updating docs after moving the repo to Intel Org (#489)

Signed-off-by: Abolfazl Shahbazi <[email protected]>

GetStarted.md

@@ -75,7 +75,7 @@ approaches.
       Clone the repo:
 
       ```
-      git clone https://github.com/IntelAI/transfer-learning.git
+      git clone https://github.com/Intel/transfer-learning.git
       cd transfer-learning
       ```
 

README.md

@@ -6,7 +6,7 @@
 
 *Note: You may find it easier to read about Intel Transfer Learning tool, follow the Get
 Started guide, and browse the API material from our published documentation site
-https://intelai.github.io/transfer-learning.*
+https://intel.github.io/transfer-learning.*
 
 <!-- SkipBadges -->
 
@@ -109,12 +109,12 @@ command can be found using, for example, `tlt train --help`.
 
 ## Note on Evaluation and Bias
 
-Intel Transfer Learning Tool provides standard evaluation metrics such as accuracy and loss for validation/test/train sets. While important, it's essential to acknowledge that these metrics may not explicitly capture biases. Users should be cautious and consider potential biases by analyzing disparities in the data and model prediction. Techniques such as confusion matrices, PR curves, ROC curves, local attribution-based and `gradCAM` explanations, can all be good indicators for bias. Clear documentation of model behavior and performance is also crucial for iterative bias mitigation. [Intel® Explainable AI Tools](https://github.com/IntelAI/intel-xai-tools/tree/main) provides components that demonstrate the aformentioned techniques with [Explainer](https://github.com/IntelAI/intel-xai-tools/tree/main/explainer), a simple API providing post-hoc model distillation and visualization methods, as well as The [Model Card Generator](https://github.com/IntelAI/intel-xai-tools/tree/main/model_card_gen) which provides an interactive HTML report that containing these workflows and demonstrations of model behavior.
+Intel Transfer Learning Tool provides standard evaluation metrics such as accuracy and loss for validation/test/train sets. While important, it's essential to acknowledge that these metrics may not explicitly capture biases. Users should be cautious and consider potential biases by analyzing disparities in the data and model prediction. Techniques such as confusion matrices, PR curves, ROC curves, local attribution-based and `gradCAM` explanations, can all be good indicators for bias. Clear documentation of model behavior and performance is also crucial for iterative bias mitigation. [Intel® Explainable AI Tools](https://github.com/Intel/intel-xai-tools/tree/main) provides components that demonstrate the aformentioned techniques with [Explainer](https://github.com/Intel/intel-xai-tools/tree/main/explainer), a simple API providing post-hoc model distillation and visualization methods, as well as The [Model Card Generator](https://github.com/Intel/intel-xai-tools/tree/main/model_card_gen) which provides an interactive HTML report that containing these workflows and demonstrations of model behavior.
 
 ## Support
 
 The Intel Transfer Learning Tool team tracks bugs and enhancement requests using
-[GitHub issues](https://github.com/IntelAI/transfer-learning-tool/issues). Before submitting a
+[GitHub issues](https://github.com/Intel/transfer-learning-tool/issues). Before submitting a
 suggestion or bug report, search the existing GitHub issues to see if your issue has already been reported.
 
 See [Legal Information](Legal.md) for Disclaimers, Trademark, and Licensing information.

api.md

@@ -1,4 +1,4 @@
 # API Reference
 
 Low-code Python\* API documentation is automatically generated from the code and
-appears in the Transfer Learning Tool documentation website's [API](https://intelai.github.io/transfer-learning/main/api.html) page.
+appears in the Transfer Learning Tool documentation website's [API](https://intel.github.io/transfer-learning/main/api.html) page.

cli.md

@@ -1,4 +1,4 @@
 # CLI Reference
 
 No-code bash CLI documentation is automatically generated from the code and
-appears in the Transfer Learning Tool documentation website's [CLI](https://intelai.github.io/transfer-learning/main/cli.html) page.
+appears in the Transfer Learning Tool documentation website's [CLI](https://intel.github.io/transfer-learning/main/cli.html) page.

