Merge remote-tracking branch 'origin/main'

README.md

@@ -9,12 +9,19 @@ Create and deploy to production a simple neural network for Computer Vision
 * Gradio for interactive user-experience platform within an online platform (Data-ICMC Website).
 
 
-# Datasets to consider
+## Datasets to consider
 * [LabelMe 12 50k](https://www.kaggle.com/datasets/dschettler8845/labelme-12-50k)
 * [City-Scapes](https://www.cityscapes-dataset.com/dataset-overview/)
 * [CamVid](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/)
 
 
-# References
+## References
 * [DSNet-Fast](https://www.researchgate.net/figure/The-architecture-of-fast-dense-segmentation-network-DSNet-fast-The-encoder-is_fig1_347180093)
-* [DSNet](https://www.researchgate.net/figure/The-architecture-of-dense-segmentation-network-DSNet-The-encoder-is-a-fully-convolutional_fig1_347180092)
+* [DSNet](https://www.researchgate.net/figure/The-architecture-of-dense-segmentation-network-DSNet-The-encoder-is-a-fully-convolutional_fig1_347180092)
+
+
+# First Model
+The first model available and deployed considers a simple 2D image, taken from the MNIST Dataset. Reefer to [_High-Performance Neural Networks
+for Visual Object Classification_](https://arxiv.org/pdf/1102.0183.pdf) for further details on the dataset.
+
+The CI/CD process will use the default Github pipeline using the available [Github Actions features](https://github.blog/2022-02-02-build-ci-cd-pipeline-github-actions-four-steps/). The training process will use the MLFLow framework, to cather and track the necessary metrics and log accordingly. Reefer to the [docs](https://mlflow.org/docs/latest/quickstart.html) for further details.