Merge pull request #9 from dronedeploy/minor_readme_edits

minor readme edits

Author: nickponline

README.md

@@ -24,19 +24,19 @@ python3 main_keras.py
 python3 main.py
 ```
 
-This will download the sample dataset and begin training a model. You can monitor training performance on [Weights and Biases](https://www.wandb.com/). Once training is complete inference will be performed on all test scenes and a number of prediction images with names like `123123_ABCABC-prediction.png` will be created in the `wandb` directory. After the the images are created they will be scored. Here's what a prediction looks like, not bad for 50 lines of code but there is a lot of room for improvement:  
+This will download the sample dataset and begin training a model. You can monitor training performance on [Weights & Biases](https://www.wandb.com/). Once training is complete, inference will be performed on all test scenes and a number of prediction images with names like `123123_ABCABC-prediction.png` will be created in the `wandb` directory. After the images are created they will be scored, and those scores stored in the `predictions` directory. Here's what a prediction looks like - not bad for 50 lines of code, but there is a lot of room for improvement:
 
 ![Example](https://github.com/dronedeploy/dd-ml-segmentation-benchmark/raw/master/img/out.gif)
 
 ### Dataset Details
 
-The dataset comprises a number of aerial scenes captured from drones. Each scene has a ground resolution of 10 cm per pixel. For each scene there is a corresponding "image", "elevation" and "label". These are located in the `images`, `elevation` and `labels` directory.
+The dataset comprises a number of aerial scenes captured from drones. Each scene has a ground resolution of 10 cm per pixel. For each scene there is a corresponding "image", "elevation" and "label". These are located in the `images`, `elevation` and `labels` directories.
 
-The images an RGB tifs, the elevations are single channel floating point tifs (where each pixel value represents elevation in meters) and finally the labels are PNGs with 7 colors representing the 7 classes (documented below)
+The images are RGB TIFFs, the elevations are single channel floating point TIFFs (where each pixel value represents elevation in meters), and finally the labels are PNGs with 7 colors representing the 7 classes (documented below).
 
 In addition please see `index.csv` - inside the downloaded dataset folder - for a description of the quality of each labelled image and the distribution of the labels.
 
-To use a dataset for training it need to first be converted to chips (see `images2chips.py`). This will created an `images-chips` and `label-chips` directory which contains a number of `300x300` (by default) RGB images. The `label-chips` are also RGB but will be very low pixel intensities `[0 .. 7]` so will appear black as first glance. You can use the `color2class` and `category2mask` function to switch between the two label representations. 
+To use a dataset for training, it must first be converted to chips (see `images2chips.py`). This will create two directories, `images-chips` and `label-chips`, which will contain a number of `300x300` (by default) RGB images. The `label-chips` are also RGB but will be very low pixel intensities `[0 .. 7]` so will appear black as first glance. You can use the `color2class` and `category2mask` function to switch between the two label representations.
 
 Here is an example of one of the labelled scenes:
 
@@ -62,7 +62,7 @@ Color (Blue, Green, Red) to Class Name:
 ----
 The sample implementation is very basic and there is immediate opportunity to experiment with:
 - Data augmentation (`dataloader.py`)
-- Hyper- parameters (`train_*.py`)
+- Hyperparameters (`train_*.py`)
 - Post-processing (`inference_*.py`)
 - Chip size (`images2chips.py`)
 - Model architecture (`train_*.py`)