updates

utils/datasets.py

@@ -180,7 +180,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
 
         # Preload labels (required for weighted CE training)
         self.labels = [np.array([])] * n
-        iter = tqdm(self.label_files, desc='Reading labels') if n > 5000 else self.label_files
+        iter = tqdm(self.label_files, desc='Reading labels') if n > 1000 else self.label_files
         for i, file in enumerate(iter):
             try:
                 with open(file, 'r') as f:

utils/utils.py

@@ -52,12 +52,11 @@ def model_info(model):
 def labels_to_class_weights(labels, nc=80):
     # Get class weights (inverse frequency) from training labels
     labels = np.concatenate(labels, 0)  # labels.shape = (866643, 5) for COCO
-    classes = labels[:, 0].astype(np.int)
-    n = np.bincount(classes, minlength=nc)
-    weights = np.zeros(nc)
-    i = n.nonzero()
-    weights[i] = 1 / n[i]  # number of targets per class
-    weights /= weights.sum()
+    classes = labels[:, 0].astype(np.int)  # labels = [class xywh]
+    weights = np.bincount(classes, minlength=nc)  # occurences per class
+    weights[weights == 0] = 1  # replace empty bins with 1
+    weights = 1 / weights  # number of targets per class
+    weights /= weights.sum()  # normalize
     return torch.Tensor(weights)
 
 
@@ -527,7 +526,7 @@ def plot_images(imgs, targets, fname='images.jpg'):
     plt.close()
 
 
-def plot_results(start=0, stop=0):  # from utils.utils import *; plot_results()
+def plot_results(start=1, stop=0):  # from utils.utils import *; plot_results()
     # Plot training results files 'results*.txt'
     # import os; os.system('wget https://storage.googleapis.com/ultralytics/yolov3/results_v3.txt')
 
@@ -542,6 +541,6 @@ def plot_results(start=0, stop=0):  # from utils.utils import *; plot_results()
         for i in range(10):
             ax[i].plot(x, results[i, x], marker='.', label=f.replace('.txt', ''))
             ax[i].set_title(s[i])
-    ax[0].legend()
     fig.tight_layout()
+    ax[4].legend()
     fig.savefig('results.png', dpi=300)