From 7b0572a1e8fa72a70ea2de6a5b48160cbf35c6ad Mon Sep 17 00:00:00 2001
From: TiagoFerreira-lab <tiagodias_95@hotmail.com>
Date: Fri, 7 Mar 2025 11:51:39 +0000
Subject: [PATCH] Update README.md

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 README.md | 73 +++++++++++++++++++++++++++++++++++++++++++++++++------
 1 file changed, 66 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index fed8a0a..d4c8a7e 100644
--- a/README.md
+++ b/README.md
@@ -5,29 +5,88 @@ Build a Convolutional Neural Network (CNN) model to classify images from a given
 [Task Descriptions and Project Instructions](https://github.com/ironhack-labs/project-1-deep-learning-image-classification-with-cnn)
 
 
-## Project Results
+## 📊 Project Results
 In this project, we classified images from the animals 10 data set.
 - Pre-processed data 
 - Built a sequential CNN model 
 - Optimized the model
-- Prediction accuracy of: 80,99%
+- Prediction accuracy of: 81.63% 
 
+---
 
-## Repository Folders and Files
+## 📂 Repository Folders and Files
 
 Here is a short description of the folder and files available on the repository.
 
 
 ### Documents
 - Presentation
-- holdout_subset.zip. You can use these images to predict with the model
 
 ### Notebooks  
 - split_validation_set: split the data set to one set for training and testing (90%) and a second one to make predictions (10%)
 - model_1.ypynb : The starting point model
 - model_optimized_ypnb: The optimized model
-- transfer_learning_winner.ipynb: using VGG16 to predict the data set
+- test_images: folder of unseen images to test on the model
 
+---
 
-## Installation
-Use **requirements.txt** to install the required packages to run the notebooks.
+## 📦 Required Modules and Dependencies
+
+The following modules are required for running this project:
+
+### 🔹 **System & File Management**
+- `os` – Handles file paths and directory operations.  
+- `zipfile` – Extracts compressed datasets.  
+- `time` – Measures execution time of processes.  
+- `google.colab.drive` – Mounts Google Drive for dataset storage.  
+- `gdown` – Downloads datasets from Google Drive.
+
+### 🔹 **Data Preprocessing & Image Handling**
+- `tensorflow.keras.preprocessing.image` – Loads, processes, and augments images.  
+- `ImageDataGenerator` – Applies **data augmentation** to improve model generalization.  
+- `opencv` (`cv2`) – (Optional) Handles advanced image processing.  
+- `numpy` – Handles numerical operations for image arrays.  
+
+### 🔹 **Deep Learning: TensorFlow & Keras**
+- `tensorflow` – Provides the framework for CNN training and inference.  
+- `tensorflow.keras.models.Sequential` – Defines a sequential CNN model.  
+- `tensorflow.keras.layers.Conv2D` – Extracts features from images using convolutional layers.  
+- `tensorflow.keras.layers.MaxPooling2D` – Reduces spatial dimensions while retaining key features.  
+- `tensorflow.keras.layers.Flatten` – Converts feature maps into a **1D vector** for dense layers.  
+- `tensorflow.keras.layers.Dense` – Fully connected layers for classification.  
+- `tensorflow.keras.layers.Dropout` – Regularization technique to prevent **overfitting**.  
+- `tensorflow.keras.layers.BatchNormalization` – Normalizes activations for **stable training**.  
+- `tensorflow.keras.regularizers.l2` – Applies **L2 Regularization** to prevent **overfitting**.  
+
+### 🔹 **Model Optimization & Callbacks**
+- `tensorflow.keras.callbacks.ReduceLROnPlateau` – Adjusts the **learning rate** dynamically when training slows down.  
+- `tensorflow.keras.callbacks.EarlyStopping` – Stops training when validation performance **stagnates**.  
+- `tensorflow.keras.optimizers.Adam` – Adaptive **optimization algorithm** for faster convergence.  
+- `tensorflow.keras.backend as K` – Provides **low-level functions** for manipulating tensors.  
+
+### 🔹 **Model Evaluation & Metrics**
+- `sklearn.metrics.confusion_matrix` – Computes the confusion matrix for model predictions.  
+- `sklearn.metrics.classification_report` – Provides a summary of **precision, recall, and F1-score**.  
+- `sklearn.metrics.precision_score` – Measures how many of the predicted positives are actually correct.  
+- `sklearn.metrics.recall_score` – Evaluates how well the model identifies **true positives**.  
+- `sklearn.metrics.f1_score` – Balances **precision and recall** into a single metric.  
+
+### 🔹 **Data Visualization**
+- `matplotlib.pyplot` – Plots **loss curves, accuracy trends, and sample images**.  
+- `seaborn` – Creates **confusion matrix heatmaps** for visualizing model predictions.  
+
+---
+
+## ⚙️ Installation
+Before running the project, ensure that all required dependencies are installed.
+
+### **🔹 Install Using `requirements.txt`**
+To install all necessary libraries at once, run:
+
+```bash
+pip install -r requirements.txt
+
+### Additional Folders
+**test_images**: folder of unseen images to test on the model
+
+---