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</h3>
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<br />
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## Supported computer vision tasks
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Giskard Vision is a comprehensive Python package designed to simplify and streamline a variety of computer vision tasks. Whether you're working on facial landmark detection, image classification, or object detection, Giskard Vision provides the tools you need to evaluate your models with ease.
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## Supported Computer Vision Tasks
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- **Facial Landmark Detection** ([Readme](https://github.com/Giskard-AI/giskard-vision/blob/main/giskard_vision/landmark_detection/Readme.md))
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- **Image Classification** ([Readme](https://github.com/Giskard-AI/giskard-vision/blob/main/giskard_vision/image_classification/Readme.md))
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- **Object Detection** ([Readme](https://github.com/Giskard-AI/giskard-vision/blob/main/giskard_vision/object_detection/Readme.md))
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## Installation
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## Install
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To install Giskard Vision, simply use pip:
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```
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```bash
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pip install giskard-vision
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```
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To install the repo in dev mode
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If you want to contribute to the development or explore the latest features, you can install the repository in development mode:
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```shell
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git clone https://github.com/Giskard-AI/giskard-vision.git
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source .venv/bin/activate
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```
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## Scan
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Giskard Vision includes powerful scanning capabilities to evaluate your models. To run a scan, first ensure that you have the `giskard` library installed:
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```shell
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pip install giskard
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```
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Then, you can perform a scan using the following code:
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```py
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from giskard_vision import scan
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dataloader = ...
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model = ...
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results = scan(model, dataloader)
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```
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Explore the examples provided to see how to implement scans in different contexts:
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- [Facial Landmark Detection Notebook](https://github.com/Giskard-AI/giskard-vision/blob/main/examples/landmark_detection/ffhq_scan.ipynb)
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- [Image Classification Notebook](https://github.com/Giskard-AI/giskard-vision/blob/main/examples/image_classification/sc_scan.ipynb)
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- [Object Detection Notebook](https://github.com/Giskard-AI/giskard-vision/blob/main/examples/object_detection/racoons_scan.ipynb)
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## Examples
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In order to explore the jupyter notebook tutorials, all you need is to install the repo in dev mode and check out `examples` directory.
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The `examples` directory contains Jupyter notebook tutorials that demonstrate how to use Giskard Vision for various tasks. To explore these tutorials:
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1. Install the repository in development mode.
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2. Navigate to the examples directory and open the notebook of interest.
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## FAQ
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#### I am getting `attributeerror: module 'cv2.face' has no attribute 'createlbphfacerecognizer'` when running some examples in dev mode
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This is most likely due to the order in which `opencv-contrib-python` module is installed. The following trick should resolve the issue:
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This issue usually occurs due to the installation order of the `opencv-contrib-python` module. To resolve it, follow these steps:
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```bash
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pip uninstall opencv-contrib-python

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