From a80d996b0cf2c275b3282fde795bb735588e668c Mon Sep 17 00:00:00 2001
From: Tinsae <tinsaekahsay@gmail.com>
Date: Sat, 2 May 2026 00:08:38 +0200
Subject: [PATCH] readme

---
 ReadME.md | 43 ++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 42 insertions(+), 1 deletion(-)

diff --git a/ReadME.md b/ReadME.md
index ca2373b..f44ec5d 100644
--- a/ReadME.md
+++ b/ReadME.md
@@ -36,5 +36,46 @@ Root_dir/
 
 ```
 
-once this is done manually
+once this is manually done, We can run finetunning a selected model. For now, 4 models are implemented.
+- ResNet-18
+- ResNet-50
+- DenseNet121
+- Inception
+
+## Fine tuning
+### Preparation
+
+Lets say we want to finetune **Inception**. In `Tune.py` we have to adjust the variables accordingly like so:
+```py
+# Set the class size. e.g
+CLASS_SIZE = 50
+# set the batch eg.
+BATCH_SIZE = 16
+# set the Tuning epochs. e.g
+EPOCH = 20
+# set the correct image size
+# if ResNet or DenseNet, we set this to 224
+RESOLUTION = 299 
+# set the model architecture
+arch = Architecture.INCEPTION
+```
+Other variable that we can change are those that are related to data size.  Namely Training sample size and full sample size. 
+```py
+# full sample size per class
+SAMPLE_SIZE = 30
+
+# Training sample size is then (full_sample - test_sample)
+TRAINING_SMPLE = 28
+
+# while at it, we can also set the learning rate
+LR_RATE = 0.0001
+```
+
+
+### Rune the process
+After we have set all necessary variables to our liking, we run the process by running Tune.py with python interpreter
+```shell
+# open terminal, cd to project root and run
+python Tune.py
+```