



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

## Downstream Use [Optional]



You can fine-tune this model for your specific task

## Out-of-Scope Use



Don't be evil.

# Bias, Risks, and Limitations



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

## Recommendations



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





## Training Procedure



### Preprocessing



### Speeds, Sizes, Times





# Evaluation



## Testing Data, Factors & Metrics

### Testing Data





### Factors





### Metrics





## Results



# Model Examination



# Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: 

# Technical Specifications [optional]

## Model Architecture and Objective



## Compute Infrastructure



### Hardware



### Software



BibTeX:



APA:



# Glossary [optional]





# More Information [optional]

Please refer to the official GitHub Repository

# Model Card Authors [optional]

@nateraw

# Model Card Contact

@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>



</details>
# Model Card for yolov6t #  Table of Contents

1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training Details
5. Evaluation
6. Model Examination
7. Environmental Impact
8. Technical Specifications
9. Citation
10. Glossary
11. More Information
12. Model Card Authors
13. Model Card Contact
14. How To Get Started With the Model # Model Details ## Model Description



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

- Developed by: 
- Shared by [Optional]: @nateraw
- Model type: 
- Language(s) (NLP): 
- License: 
- Related Models: yolov6s, yolov6n
    - Parent Model: N/A
- Resources for more information: The official GitHub Repository # Uses ## Direct Use



This model is meant to be used as a general object detector. ## Downstream Use [Optional]



You can fine-tune this model for your specific task ## Out-of-Scope Use



Don't be evil. # Bias, Risks, and Limitations



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



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 ## Training Procedure ### Preprocessing ### Speeds, Sizes, Times # Evaluation ## Testing Data, Factors & Metrics ### Testing Data ### Factors ### Metrics ## Results # Model Examination # Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: # Technical Specifications [optional] ## Model Architecture and Objective ## Compute Infrastructure ### Hardware ### Software



BibTeX:



APA: # Glossary [optional] # More Information [optional]

Please refer to the official GitHub Repository # Model Card Authors [optional]

@nateraw # Model Card Contact

@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>



</details>
TAGS
#pytorch #object-detection #yolo #autogenerated-modelcard #en #arxiv-1910.09700 #license-gpl-3.0 #region-us 
 # Model Card for yolov6t #  Table of Contents

1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training Details
5. Evaluation
6. Model Examination
7. Environmental Impact
8. Technical Specifications
9. Citation
10. Glossary
11. More Information
12. Model Card Authors
13. Model Card Contact
14. How To Get Started With the Model # Model Details ## Model Description



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

- Developed by: 
- Shared by [Optional]: @nateraw
- Model type: 
- Language(s) (NLP): 
- License: 
- Related Models: yolov6s, yolov6n
    - Parent Model: N/A
- Resources for more information: The official GitHub Repository # Uses ## Direct Use



This model is meant to be used as a general object detector. ## Downstream Use [Optional]



You can fine-tune this model for your specific task ## Out-of-Scope Use



Don't be evil. # Bias, Risks, and Limitations



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



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 ## Training Procedure ### Preprocessing ### Speeds, Sizes, Times # Evaluation ## Testing Data, Factors & Metrics ### Testing Data ### Factors ### Metrics ## Results # Model Examination # Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: # Technical Specifications [optional] ## Model Architecture and Objective ## Compute Infrastructure ### Hardware ### Software



BibTeX:



APA: # Glossary [optional] # More Information [optional]

Please refer to the official GitHub Repository # Model Card Authors [optional]

@nateraw # Model Card Contact

@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>



</details>
passage: TAGS
#pytorch #object-detection #yolo #autogenerated-modelcard #en #arxiv-1910.09700 #license-gpl-3.0 #region-us 
# Model Card for yolov6t#  Table of Contents

1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Training Details
5. Evaluation
6. Model Examination
7. Environmental Impact
8. Technical Specifications
9. Citation
10. Glossary
11. More Information
12. Model Card Authors
13. Model Card Contact
14. How To Get Started With the Model# Model Details## Model Description



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

- Developed by: 
- Shared by [Optional]: @nateraw
- Model type: 
- Language(s) (NLP): 
- License: 
- Related Models: yolov6s, yolov6n
    - Parent Model: N/A
- Resources for more information: The official GitHub Repository# Uses## Direct Use



This model is meant to be used as a general object detector.## Downstream Use [Optional]



You can fine-tune this model for your specific task## Out-of-Scope Use



Don't be evil.# Bias, Risks, and Limitations



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



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## Training Procedure### Preprocessing### Speeds, Sizes, Times# Evaluation## Testing Data, Factors & Metrics### Testing Data### Factors### Metrics## Results# Model Examination# Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted:# Technical Specifications [optional]## Model Architecture and Objective## Compute Infrastructure### Hardware### Software



BibTeX:



APA: