Merge pull request #3 from dronedeploy/quickstart

quickstart and store in dir

Author: nickponline

README.md

@@ -5,6 +5,24 @@ This repository contains a description of the DroneDeploy Segmentation Dataset a
 
 ![Example](https://github.com/dronedeploy/dd-ml-segmentation-benchmark/raw/master/img/example.jpg)
 
+### Quickstart
+
+```
+# clone this repository
+git clone https://github.com/dronedeploy/dd-ml-segmentation-benchmark.git
+cd dd-ml-segmentation-benchmark/
+# install requirements
+pip3 install -r requirements.txt
+# optional: log in to W&B to see your training metrics,
+# track your experiments, and submit your models to the benchmark
+wandb login
+# train Fastai model
+python3 main.py
+# train Keras model
+python3 main_keras.py
+
+```
+
 ### Training
 
 To start training a model on a small sample dataset run the following, once working you should use the *full dataset*  by changing `main.py`

libs/scoring.py

@@ -25,7 +25,8 @@ def wherecolor(img, color, negate = False):
 def plot_confusion_matrix(y_true, y_pred, classes,
                           normalize=True,
                           title=None,
-                          cmap=plt.cm.Blues):
+                          cmap=plt.cm.Blues,
+                          savedir="score_files"):
     """
     This function prints and plots the confusion matrix.
     Normalization can be applied by setting `normalize=True`.
@@ -75,8 +76,11 @@ def plot_confusion_matrix(y_true, y_pred, classes,
     plt.ylim([-0.5, cm.shape[0]- 0.5])
 
     fig.tight_layout()
+    # save to directory
+    if not os.path.isdir(savedir):
+        os.mkdir(savedir)
+    savefile = savedir + '/score-' + title
 
-    savefile = os.path.join(base, 'score-' + fname)
     plt.savefig(savefile)
     return savefile, cm