update README

README.md

@@ -9,4 +9,31 @@ app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Generative Augmented Classifiers
+
+Main GitHub Repo: [Generative Data Augmentation](https://github.com/zhulinchng/generative-data-augmentation) | Image Classification Demo: [Generative Augmented Classifiers](https://huggingface.co/spaces/czl/generative-augmented-classifiers).
+
+This demo showcases the performance of image classifiers trained on various datasets as part of the project `Investigating the Effectiveness of Generative Diffusion Models in Synthesizing Images for Data Augmentation in Image Classification' dissertation.
+
+## Demo Usage Instructions
+
+1. Select the dataset, the model architecture, training methods, type of training dataset to evaluate the classifier on.
+2. Upload an image, or click  `Sample Random Image` to select a random image from the validation dataset.
+3. Click `Classify` to classify the image using the selected classifier.
+4. To download the classifier, click `Download Model: <model_name>`.
+
+The top 5 predicted labels and their corresponding probabilities are displayed.
+
+## Configuration
+
+```bash
+git clone https://huggingface.co/spaces/czl/generative-augmented-classifiers
+cd generative-data-augmentation-demo
+# Setup the data directory structure as shown above
+conda create --name $env_name python=3.11.* # Replace $env_name with your environment name
+conda activate $env_name
+# Visit PyTorch website https://pytorch.org/get-started/previous-versions/#v212 for PyTorch installation instructions.
+pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url # Obtain the correct URL from the PyTorch website
+pip install -r requirements.txt
+python app.py
+```

app.py

@@ -223,7 +223,9 @@ if __name__ == "__main__":
         gr.Markdown(
             """
 # Generative Augmented Image Classifiers
-This demo showcases the performance of image classifiers trained on various datasets.
+This demo showcases the performance of image classifiers trained on various datasets as part of the project `Investigating the Effectiveness of Generative Diffusion Models in Synthesizing Images for Data Augmentation in Image Classification' dissertation.
+
+Main GitHub Repo: [Generative Data Augmentation](https://github.com/zhulinchng/generative-data-augmentation) | Generative Data Augmentation Demo: [Generative Data Augmented](https://huggingface.co/spaces/czl/generative-data-augmentation-demo).
 """
         )
         with gr.Row():
@@ -266,9 +268,7 @@ This demo showcases the performance of image classifiers trained on various data
                     outputs=[training_ds],
                 )
                 generate_button = gr.Button("Sample Random Image")
-                random_image_output = gr.Image(
-                    type="pil", label="Random Image from Validation Set"
-                )
+                random_image_output = gr.Image(type="pil", label="Image to Classify")
                 classify_button_random = gr.Button("Classify")
             with gr.Column():
                 output_label_random = gr.Label(num_top_classes=5)