updates

test.py

@@ -103,15 +103,15 @@ def test(
             mean_R = np.mean(mR)
             mean_P = np.mean(mP)
 
-            # Print image mAP and running mean mAP
-            print(('%11s%11s' + '%11.3g' * 3) % (len(mAPs), dataloader.nF, mean_P, mean_R, mean_mAP))
+        # Print image mAP and running mean mAP
+        print(('%11s%11s' + '%11.3g' * 3) % (len(mAPs), dataloader.nF, mean_P, mean_R, mean_mAP))
 
     # Print mAP per class
     print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP') + '\n\nmAP Per Class:')
 
     classes = load_classes(data_cfg_dict['names'])  # Extracts class labels from file
     for i, c in enumerate(classes):
-        print('%15s: %-.4f' % (c, AP_accum[i] / AP_accum_count[i]))
+        print('%15s: %-.4f' % (c, AP_accum[i] / (AP_accum_count[i] + 1E-16)))
 
     # Return mAP
     return mean_mAP, mean_R, mean_P

utils/datasets.py

@@ -91,13 +91,13 @@ class LoadWebcam:  # for inference
 
 class LoadImagesAndLabels:  # for training
     def __init__(self, path, batch_size=1, img_size=608, multi_scale=False, augment=False):
-        self.path = path
         with open(path, 'r') as file:
             self.img_files = file.readlines()
+            self.img_files = [x.replace('\n', '') for x in self.img_files]
+            self.img_files = list(filter(lambda x: len(x) > 0, self.img_files))
 
-        self.img_files = [path.replace('\n', '') for path in self.img_files]
-        self.label_files = [path.replace('images', 'labels').replace('.png', '.txt').replace('.jpg', '.txt')
-                            for path in self.img_files]
+        self.label_files = [x.replace('images', 'labels').replace('.png', '.txt').replace('.jpg', '.txt')
+                            for x in self.img_files]
 
         self.nF = len(self.img_files)  # number of image files
         self.nB = math.ceil(self.nF / batch_size)  # number of batches

utils/utils.py

@@ -438,13 +438,14 @@ def plot_results():
     # Plot YOLO training results file 'results.txt'
     import glob
     import matplotlib.pyplot as plt
+    import numpy as np
     # import os; os.system('rm -rf results.txt && wget https://storage.googleapis.com/ultralytics/results_v1_0.txt')
 
     plt.figure(figsize=(16, 8))
-    s = ['X', 'Y', 'Width', 'Height', 'Objectness', 'Classification', 'Total Loss', 'Precision', 'Recall', 'mAP']
-    files = sorted(glob.glob('results*.txt'))
+    s = ['X', 'Y', 'Width', 'Height', 'Confidence', 'Classification', 'Total Loss', 'mAP', 'Recall', 'Precision']
+    files = sorted(glob.glob('results.txt'))
     for f in files:
-        results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 7, 8, 17, 18, 16]).T  # column 16 is mAP
+        results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 7, 8, 11, 12, 13]).T  # column 13 is mAP
         n = results.shape[1]
         for i in range(10):
             plt.subplot(2, 5, i + 1)