thinkoverit/zebra-face-segmentation

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Segmentation fine tuned model for zebra face detection.

Model Details

Model Description

This is Yolo11x-seg based fined tuned model

1)	Face Annotated Dataset in YOLO format is used to train YOLO model for face detection
2)	Trained model is used to crop the face out of all images from classification folder structure dataset.
3)	Generated classification dataset is manullay reviewed to remove any false detections from datatset.
4) 	New dataset is used for training MobilenNet / Yolo-cls models using fine tuning and transfer learning.
5) 	Fine tuned model is used to indentify the new / test images.

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Model Sources [optional]

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Uses

Direct Use

Model should be used to segment zebra faces from zebra pictures, which can further be used for classification identification purpose.

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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

How to Get Started with the Model

Use the code below to get started with the model.

model = YOLO('yolo11xseg-zebra.pt')

result = model.predict("")

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Training Details

Training Data

Face annotated Dataset (For step 1) - https://universe.roboflow.com/pixel6/face-t7r4g

Face Dataset (For step 4) - https://universe.roboflow.com/pixel6/face-classify-color

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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