Added more info for functionality in README

README.rst

@@ -3,7 +3,7 @@
     :alt: Documentation Status
 
 
-Deep Learning Toolbox for PyTorch
+PyTorch Deep Learning Toolbox
 =====================================
 
 PyDLT is a set of tools aimed to make experimenting with PyTorch_ easier 
@@ -15,6 +15,92 @@ Documentation is available here_.
 
 .. _here: http://pydlt.readthedocs.io/
 
+Features include:
+
+- Trainers (currently Vanilla, VanillaGAN, WGAN-GP, BEGAN, FisherGAN)
+
+.. code:: python3
+
+    trainer = dlt.train.VanillaGANTrainer(generator, discriminator, g_optim, d_optim)
+    for batch, (prediction, losses) in trainer(data_loader):
+        # Training happens in the iterator and relevant results are returned for each step
+
+- Built in configurable parser with arguments.
+
+.. code:: python3
+
+    opt = dlt.config.parse() # Has built in options (can add extra)
+    print('Some Settings: ', opt.experiment_name, opt.batch_size, opt.lr)
+
+- Parser compatible functions and configuration files support.
+
+.. code:: bash
+
+    $ python main.py @settings.cfg
+    Some Settings:  config_test 32 0.0001
+
+- HDR imaging support (.hdr, .exr, and .pfm formats)
+
+.. code:: python3
+
+    img = dlt.hdr.imread('test.pfm')
+    dlt.hdr.imwrite('test.exr', img)
+
+- Checkpointing of (torch serializable) objects; Network state dicts supported.
+
+.. code:: python3
+
+    data_chkp = Checkpointer('data')
+    data_chkp.save(np.array([1,2,3]))
+    a = data_chkp.load()
+
+- Image operations and easy conversions between multiple library views (torch, cv, plt)
+
+.. code:: python3
+
+    img = cv2.imread('image.jpg') # Height x Width x Channels - BGR
+    dlt.viz.imshow(img, view='cv')  # Height x Width x Channels - RGB
+    tensor_with_torch_view = cv2torch(img) # Channels x Height x Width - RGB
+
+- Easy visualization (and make_grid supporting Arrays, Tensors, Variables and lists)
+
+.. code:: python3
+
+    for batch, (prediction, loss) in trainer(loader):
+        grid = dlt.util.make_grid([ batch[0], batch[1], prediction], size(3, opt.batch_size))
+        dlt.viz.imshow(grid, pause=0.01, title='Training Progress')
+
+- Model parameter and layer input/outputs/gradients visualization.
+
+.. code:: python3
+
+    net = nn.Sequential(nn.Linear(10, 10))
+    dlt.viz.modules.forward_hook(net, [nn.Linear], tag='layer_outputs', histogram=False)
+    net(Variable(torch.Tensor(3,10)))
+
+- CSV Logger.
+
+.. code:: python3
+
+    log = dlt.util.Logger('losses', ['train_loss', 'val_loss'])
+    log({'train_loss': 10, 'val_loss':20})
+
+- Command line tool for easy plotting of CSV files (with live updating).
+
+.. code:: bash
+
+    $ dlt-plot --file losses.csv train_loss val_loss --refresh 5 --loglog True --tail 100
+
+- A minimal Progress bar (with global on/off switch).
+
+.. code:: python3
+
+    from dlt.util import barit
+    barit.silent = False # Default is False
+    for batch in barit(loader, start='Loading'):
+        pass
+
+
 Installation
 ---------------------
 
@@ -24,20 +110,23 @@ Make sure you have PyTorch_ installed. OpenCV is also required:
     
     conda install -c menpo opencv
 
-PyDLT can be installed using conda:
+conda install (recommended):
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. code:: bash
 
     conda install -c demetris pydlt
 
-Or from source:
+From source:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. code:: bash
 
     git clone https://github.com/dmarnerides/pydlt.git
     cd pydlt
     python setup.py install
 
+
 About
 --------