hyperparameter evolution update (#566)

train.py

@@ -213,6 +213,7 @@ def train(hyp, opt, device, tb_writer=None):
     # Start training
     t0 = time.time()
     nw = max(3 * nb, 1e3)  # number of warmup iterations, max(3 epochs, 1k iterations)
+    # nw = min(nw, (epochs - start_epoch) / 2 * nb)  # limit warmup to < 1/2 of training
     maps = np.zeros(nc)  # mAP per class
     results = (0, 0, 0, 0, 0, 0, 0)  # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
     scheduler.last_epoch = start_epoch - 1  # do not move
@@ -477,8 +478,8 @@ if __name__ == '__main__':
                 'anchor_t': (1, 2.0, 8.0),  # anchor-multiple threshold
                 'fl_gamma': (0, 0.0, 2.0),  # focal loss gamma (efficientDet default gamma=1.5)
                 'hsv_h': (1, 0.0, 0.1),  # image HSV-Hue augmentation (fraction)
-                'hsv_s': (1, 0.0, 0.8),  # image HSV-Saturation augmentation (fraction)
-                'hsv_v': (1, 0.0, 0.8),  # image HSV-Value augmentation (fraction)
+                'hsv_s': (1, 0.0, 0.9),  # image HSV-Saturation augmentation (fraction)
+                'hsv_v': (1, 0.0, 0.9),  # image HSV-Value augmentation (fraction)
                 'degrees': (1, 0.0, 45.0),  # image rotation (+/- deg)
                 'translate': (1, 0.0, 0.9),  # image translation (+/- fraction)
                 'scale': (1, 0.0, 0.9),  # image scale (+/- gain)
@@ -495,7 +496,7 @@ if __name__ == '__main__':
         if opt.bucket:
             os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket)  # download evolve.txt if exists
 
-        for _ in range(10):  # generations to evolve
+        for _ in range(100):  # generations to evolve
             if os.path.exists('evolve.txt'):  # if evolve.txt exists: select best hyps and mutate
                 # Select parent(s)
                 parent = 'single'  # parent selection method: 'single' or 'weighted'