diff --git a/train.py b/train.py
index c08224ed4..26df91057 100644
--- a/train.py
+++ b/train.py
@@ -42,7 +42,7 @@ hyp = {'lr0': 0.01,  # initial learning rate (SGD=1E-2, Adam=1E-3)
 
 def train(hyp, opt, device, tb_writer=None):
     print(f'Hyperparameters {hyp}')
-    log_dir = tb_writer.log_dir if tb_writer else 'runs/evolution'  # run directory
+    log_dir = tb_writer.log_dir if tb_writer else 'runs/evolve'  # run directory
     wdir = str(Path(log_dir) / 'weights') + os.sep  # weights directory
     os.makedirs(wdir, exist_ok=True)
     last = wdir + 'last.pt'
@@ -491,6 +491,7 @@ if __name__ == '__main__':
         assert opt.local_rank == -1, 'DDP mode not implemented for --evolve'
         opt.notest, opt.nosave = True, True  # only test/save final epoch
         # ei = [isinstance(x, (int, float)) for x in hyp.values()]  # evolvable indices
+        yaml_file = Path('runs/evolve/hyp_evolved.yaml')  # save best result here
         if opt.bucket:
             os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket)  # download evolve.txt if exists
 
@@ -518,17 +519,21 @@ if __name__ == '__main__':
                 while all(v == 1):  # mutate until a change occurs (prevent duplicates)
                     v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
                 for i, k in enumerate(hyp.keys()):  # plt.hist(v.ravel(), 300)
-                    hyp[k] = x[i + 7] * v[i]  # mutate
+                    hyp[k] = float(x[i + 7] * v[i])  # mutate
 
-            # Clip to limits
+            # Constrain to limits
             for k, v in meta.items():
-                hyp[k] = np.clip(hyp[k], v[1], v[2])
+                hyp[k] = max(hyp[k], v[1])  # lower limit
+                hyp[k] = min(hyp[k], v[2])  # upper limit
+                hyp[k] = round(hyp[k], 5)  # significant digits
 
             # Train mutation
             results = train(hyp.copy(), opt, device)
 
             # Write mutation results
-            print_mutation(hyp, results, opt.bucket)
+            print_mutation(hyp.copy(), results, yaml_file, opt.bucket)
 
-            # Plot results
-            # plot_evolution_results(hyp)
+        # Plot results
+        plot_evolution_results(yaml_file)
+        print('Hyperparameter evolution complete. Best results saved as: %s\nCommand to train a new model with these '
+              'hyperparameters: $ python train.py --hyp %s' % (f, f))
diff --git a/utils/utils.py b/utils/utils.py
index 184e8dc25..146383471 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -818,11 +818,11 @@ def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=10
     return print_results(k)
 
 
-def print_mutation(hyp, results, bucket=''):
+def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''):
     # Print mutation results to evolve.txt (for use with train.py --evolve)
     a = '%10s' * len(hyp) % tuple(hyp.keys())  # hyperparam keys
     b = '%10.3g' * len(hyp) % tuple(hyp.values())  # hyperparam values
-    c = '%10.4g' * len(results) % results  # results (P, R, mAP, F1, test_loss)
+    c = '%10.4g' * len(results) % results  # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3)
     print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c))
 
     if bucket:
@@ -831,11 +831,19 @@ def print_mutation(hyp, results, bucket=''):
     with open('evolve.txt', 'a') as f:  # append result
         f.write(c + b + '\n')
     x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0)  # load unique rows
-    np.savetxt('evolve.txt', x[np.argsort(-fitness(x))], '%10.3g')  # save sort by fitness
+    x = x[np.argsort(-fitness(x))]  # sort
+    np.savetxt('evolve.txt', x, '%10.3g')  # save sort by fitness
 
     if bucket:
         os.system('gsutil cp evolve.txt gs://%s' % bucket)  # upload evolve.txt
 
+    # Save yaml
+    for i, k in enumerate(hyp.keys()):
+        hyp[k] = float(x[0, i + 7])
+    with open(yaml_file, 'w') as f:
+        f.write('# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: ' % len(x) + c + '\n\n')
+        yaml.dump(hyp, f, sort_keys=False)
+
 
 def apply_classifier(x, model, img, im0):
     # applies a second stage classifier to yolo outputs
@@ -1146,23 +1154,26 @@ def plot_labels(labels, save_dir=''):
     plt.close()
 
 
-def plot_evolution_results(hyp):  # from utils.utils import *; plot_evolution_results(hyp)
+def plot_evolution_results(yaml_file='hyp_evolved.yaml'):  # from utils.utils import *; plot_evolution_results()
     # Plot hyperparameter evolution results in evolve.txt
+    with open(yaml_file) as f:
+        hyp = yaml.load(f, Loader=yaml.FullLoader)
     x = np.loadtxt('evolve.txt', ndmin=2)
     f = fitness(x)
     # weights = (f - f.min()) ** 2  # for weighted results
-    plt.figure(figsize=(12, 10), tight_layout=True)
+    plt.figure(figsize=(14, 10), tight_layout=True)
     matplotlib.rc('font', **{'size': 8})
     for i, (k, v) in enumerate(hyp.items()):
         y = x[:, i + 7]
         # mu = (y * weights).sum() / weights.sum()  # best weighted result
         mu = y[f.argmax()]  # best single result
-        plt.subplot(4, 5, i + 1)
+        plt.subplot(4, 6, i + 1)
         plt.plot(mu, f.max(), 'o', markersize=10)
         plt.plot(y, f, '.')
         plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9})  # limit to 40 characters
         print('%15s: %.3g' % (k, mu))
     plt.savefig('evolve.png', dpi=200)
+    print('\nPlot saved as evolve.png')
 
 
 def plot_results_overlay(start=0, stop=0):  # from utils.utils import *; plot_results_overlay()