docs/index.rst

@@ -19,5 +19,5 @@
    Supported Models <Models>
    Legal
    genindex
-   GitHub Repository <https://github.com/IntelAI/transfer-learning-tool>
+   GitHub Repository <https://github.com/Intel/transfer-learning-tool>
 

docs/notebooks/README.rst

@@ -22,27 +22,27 @@ Intel Transfer Learning Tool API Tutorial Notebooks
 .. |imageClassPyTorch| image:: /images/Jupyter_logo.svg
              :alt: Jupyter notebook .ipynb file
              :height: 35
-.. _imageClassPyTorch: https://github.com/IntelAI/transfer-learning/blob/main/notebooks/image_classification/tlt_api_pyt_image_classification/TLT_PyTorch_Image_Classification_Transfer_Learning.ipynb
+.. _imageClassPyTorch: https://github.com/Intel/transfer-learning/blob/main/notebooks/image_classification/tlt_api_pyt_image_classification/TLT_PyTorch_Image_Classification_Transfer_Learning.ipynb
 
 .. |imageClassTensorFlow| image:: /images/Jupyter_logo.svg
              :alt: Jupyter notebook .ipynb file
              :height: 35
-.. _imageClassTensorflow: https://github.com/IntelAI/transfer-learning/blob/main/notebooks/image_classification/tlt_api_tf_image_classification/TLT_TF_Image_Classification_Transfer_Learning.ipynb
+.. _imageClassTensorflow: https://github.com/Intel/transfer-learning/blob/main/notebooks/image_classification/tlt_api_tf_image_classification/TLT_TF_Image_Classification_Transfer_Learning.ipynb
 
 .. |textClassPyTorch| image:: /images/Jupyter_logo.svg
              :alt: Jupyter notebook .ipynb file
              :height: 35
-.. _textClassPyTorch: https://github.com/IntelAI/transfer-learning/blob/main/notebooks/text_classification/tlt_api_pyt_text_classification/TLT_PYT_Text_Classification.ipynb
+.. _textClassPyTorch: https://github.com/Intel/transfer-learning/blob/main/notebooks/text_classification/tlt_api_pyt_text_classification/TLT_PYT_Text_Classification.ipynb
 
 .. |textClassTensorFlow| image:: /images/Jupyter_logo.svg
              :alt: Jupyter notebook .ipynb file
              :height: 35
-.. _textClassTensorflow: https://github.com/IntelAI/transfer-learning/blob/main/notebooks/text_classification/tlt_api_tf_text_classification/TLT_TF_Text_Classification.ipynb
+.. _textClassTensorflow: https://github.com/Intel/transfer-learning/blob/main/notebooks/text_classification/tlt_api_tf_text_classification/TLT_TF_Text_Classification.ipynb
 
 .. |imageAnomalyPyTorch| image:: /images/Jupyter_logo.svg
              :alt: Jupyter notebook .ipynb file
              :height: 35
-.. _imageAnomalyPyTorch: https://github.com/IntelAI/transfer-learning/blob/main/notebooks/image_anomaly_detection/tlt_api_pyt_anomaly_detection/Anomaly_Detection.ipynb
+.. _imageAnomalyPyTorch: https://github.com/Intel/transfer-learning/blob/main/notebooks/image_anomaly_detection/tlt_api_pyt_anomaly_detection/Anomaly_Detection.ipynb
 
 .. csv-table::
    :header: "Notebook Title", ".ipynb Link", "Use Case", "Framework"
@@ -60,12 +60,12 @@ Intel Transfer Learning Tool API End-to-End Pipelines
 .. |imageClassMedical| image:: /images/Jupyter_logo.svg
              :alt: Jupyter notebook .ipynb file
              :height: 35
-.. _imageClassMedical: https://github.com/IntelAI/transfer-learning/blob/main/notebooks/e2e_workflows/Medical_Imaging_Classification.ipynb
+.. _imageClassMedical: https://github.com/Intel/transfer-learning/blob/main/notebooks/e2e_workflows/Medical_Imaging_Classification.ipynb
 
 .. |imageClassRemote| image:: /images/Jupyter_logo.svg
              :alt: Jupyter notebook .ipynb file
              :height: 35
-.. _imageClassRemote: https://github.com/IntelAI/transfer-learning/blob/main/notebooks/e2e_workflows/Remote_Sensing_Image_Scene_Classification.ipynb
+.. _imageClassRemote: https://github.com/Intel/transfer-learning/blob/main/notebooks/e2e_workflows/Remote_Sensing_Image_Scene_Classification.ipynb
 
 
 .. csv-table::
@@ -81,12 +81,12 @@ Intel Transfer Learning Tool Performance Comparison
 .. |imageClassTFPerf| image:: /images/Jupyter_logo.svg
              :alt: Jupyter notebook .ipynb file
              :height: 35
-.. _imageClassTFPerf: https://github.com/IntelAI/transfer-learning/blob/main/notebooks/performance/tf_image_classification_performance.ipynb
+.. _imageClassTFPerf: https://github.com/Intel/transfer-learning/blob/main/notebooks/performance/tf_image_classification_performance.ipynb
 
