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@@ -3,6 +3,40 @@ Implementation of [NIMA: Neural Image Assessment](https://arxiv.org/abs/1709.054
 
 NIMA assigns a Mean + Standard Deviation score to images, and can be used as a tool to automatically inspect quality of images or as a loss function to further improve the quality of generated images.
 
+Contains weights trained on the AVA dataset for the following models:
+- MobileNet
+- NASNet Mobile
+
+# Usage
+## Evaluation
+There are `evaluate_*.py` scripts which can be used to evaluate an image using a specific model. The weights for the specific model must be downloaded from the [Releases Tab](https://github.com/titu1994/neural-image-assessment/releases) and placed in the weights directory.
+
+Change the image to evaluate by changing the path of the image in the variable `img_path`. Support for passing arguments to these evaluate scripts will come eventually.
+
+## Training
+The AVA dataset is required for training these models. I used 250,000 images to train and the last 5000 images to evaluate (this is not the same format as in the paper).
+
+First, ensure that the dataset is clean - no currupted JPG files etc by using the `check_dataset.py` script in the utils folder. If such currupted images exist, it will drastically slow down training since the Tensorflow Dataset buffers will constantly flush and reload on each occurance of a currupted image.
+
+Then, there are two ways of training these models.
+### Direct-Training
+In direct training, you have to ensure that the model can be loaded, trained, evaluated and then saved all on a single GPU. If this cannot be done (because the model is too large), refer to the Pretraining section.
+
+Use the `train_*.py` scripts for direct training. Note, if you want to train other models, copy-paste a train script and only edit the `base_model` creation part, everythin else should likely be the same.
+
+### Pre-Training
+If the model is too large to train directly, training can still be done in a roundabout way (as long as you are able to do inference with a batch of images with the model).
+
+**Note** : One obvious drawback of such a method is that it wont have the performance of the full model without further finetuning. 
+
+This is a 3 step process:
+
+1)  **Extract features from the model**: Use the `extract_*_features.py` script to extract the features from the large model. In this step, you can change the batch_size to be small enough to not overload your GPU memory, and save all the features to 2 TFRecord objects.
+
+2) **Pre-Train the model**: Once the features have been extracted, you can simply train a small feed forward network on those features directly. Since the feed forward network will likely easily fit onto memory, you can use large batch sizes to quickly train the network.
+
+3) **Fine-Tune the model**: This step is optional, only for those who have sufficient memory to load both the large model and the feed forward classifier at the same time. Use the `train_nasnet_mobile.py` as reference as to how to load both the large model and the weights of the feed forward network into this large model and then train fully for several epochs at a lower learning rate.
+
 # Example
 <img src="https://github.com/titu1994/neural-image-assessment/blob/master/images/NIMA.jpg?raw=true" height=100% width=100%>
 
@@ -11,4 +45,3 @@ NIMA assigns a Mean + Standard Deviation score to images, and can be used as a t
 # Requirements
 - Keras
 - Tensorflow (CPU to evaluate, GPU to train)
-- skimage