diff --git a/README.md b/README.md
index 83fbf691..b0b73bf2 100755
--- a/README.md
+++ b/README.md
@@ -45,7 +45,10 @@ HS**V** Intensity | +/- 50%
 
 # Inference
 
-Checkpoints are saved in `/checkpoints` directory. Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder, shown here.
+Checkpoints are saved in `/checkpoints` directory. Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder, shown here. Alternatively you can use the official YOLOv3 weights:
+
+-PyTorch format: https://storage.googleapis.com/ultralytics/yolov3.pt
+-darknet format: https://pjreddie.com/media/files/yolov3.weights
 
 ![Alt](https://github.com/ultralytics/yolov3/blob/master/data/zidane_result.jpg "inference example")
 
diff --git a/train.py b/train.py
index 27074389..514195c2 100644
--- a/train.py
+++ b/train.py
@@ -68,7 +68,7 @@ def main(opt):
         # optimizer = torch.optim.SGD(model.parameters(), lr=.001, momentum=.9, weight_decay=5e-4, nesterov=True)
         # optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()))
         optimizer = torch.optim.Adam(model.parameters())
-        optimizer.load_state_dict(checkpoint['optimizer'])
+        #optimizer.load_state_dict(checkpoint['optimizer'])
 
         start_epoch = checkpoint['epoch'] + 1
         best_loss = checkpoint['best_loss']
@@ -79,6 +79,7 @@ def main(opt):
             print('Using ', torch.cuda.device_count(), ' GPUs')
             model = nn.DataParallel(model)
         model.to(device).train()
+        # optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=.9, weight_decay=5e-4)
         optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=1e-4, weight_decay=5e-4)
 
     # Set scheduler
diff --git a/utils/gcp.sh b/utils/gcp.sh
index 71e790d8..2c9da943 100644
--- a/utils/gcp.sh
+++ b/utils/gcp.sh
@@ -4,9 +4,11 @@
 sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3 && cd yolov3 && python3 train.py -img_size 416 -epochs 160
 
 # Resume
-cd yolov3 && python3 train.py -img_size 416 -resume 1
+python3 train.py -img_size 416 -resume 1
 
 # Detect
 gsutil cp gs://ultralytics/fresh9_5_e201.pt yolov3/checkpoints
-cd yolov3 && python3 detect.py
+python3 detect.py
 
+# Test
+python3 test.py -img_size 416 -weights_path checkpoints/latest.pt
diff --git a/utils/utils.py b/utils/utils.py
index 17b8f9bd..abd42c76 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -367,11 +367,11 @@ def plotResults():
     import matplotlib.pyplot as plt
     plt.figure(figsize=(16, 8))
     s = ['X', 'Y', 'Width', 'Height', 'Objectness', 'Classification', 'Total Loss', 'Precision', 'Recall']
-    for f in ('/Users/glennjocher/Downloads/results.txt',
-              '/Users/glennjocher/Downloads/resultsBCE.txt'):
+    for f in ('/Users/glennjocher/Downloads/results_CE.txt',
+              '/Users/glennjocher/Downloads/results_BCE.txt'):
         results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 7, 8, 9, 10]).T
         for i in range(9):
             plt.subplot(2, 5, i + 1)
-            plt.plot(results[i, :], marker='.', label=f)
+            plt.plot(results[i, :19], marker='.', label=f)
             plt.title(s[i])
-        plt.legend()
+        plt.legend
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