updates

README.md

@@ -3,7 +3,7 @@
 # Introduction
 
 This directory contains software developed by Ultralytics LLC, and **is freely available for redistribution under the GPL-3.0 license**. For more information on Ultralytics projects please visit:
-http://www.ultralytics.com  
+http://www.ultralytics.com
 
 # Description
 

test.py

@@ -42,13 +42,11 @@ elif weights_path.endswith('.pt'):  # pytorch format
 
 model.to(device).eval()
 
-# Get PyTorch dataloader
+# Get dataloader
 # dataset = load_images_with_labels(test_path)
 # dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batch_size, shuffle=False, num_workers=opt.n_cpu)
 dataloader = load_images_and_labels(test_path, batch_size=opt.batch_size, img_size=opt.img_size)
 
-Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
-
 n_gt = 0
 correct = 0
 
@@ -87,11 +85,6 @@ for batch_i, (imgs, targets) in enumerate(dataloader):
             correct.extend([0 for _ in range(len(detections))])
         else:
             # Extract target boxes as (x1, y1, x2, y2)
-            # target_boxes = torch.FloatTensor(annotations[:, 1:].shape)
-            # target_boxes[:, 0] = (annotations[:, 1] - annotations[:, 3] / 2)
-            # target_boxes[:, 1] = (annotations[:, 2] - annotations[:, 4] / 2)
-            # target_boxes[:, 2] = (annotations[:, 1] + annotations[:, 3] / 2)
-            # target_boxes[:, 3] = (annotations[:, 2] + annotations[:, 4] / 2)
             target_boxes = xywh2xyxy(annotations[:,1:5])
             target_boxes *= opt.img_size
 

utils/utils.py

@@ -368,10 +368,10 @@ def plotResults():
     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/resultsBCE2.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.title(s[i])
-        plt.legend()
+        plt.legend(cocococosadfc)