Merge pull request #389 from johko/fix-repo-paths

Fix Repo Paths

Author: johko

CONTRIBUTING.md

@@ -5,7 +5,7 @@ Hey 👋, great that you want to contribute to the Community Computer Vision Cou
 ### Adding content to the course
 *Important Note: If you’ve never contributed to open-source projects on GitHub, kindly read [this document](https://www.notion.so/19411c29298644df8e9656af45a7686d?pvs=25) which shows to do so with an example for the skops repository.*
 
-1. First go to the [discussion section](https://github.com/johko/computer-vision-course/discussions/).
+1. First go to the [discussion section](https://github.com/huggingface/computer-vision-course/discussions/).
 2. Here you find a section for each unit of the course. Go to the unit you want to contribute to. Open a new discussion and describe what you want to add.
 3. Wait for an approval from the repository maintainers or change requests
 4. When your suggestions is approved, follow these steps:
@@ -15,7 +15,7 @@ Hey 👋, great that you want to contribute to the Community Computer Vision Cou
 
 
 ### Typos/Bug fixes
-1. Open an [Issue](https://github.com/johko/computer-vision-course/issues) describing which content you want to add, change or fix
+1. Open an [Issue](https://github.com/huggingface/computer-vision-course/issues) describing which content you want to add, change or fix
 2. Wait for an approval from the repository maintainers
 3. Follow the steps below to create a PR
 
@@ -24,7 +24,7 @@ Hey 👋, great that you want to contribute to the Community Computer Vision Cou
 1. Fork the repository
 2. Create a new branch for your changes
 3. Make your changes
-4. Create a pull request to the [stage](https://github.com/johko/computer-vision-course/tree/stage) branch of the main repository
+4. Create a pull request to the [stage](https://github.com/huggingface/computer-vision-course/tree/stage) branch of the main repository
 5. Wait for the maintainers to approve your PR
 6. Celebrate your contribution 🥳🎉
 

README.md

@@ -1,4 +1,4 @@
-![Course](https://github.com/johko/computer-vision-course/assets/53175384/58e39903-5a3a-4d48-8f3c-5811f31b93b5)
+![Course](https://github.com/huggingface/computer-vision-course/assets/53175384/58e39903-5a3a-4d48-8f3c-5811f31b93b5)
 
 # Community-led Computer Vision Community Course  🤗
 
@@ -39,7 +39,7 @@ Join [the Hugging Face discord](https://discord.gg/hugging-face-8795489624644936
 
 ### Contributors
 
-<a href="https://github.com/johko/computer-vision-course/graphs/contributors">
+<a href="https://github.com/huggingface/computer-vision-course/graphs/contributors">
   <img src="https://contrib.rocks/image?repo=johko/computer-vision-course" />
 </a>
 

chapters/en/unit0/welcome/welcome.mdx

@@ -90,7 +90,7 @@ The course is organized into multiple units, covering the fundamentals and delvi
 
 ## Meet our team
 
-This course is made by the Hugging Face Community with love 💜! Join us by adding your contribution [on GitHub](https://github.com/johko/computer-vision-course).
+This course is made by the Hugging Face Community with love 💜! Join us by adding your contribution [on GitHub](https://github.com/huggingface/computer-vision-course).
 Our goal was to create a computer vision course that is beginner-friendly and that could act as a resource for others. Around 60+ people from all over the world joined forces to make this project happen. Here we give them credit:
 
 **Unit 1 - Fundamentals of Computer Vision**

chapters/en/unit3/vision-transformers/knowledge-distillation.mdx

@@ -26,7 +26,7 @@ The distillation loss is formulated as:
 
 The KL loss refers to the [Kullback-Leibler Divergence](https://en.wikipedia.org/wiki/Kullback–Leibler_divergence) between the teacher and the student's output distributions. The overall loss for the student model is then formulated as the sum of this distillation loss with the standard cross-entropy loss over the ground-truth labels.
 
-To see this loss function implemented in Python and a fully worked out example in Python, let's check out the [notebook for this section](https://github.com/johko/computer-vision-course/blob/main/notebooks/Unit%203%20-%20Vision%20Transformers/KnowledgeDistillation.ipynb).
+To see this loss function implemented in Python and a fully worked out example in Python, let's check out the [notebook for this section](https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit%203%20-%20Vision%20Transformers/KnowledgeDistillation.ipynb).
 