 .. |textClassHFPerf| image:: /images/Jupyter_logo.svg
              :alt: Jupyter notebook .ipynb file
              :height: 35
-.. _textClassHFPerf: https://github.com/IntelAI/transfer-learning/blob/main/notebooks/performance/hf_text_classification_performance.ipynb
+.. _textClassHFPerf: https://github.com/Intel/transfer-learning/blob/main/notebooks/performance/hf_text_classification_performance.ipynb
 
 .. csv-table::
    :header: "Notebook Title", ".ipynb Link", "Use Case", "Framework"

notebooks/README.md

@@ -53,4 +53,4 @@ and [Intel Extension for PyTorch](https://github.com/intel/intel-extension-for-p
 | [Performance Comparison: Text Classification Transfer Learning with Hugging Face and the Intel Transfer Learning Tool](/notebooks/performance/hf_text_classification_performance.ipynb) | NLP: Text Classification | Hugging Face, PyTorch, and the Intel Transfer Learning Tool API | Compares training and evaluation metrics for text classification transfer learning using the Hugging Face Trainer and the Intel Transfer Learning Tool. |
 
 ### Note on Evaluation and Bias
-All notebooks provide standard evaluation metrics such as accuracy and loss for validation/test/train sets. While important, it's essential to acknowledge that these metrics may not explicitly capture biases. Users should be cautious and consider potential biases by analyzing disparities in the data and model prediction. Techniques such as confusion matrices, PR curves, ROC curves, local attribution-based and `gradCAM` explanations, can all be good indicators for bias. Clear documentation of model behavior and performance is also crucial for iterative bias mitigation. [Intel® Explainable AI Tools](https://github.com/IntelAI/intel-xai-tools/tree/main) provides components that demonstrate the aformentioned techniques with [Explainer](https://github.com/IntelAI/intel-xai-tools/tree/main/explainer), a simple API providing post-hoc model distillation and visualization methods, as well as The [Model Card Generator](https://github.com/IntelAI/intel-xai-tools/tree/main/model_card_gen) which provides an interactive HTML report that containing these workflows and demonstrations of model behavior.
+All notebooks provide standard evaluation metrics such as accuracy and loss for validation/test/train sets. While important, it's essential to acknowledge that these metrics may not explicitly capture biases. Users should be cautious and consider potential biases by analyzing disparities in the data and model prediction. Techniques such as confusion matrices, PR curves, ROC curves, local attribution-based and `gradCAM` explanations, can all be good indicators for bias. Clear documentation of model behavior and performance is also crucial for iterative bias mitigation. [Intel® Explainable AI Tools](https://github.com/Intel/intel-xai-tools/tree/main) provides components that demonstrate the aformentioned techniques with [Explainer](https://github.com/Intel/intel-xai-tools/tree/main/explainer), a simple API providing post-hoc model distillation and visualization methods, as well as The [Model Card Generator](https://github.com/Intel/intel-xai-tools/tree/main/model_card_gen) which provides an interactive HTML report that containing these workflows and demonstrations of model behavior.

notebooks/setup.md

@@ -16,7 +16,7 @@ Software Requirements:
 2. Clone the GitHub repo if you haven't done this in step 1
 
    ```
-   git clone https://github.com/IntelAI/transfer-learning.git
+   git clone https://github.com/Intel/transfer-learning.git
    cd transfer-learning 
    ```
 

setup.py

@@ -49,7 +49,7 @@ def get_framework_requirements(framework_name):
 setup(name="intel-transfer-learning-tool",
       description="Intel® Transfer Learning Tool",
       version="0.7.0",
-      url='https://github.com/IntelAI/transfer-learning',
+      url='https://github.com/Intel/transfer-learning',
       license='Apache 2.0',
       author='IntelAI',
       author_email='[email protected]',

