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docs/en/guides/conda-quickstart.md
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@ -10,7 +10,7 @@ keywords: Ultralytics, Conda, setup, installation, environment, guide, machine l
<img width="800" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-conda-package-visual.avif" alt="Ultralytics Conda Package Visual">
</p>
This guide provides a comprehensive introduction to setting up a Conda environment for your Ultralytics projects. Conda is an open-source package and environment management system that offers an excellent alternative to pip for installing packages and dependencies. Its isolated environments make it particularly well-suited for data science and machine learning endeavors. For more details, visit the Ultralytics Conda package on [Anaconda](https://anaconda.org/conda-forge/ultralytics) and check out the Ultralytics feedstock repository for package updates on [GitHub](https://github.com/conda-forge/ultralytics-feedstock/).
This guide provides a comprehensive introduction to setting up a Conda environment for your Ultralytics projects. Conda is an open-source package and environment management system that offers an excellent alternative to pip for installing packages and dependencies. Its isolated environments make it particularly well-suited for data science and [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) endeavors. For more details, visit the Ultralytics Conda package on [Anaconda](https://anaconda.org/conda-forge/ultralytics) and check out the Ultralytics feedstock repository for package updates on [GitHub](https://github.com/conda-forge/ultralytics-feedstock/).
[![Conda Version](https://img.shields.io/conda/vn/conda-forge/ultralytics?logo=condaforge)](https://anaconda.org/conda-forge/ultralytics)
[![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/ultralytics.svg)](https://anaconda.org/conda-forge/ultralytics)
@ -68,7 +68,7 @@ conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cu
## Using Ultralytics
With Ultralytics installed, you can now start using its robust features for object detection, instance segmentation, and more. For example, to predict an image, you can run:
With Ultralytics installed, you can now start using its robust features for [object detection](https://www.ultralytics.com/glossary/object-detection), [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), and more. For example, to predict an image, you can run:
```python
from ultralytics import YOLO
@ -162,7 +162,7 @@ Yes, you can enhance performance by utilizing a CUDA-enabled environment. Ensure
conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=11.8 ultralytics
```
This setup enables GPU acceleration, crucial for intensive tasks like deep learning model training and inference. For more information, visit the [Ultralytics installation guide](../quickstart.md).
This setup enables GPU acceleration, crucial for intensive tasks like [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) model training and inference. For more information, visit the [Ultralytics installation guide](../quickstart.md).
### What are the benefits of using Ultralytics Docker images with a Conda environment?