labels.jpg class names (#2454)

* labels.png class names

* fontsize=10

train.py

             # cf = torch.bincount(c.long(), minlength=nc) + 1.  # frequency
             # model._initialize_biases(cf.to(device))
             if plots:
-                plot_labels(labels, save_dir, loggers)
+                plot_labels(labels, names, save_dir, loggers)
                 if tb_writer:
                     tb_writer.add_histogram('classes', c, 0)
 

utils/plots.py

     plt.savefig(str(Path(path).name) + '.png', dpi=300)
 
 
-def plot_labels(labels, save_dir=Path(''), loggers=None):
+def plot_labels(labels, names=(), save_dir=Path(''), loggers=None):
     # plot dataset labels
     print('Plotting labels... ')
     c, b = labels[:, 0], labels[:, 1:].transpose()  # classes, boxes
     matplotlib.use('svg')  # faster
     ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
     ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
-    ax[0].set_xlabel('classes')
+    ax[0].set_ylabel('instances')
+    if 0 < len(names) < 30:
+        ax[0].set_xticks(range(len(names)))
+        ax[0].set_xticklabels(names, rotation=90, fontsize=10)
+    else:
+        ax[0].set_xlabel('classes')
     sns.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)
     sns.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)