tests/README.md

@@ -5,7 +5,7 @@ Then install the following dependencies:
 
 ```
 # Clone this repo, if you don't already have it
-git clone https://github.com/IntelAI.transfer-learning.git
+git clone https://github.com/Intel.transfer-learning.git
 cd transfer-learning
 
 # Run all tests with make, or skip this step to run individually

workflows/disease_prediction/README.md

@@ -54,13 +54,13 @@ Linux OS (Ubuntu 22.04) is used to validate this reference solution. Make sure t
 
 ## How It Works?
 
-The Vision reference Implementation component uses [Intel Transfer Learning Toolkit based vision workload](https://github.com/IntelAI/transfer-learning), which is optimized for image fine-tuning and inference. This workload uses Tensorflowhub's ResNet-50 model to fine-tune a new convolutional neural network model with subtracted CESM image dataset. The images are preprocessed by using domain expert-defined segmented regions to reduce redundancies during training.
+The Vision reference Implementation component uses [Intel Transfer Learning Toolkit based vision workload](https://github.com/Intel/transfer-learning), which is optimized for image fine-tuning and inference. This workload uses Tensorflowhub's ResNet-50 model to fine-tune a new convolutional neural network model with subtracted CESM image dataset. The images are preprocessed by using domain expert-defined segmented regions to reduce redundancies during training.
 
 ## Get Started
 
 ### Download the repository
 
-git clone https://github.com/IntelAI/transfer-learning.git vision_workflow
+git clone https://github.com/Intel/transfer-learning.git vision_workflow
 cd vision_workflow/workflows/disease_prediction
 
 
@@ -214,7 +214,7 @@ To implement this reference use case on a different or customized pre-training m
 For more information or to read about other relevant workflow examples, see these guides and software resources:
 - [Intel® AI Analytics Toolkit (AI Kit)](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html)
 - [Intel® Neural Compressor](https://github.com/intel/neural-compressor)
-- [Intel® Transfer Learning Tool](https://github.com/IntelAI/transfer-learning/tree/v0.7.0)
+- [Intel® Transfer Learning Tool](https://github.com/Intel/transfer-learning/tree/v0.7.0)
 
 ## Support
 If you have any questions with this workflow, want help with troubleshooting, want to report a bug or submit enhancement requests, please submit a GitHub issue.

workflows/vision_anomaly_detection/README.md

@@ -16,7 +16,7 @@ This workflow is a fine-tuning module under the [Visual Quality Inspection refer
 - [Support](#support)
 
 ## Technical Overview
-This repository provides a layer within the higher level Visual Quality Inspection reference kit and supports the following using [Intel® Transfer Learning Tool](https://github.com/IntelAI/transfer-learning):
+This repository provides a layer within the higher level Visual Quality Inspection reference kit and supports the following using [Intel® Transfer Learning Tool](https://github.com/Intel/transfer-learning):
 - Fine-tuning and inference on custom dataset
 - Implementation for different feature extractors based on:
   - Pre-trained model (without fine-tuning)
@@ -87,7 +87,7 @@ It contains the workflow code:
 ```
 export $WORKSPACE=/<workdir/path>
 cd $WORKSPACE
-git clone https://github.com/IntelAI/transfer-learning.git
+git clone https://github.com/Intel/transfer-learning.git
 cd transfer-learning/workflows/vision_anomaly_detection
 ```
 
@@ -124,7 +124,7 @@ Ensure you have completed steps in the [Get Started Section](#get-started).
 Build or Pull the provided docker image.
 
 ```bash
-git clone https://github.com/IntelAI/models -b r2.11 intel-models
+git clone https://github.com/Intel/models -b r2.11 intel-models
 cd docker
 docker compose build
 cd ..
@@ -244,7 +244,7 @@ pip install -r requirements.txt
 
 Download the mvtec dataset using Intel Model Zoo dataset download API
 ```
-git clone https://github.com/IntelAI/models.git $WORKSPACE/models
+git clone https://github.com/Intel/models.git $WORKSPACE/models
 cd $WORKSPACE/models/datasets/dataset_api/
 ```
 
@@ -280,8 +280,8 @@ python src/vision_anomaly_wrapper.py --config_file config/config.yaml
 
 ## Learn More
 For more information or to read about other relevant workflow examples, see these guides and software resources:
-- [Intel® Transfer Learning Tool](https://github.com/IntelAI/transfer-learning)
-- [Anomaly Detection fine-tuning workflow using SimSiam and CutPaste techniques](https://github.com/IntelAI/transfer-learning/tree/main/workflows/vision_anomaly_detection)
+- [Intel® Transfer Learning Tool](https://github.com/Intel/transfer-learning)
+- [Anomaly Detection fine-tuning workflow using SimSiam and CutPaste techniques](https://github.com/Intel/transfer-learning/tree/main/workflows/vision_anomaly_detection)
 - [Intel® AI Analytics Toolkit (AI Kit)](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html)
 - [Intel® Extension for PyTorch](https://intel.github.io/intel-extension-for-pytorch/)
 - [Intel® Extension for Scikit-learn](https://www.intel.com/content/www/us/en/developer/tools/oneapi/scikit-learn.html#gs.x609e4)