Update Readme with config usage

Add config usage in Readme

Update README with config usage instructions

README.md

@@ -59,11 +59,75 @@ This repository aims at mirroring popular semantic segmentation architectures in
 ### Data
 
 * Download data for desired dataset(s) from list of URLs [here](https://meetshah1995.github.io/semantic-segmentation/deep-learning/pytorch/visdom/2017/06/01/semantic-segmentation-over-the-years.html#sec_datasets).
-* Extract the zip / tar and modify the path appropriately in `config.yaml`
+* Extract the zip / tar and modify the path appropriately in your `config.yaml`
 
 
 ### Usage
 
+**Setup config file**
+
+```yaml
+# Model Configuration
+model:
+    arch: <name> [options: 'fcn[8,16,32]s, unet, segnet, pspnet, icnet, icnetBN, linknet, frrn[A,B]'
+    <model_keyarg_1>:<value>
+
+# Data Configuration
+data:
+    dataset: <name> [options: 'pascal, camvid, ade20k, mit_sceneparsing_benchmark, cityscapes, nyuv2, sunrgbd, vistas'] 
+    train_split: <split_to_train_on>
+    val_split: <spit_to_validate_on>
+    img_rows: 512
+    img_cols: 1024
+    path: <path/to/data>
+    <dataset_keyarg1>:<value>
+
+# Training Configuration
+training:
+    n_workers: 64
+    train_iters: 35000
+    batch_size: 16
+    val_interval: 500
+    print_interval: 25
+    loss:
+        name: <loss_type> [options: 'cross_entropy, bootstrapped_cross_entropy, multi_scale_crossentropy']
+        <loss_keyarg1>:<value>
+
+    # Optmizer Configuration
+    optimizer:
+        name: <optimizer_name> [options: 'sgd, adam, adamax, asgd, adadelta, adagrad, rmsprop']
+        lr: 1.0e-3
+        <optimizer_keyarg1>:<value>
+
+        # Warmup LR Configuration
+        warmup_iters: <iters for lr warmup>
+        mode: <'constant' or 'linear' for warmup'>
+        gamma: <gamma for warm up>
+
+    # Augmentations Configuration
+    augmentations:
+        gamma: x                                     #[gamma varied in 1 to 1+x]
+        hue: x                                       #[hue varied in -x to x]
+        brightness: x                                #[brightness varied in 1-x to 1+x]
+        saturation: x                                #[saturation varied in 1-x to 1+x]
+        contrast: x                                  #[contrast varied in 1-x to 1+x]
+        rcrop: [h, w]                                #[crop of size (h,w)]
+        translate: [dh, dw]                          #[reflective translation by (dh, dw)]
+        rotate: d                                    #[rotate -d to d degrees]
+        scale: [h,w]                                 #[scale to size (h,w)]
+        ccrop: [h,w]                                 #[center crop of (h,w)]
+        hflip: p                                     #[flip horizontally with chance p]
+        vflip: p                                     #[flip vertically with chance p]
+
+    # LR Schedule Configuration
+    lr_schedule:
+        name: <schedule_type> [options: 'constant_lr, poly_lr, multi_step, cosine_annealing, exp_lr']
+        <scheduler_keyarg1>:<value>
+
+    # Resume from checkpoint  
+    resume: <path_to_checkpoint>
+```
+
 **To train the model :**
 
 ```