 <a target="_blank" href="https://colab.research.google.com/github/johko/computer-vision-course/blob/main/notebooks/Unit%203%20-%20Vision%20Transformers/KnowledgeDistillation.ipynb">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>

chapters/en/unit9/tools_and_frameworks.mdx

@@ -18,7 +18,7 @@ pip install -U tensorflow-model-optimization
 
 ### Hands-on guide
 
-For a hands-on guide on how to use the Tensorflow Model Optimization Toolkit, refer this [notebook](https://github.com/johko/computer-vision-course/blob/main/notebooks/Unit%209%20-%20Model%20Optimization/tmo.ipynb)
+For a hands-on guide on how to use the Tensorflow Model Optimization Toolkit, refer this [notebook](https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit%209%20-%20Model%20Optimization/tmo.ipynb)
 ## PyTorch Quantization
 
 ### Overview
@@ -40,7 +40,7 @@ import torch.quantization
 ```
 ## Hands-on guide
 
-For a hands-on guide on how to use the Pytorch Quantization, refer this [notebook](https://github.com/johko/computer-vision-course/blob/main/notebooks/Unit%209%20-%20Model%20Optimization/torch.ipynb)
+For a hands-on guide on how to use the Pytorch Quantization, refer this [notebook](https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit%209%20-%20Model%20Optimization/torch.ipynb)
 
 ## ONNX Runtime
 
@@ -72,7 +72,7 @@ pip install onnxruntime-gpu
 
 ### Hands-on guide
 
-For a hands-on guide on how to use the ONNX Runtime, refer this [notebook](https://github.com/johko/computer-vision-course/blob/main/notebooks/Unit%209%20-%20Model%20Optimization/onnx.ipynb)
+For a hands-on guide on how to use the ONNX Runtime, refer this [notebook](https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit%209%20-%20Model%20Optimization/onnx.ipynb)
 
 ## TensorRT
 
@@ -92,7 +92,7 @@ for other installation methods, see [here](https://docs.nvidia.com/deeplearning/
 
 ### Hands-on guide
 
-For a hands-on guide on how to use the TensorRT, refer this [notebook](https://github.com/johko/computer-vision-course/blob/main/notebooks/Unit%209%20-%20Model%20Optimization/tensorrt.ipynb)
+For a hands-on guide on how to use the TensorRT, refer this [notebook](https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit%209%20-%20Model%20Optimization/tensorrt.ipynb)
 
 ## OpenVINO
 
@@ -116,7 +116,7 @@ For other installation methods, see [here](https://docs.openvino.ai/2023.2/openv
 
 ### Hands-on guide
 
-For a hands-on guide on how to use the OpenVINO, refer this [notebook](https://github.com/johko/computer-vision-course/blob/main/notebooks/Unit%209%20-%20Model%20Optimization/openvino.ipynb)
+For a hands-on guide on how to use the OpenVINO, refer this [notebook](https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit%209%20-%20Model%20Optimization/openvino.ipynb)
 
 ## Optimum
 
@@ -146,7 +146,7 @@ For installation of accelerator-specific features, see [here](https://huggingfac
 
 ### Hands-on guide
 
-For a hands-on guide on how to use Optimum for quantization, refer this [notebook](https://github.com/johko/computer-vision-course/blob/main/notebooks/Unit%209%20-%20Model%20Optimization/optimum.ipynb)
+For a hands-on guide on how to use Optimum for quantization, refer this [notebook](https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit%209%20-%20Model%20Optimization/optimum.ipynb)
 
 ## EdgeTPU
 
@@ -160,6 +160,6 @@ The benefits of using EdgeTPU includes:
 
 For more details on EdgeTPU, see [here](https://cloud.google.com/edge-tpu)
 
-For guide on how to setup and use EdgeTPU, refer this [notebook](https://github.com/johko/computer-vision-course/blob/main/notebooks/Unit%209%20-%20Model%20Optimization/edge_tpu.ipynb)
+For guide on how to setup and use EdgeTPU, refer this [notebook](https://github.com/huggingface/computer-vision-course/blob/main/notebooks/Unit%209%20-%20Model%20Optimization/edge_tpu.ipynb)