Beta b

.gitignore

@@ -1,11 +1,11 @@
 # Exclude Visual Studio Code project settings
-/.vscode
+/.vscode 
 
-# Exclude Visual Studio project settings
+# Exclude Visual Studio project settings  
 /.vs
 
 # Exclude database files
-/Database
+/Database 
 
 # Exclude downloaded files
 /download
@@ -25,23 +25,23 @@ Samples/*
 # Do not ignore directories in logs/fit that end with _STR
 !logs/fit/*_STR/
 
-# Do not ignore directories in Samples that end with _STR
+# Do not ignore directories in Samples that end with _STR  
 !Samples/*_STR/
 
 # Do not ignore .gz files in directories in Samples that end with _STR
 !Samples/*_STR.gz
 
 # Exclude specific model weight files
 /PAI_model_weights.h5
-/PAI_model_weights_BL.h5
+/PAI_model_weights_BL.h5 
 /PAI_model_T.h5
 /PAI_model_T_BL.h5
 
-# Exclude cache files
+# Exclude cache files  
 /cache
 
 # Exclude build artifacts
-/Build
+/Build 
 
 # Exclude validation files
 /validation
@@ -57,7 +57,7 @@ Samples/*
 /freearc1.tmp
 
 # Exclude logs and dataset files in the CLI interface
-/Interface/CLI/Data/logs
+/Interface/CLI/Data/logs  
 /Interface/CLI/Data/dataset.npy
 
 # Exclude temporary Python version file in the CLI interface
@@ -69,28 +69,29 @@ Samples/*
 # Exclude temporary Python version file in the GUI interface
 /Interface/GUI/Data/Python Ver.tmp
 
-# venv
-/venv_2
+# Virtual environment directories
+/venv_2  
 /venv
 
-# Python Embed
+# Python Embed directory
 /Interface/CLI/Data/Python Embed 3.10.11
 /Interface/CLI/Data/Use_Python_Embed.tmp
 /Interface/CLI/Python.Embed.3.10.11.exe
 
-# GUI DEV
+# GUI development file
 /Interface/GUI/GUI_DEV.cmd
 
 # Exclude logs in GUI interface
 /Interface/GUI/Data/logs
 
-# Other
+# Other excluded files
 /Test_ENV_G.py
 /scc.exe
 /SCC_Auto.cmd
 /Microsoft.PowerShell_profile_Nvidia_smi.ps1
 /Data/image_SUB_generator.pkl
 /GPU_Info.txt
-/Build.py
-/env
-/Temp
+/Build.py
+/env
+/Temp
+/Test.md

Create_requirements.cmd

@@ -1,4 +1,5 @@
 @echo off
+echo Generating requirements.txt...
 del requirements.txt >nul  2>&1
 echo Y | pigar -l ERROR generate
 

Create_tensorboard_i.cmd

@@ -1 +1 @@
-tensorboard --logdir logs\fit --reload_interval 30 --reload_multifile True
+tensorboard --logdir logs\fit --reload_interval 5 --reload_multifile True

Interface/CLI/Data/Utils/Other.py

@@ -72,7 +72,7 @@ def P_warning(msg):
     Args:
         msg (str): The warning message to print.
     """
-    print_Color_V2(f'<light_red>Warning: <yellow>{msg}')
+    print_Color_V2(f'<light_red>Warning: <yellow>{msg} (⚠️)')
 
 def evaluate_model_full(y_test, model_pred, model=None, x_test=None):
     """Evaluates a machine learning model on a test set.

Interface/GUI/Data/Utils/Other.py

@@ -72,7 +72,7 @@ def P_warning(msg):
     Args:
         msg (str): The warning message to print.
     """
-    print_Color_V2(f'<light_red>Warning: <yellow>{msg}')
+    print_Color_V2(f'<light_red>Warning: <yellow>{msg} (⚠️)')
 
 def evaluate_model_full(y_test, model_pred, model=None, x_test=None):
     """Evaluates a machine learning model on a test set.

README.md

@@ -12,8 +12,8 @@
 
 ### This project uses a deep learning model built with the TensorFlow Library to detect pneumonia in X-ray images. The model architecture is based on the EfficientNetB7 model, which has achieved an accuracy of approximately 97.12% (97.11538%) on our test data. This high accuracy rate is one of the strengths of our AI model.
 > [!IMPORTANT]
-> The code that have achived the highest acc is `backup/V6/Model_T&T.ipynb`.\
-> And the code with the light model is `backup/V7/Model_T&T.ipynb`.
+> The code that had achieved the highest acc is `backup/V6/Model_T&T.ipynb`.\
+> And the codes with the light and super light models are `backup/V7/Model_T&T.ipynb`, `backup/V8/Model_T&T.ipynb`.
 
 ## Usage
 > [!TIP]
@@ -73,13 +73,14 @@ This combined dataset provides a comprehensive set of images for training the mo
 ### Model list:
 | Model    | Base Model      | Params | acc    | Status |
 |----------|-----------------|--------|--------|--------|
-| V6       | efficientnet-b7 | 65.4M  | 97.12% |   ✅  |
+| V6       | efficientnet-b7 | 65.4M  | 97.12% |   ✅   |
 | V7 light | efficientnet-b4 | 29.7M  | 97.12% |   ✅  |
+| V8 Super light | efficientnet-b0 | 4.8M  | 96.47% |   ✅  |
 
 ## Training Methods
 ### The AI model supports two distinct training approaches:
 
-- rev1: A straightforward method using Keras' fit function for basic training.
+- rev1: A straightforward method using Keras fit function for basic training.
 - rev2: An enhanced training strategy incorporating data augmentation and subset training for improved accuracy and generalization.
 ### rev2 Training Simplified:
 - Memory Optimization: Begins with clearing system memory to ensure efficient resource utilization.
@@ -119,7 +120,7 @@ The model provided in this project should not be used for medical diagnosis with
 ## Results
 
 > [!WARNING]
-> Results were achived using Rev2 training method and Rev1.2 model and
+> Results were achieved using Rev2 training method and Rev1.2 model and
 > with `backup/V6/Model_T&T.ipynb` code.
 <!-- #### N/A --> 
 ### Acc:
@@ -129,6 +130,7 @@ The model provided in this project should not be used for medical diagnosis with
 |----------|----------|
 | V6       | ![img_](doc/V6+/D1.png)![img_](doc/V6+/D2.png)![img_](doc/V6+/D3.png)|
 | V7 light | 🚧None🚧|
+| V8 super light | 🚧None🚧|
 ### Other:
 ![img_](doc/V6/D4.png)
 <!--

SECURITY.md

@@ -3,8 +3,9 @@
 ## Model list:
 | Model    | Base Model      | Params | acc    | Status |
 |----------|-----------------|--------|--------|--------|
-| V6       | efficientnet-b7 | 65.4M  | 97.12% |   ✅  |
+| V6       | efficientnet-b7 | 65.4M  | 97.12% |   ✅   |
 | V7 light | efficientnet-b4 | 29.7M  | 97.12% |   ✅  |
+| V8 Super light | efficientnet-b0 | 4.8M  | 96.47% |   ✅  |
 
 ## CLI:
 The last version of CLI is V0.8.9.3 and currently its has entered a minimal maintenance and not being updated.