project description

README.md

@@ -9,5 +9,43 @@ app_file: app.py
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 short_description: 🔢 Digit matching & recognition  through Siamese CNN
 ---
+# 🔢 Siamese CNN: Number Recognition & Similarities
+📩
+<a href="mailto:lucagabri98@live.it"><img src="https://img.shields.io/badge/lucagabri98-c71610?style=flat&logo=gmail&logoColor=white" width="80"></a>
+<a href="la-databizanalyst"><img src="https://img.shields.io/badge/%40LA-0E76A8?style=flat&logo=linkedin&logoColor=white" width="32"></a>
+📩  
+**Note:** See model performances and examples [here 📈](https://github.com/Lulloooo/SiameseNet-NumRecognition/blob/main/Performances-Examples.md)
+<br/><br/>
+## 🎯 PURPOSES
+This project aims to develop a Siamese Neural Network 🧠 able to match and identify different kinds of numerical digits 🔢.  
+The model learns to measure how similar two images are rather than directly classifying them. This makes it suitable for tasks like:  
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+- **🤝 Digits matching and recognition**: Compare the uploaded digits with those in the test set. Once a match is found, label the uploaded digit with the same label as the matched one.
+- **🔓 Security unlocking mechanisms**: The camera captures an istant image of a face or an object. If the similarity with those in the training pool is high, it unlocks the device/program.
+- **🔎 Duplicate detection**: Define a similarity score between the external image and those in the reference set. If similarity with one of these is high, it is likely is a duplicate.
+<br/><br/>
+## 🛠️ WORKFLOW
+The workflow is quite straightforward: the user is prompted to upload a picture of a number or to draw it in a canvas, and the Siamese Network returns:  
+- 1️⃣ Which digit is the uploaded/drawn picture (The uploaded img is a ...) along with its similarity score.
+- 2️⃣ 3 matching pics coming from the testing dataset.
+<br/><br/>
+## ⚠️ WARNINGS 
+Be fully aware of the model's limitations by reading the [model limitations doc ✋](https://github.com/Lulloooo/SiameseNet-NumRecognition/blob/main/Performances-Examples.md)
+<br/><br/>
+## 🧠 MODEL OVERVIEW  
+The Siamese architecture leverages shared convolutional layers to extract features from two images, then computes their Euclidean distance in embedding space. A contrastive loss function is employed:
+
+&nbsp;&nbsp;**L = (1 - Y) * (1/2) * D^2 + Y * (1/2) * (max(0, m - D))^2**
+
+with:
+- D: distance between embeddings
+- Y = 0 for similar and Y = 1 for dissimilar
+- m: margin parameters (can be changed)
+<br/><br/>
+
+__________________________________________________________________________________________________________
+**Note**: I got the idea to implement this kind of network for this kind of task + some code chunks from the [Daily Dose of Data Science](https://www.dailydoseofds.com/) newsletter 📨. They are really awesome, and everyone interested in data science should check them too 😉.  
+  
+
+**Note2**: As this was a work-related project, it has been approved for posting, and all sensitive information and data are omitted to protect privacy. Code snippet containing sensitive data has not been posted. 
+__________________________________________________________________________________________________________