Add article to demo

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -101,20 +101,27 @@ def segment(satellite_image: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
     return raw_segmentation, segmentation_overlay
 
 
-i = gr.inputs.Image()
-o = [gr.Image(), gr.Image()]
+inputs = gr.inputs.Image(label="Input Image")
+outputs = [gr.Image(label="Raw Segmentation"), gr.Image(label="Segmentation Overlay")]
 images_dir = "sample_sat_images/"
 examples = [f"{images_dir}/{image_id}" for image_id in os.listdir(images_dir)]
-title = "Satellite Images Landcover Segmentation"
-description = "Upload an image or select from examples to segment"
+title = "Satellite Images Landcover Classification"
+description = (
+    "Upload a satellite image from your computer or select one from"
+    " the examples to automatically. The model will segment the landcover"
+    "  types from a preselected set of possible types."
+)
+article = open("article.md", "r").read()
+
 
 iface = gr.Interface(
     segment,
-    i,
-    o,
+    inputs,
+    outputs,
     examples=examples,
     title=title,
     description=description,
     cache_examples=True,
+    article=article,
 )
 iface.launch()

article.md

@@ -0,0 +1,29 @@
+## **Problem statement**
+
+The objective of this task is to classify different landcover types in a satellite image. This problem is approached as a machine learning task known as semantic segmentation, where the goal is to predict the class label for each individual pixel in the image.
+
+## **Dataset**
+The [dataset](https://www.kaggle.com/datasets/balraj98/deepglobe-land-cover-classification-dataset) used for this project is from the 2018 DeepGlobe Landcover Classification Challenge. It consists of a total of 803 satellite images, each with dimensions of 2448x2448 pixels. Each image in the dataset is accompanied by a segmentation mask that assigns class labels to the pixels.
+
+| Landcover Name            | Color                    | Explanation / Function                                         |
+| -------------------- | ------------------------ | ------------------------------------------------------------ |
+| Urban land           | <span style="color:cyan">Cyan</span>      | Man-made, built-up areas with human artifacts                |
+| Agriculture land     | <span style="color:yellow">Yellow</span>  | Farms, planned plantations, cropland, orchards                |
+| Rangeland            | <span style="color:magenta">Magenta</span> | Non-forest, non-farm, green land, grass                      |
+| Forest land          | <span style="color:green">Green</span>     | Land with at least 20% tree crown density and clear cuts     |
+| Water                | <span style="color:blue">Blue</span>       | Rivers, oceans, lakes, wetlands, ponds                       |
+| Barren land          | <span style="color:gray">White</span>      | Mountains, rocks, deserts, beaches, vegetation-free land     |
+| Unknown              | <span style="color:black">Black</span>     | Clouds and others                                             |
+
+## **Model**
+For this task, we utilized a pre-trained UNet model with weights pretrained on the ImageNet dataset. We then fine-tuned the UNet using the DeepGlobe Landcover Classification dataset. The training process took approximately 2 hours using a single NVIDIA T4 GPU.
+## **Team members**
+David Mora
+
+Eduard's Mendez
+
+Santiago Ahumada
+
+## **Aditional information**
+
+If you are interested in contributing to the project or just getting more information about the details you can head over to our GitHub [repository](https://github.com/DavidFM43/landcover-segmentation).