optimised

ReadME.md

@@ -0,0 +1,85 @@
+# Python venv
+Start a python environment here in this directory
+```py
+python -m venv .
+```
+
+Then  we start the env using
+```py
+source ./bin/activate
+```
+
+We can then install whats needed with `pip`. for exampe
+we can put all dependencies in some text file. say dependencies.txt
+```py
+# pip install
+# already added dependencies.txt
+pip install -r dependencies.txt
+
+```
+
+Downloading the data from google drive was impossible. So Downloaded them manualy
+and They need to be put in the a ./data directory
+The download url was available in the error log.
+`https://drive.google.com/uc?id=0B7EVK8r0v71pZjFTYXZWM3FlRnM` 
+this is the same location thats available in the official site
+
+```
+Root_dir/
+└── data/
+    └── celeba/
+        ├── img_align_celeba.zip
+        ├── list_attr_celeba.txt
+        ├── list_bbox_celeba.txt
+        ├── list_eval_partition.txt
+        └── list_landmarks_align_celeba.txt
+
+```
+
+once this is manually done, We can run finetunning a selected model. For now, 8 models are implemented.
+- ResNet-18
+- ResNet-50
+- DenseNet121
+- Inception
+- GoogleNet
+- ShuffleNet
+- EfficientNet
+- WideResNet
+
+## 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
+```
+