adding read me

README.md

@@ -1,26 +1,9 @@
-## Human Emotion Detection
-A full fledged application that detects human emotion from an Image.
-In this project, I have finetuned Efficient model to achieve 80% accuracy on the validation dataset.
-
-This project uses human emotions dataset from Kaggle to finetune efficientnet model
-
-The application is created using flask framework and deployed on AWS EC2 instance . The docker image was pushed to ECR and pulled into EC2 instance using Github actions as a part of CI/CD pipeline implementation
-
-I tried using TransferNet approach and Finetuning approach. With Transfernet, the model accuracy was only 63% while it increased to 80% with a finetuned model
-
-## Evaluation Metrics
-
-Loss: 0.5
-Accuracy: 80%
-Top_k_accuracy: 93%
-
-### Example: How finetuning improved the model performance
-
-Before finetuning, this image was incorrecly labeled as Sad
-
-![Incorrect labeling with transfer learning](TransferLearning.png)
-
-After finetuning, this image was correctly labeled as happy
-
-![Correct labeling with finetuned model](Finetuning.png)
-
+---
+title: Human Emotion Detection
+emoji: 🧠
+colorFrom: blue
+colorTo: indigo
+sdk: docker
+app_file: app.py
+pinned: false
+---