diff --git a/train.py b/train.py
index d6364fc0..25f88de6 100644
--- a/train.py
+++ b/train.py
@@ -301,14 +301,15 @@ if __name__ == '__main__':
             # Mutate hyperparameters
             old_hyp = hyp.copy()
             init_seeds(seed=int(time.time()))
-            for k in hyp.keys():
-                x = (np.random.randn(1) * 0.2 + 1) ** 1.1  # plt.hist(x.ravel(), 100)
+            s = [.3, .3, .3, .3, .3, .3, .3, .3, .05, .3]
+            for i, k in enumerate(hyp.keys()):
+                x = (np.random.randn(1) * s[i] + 1) ** 1.1  # plt.hist(x.ravel(), 100)
                 hyp[k] = hyp[k] * float(x)  # vary by about 30% 1sigma
 
             # Apply limits
             hyp['iou_t'] = np.clip(hyp['iou_t'], 0, 0.90)
-            hyp['momentum'] = 0.9  # np.clip(hyp['momentum'], 0, 0.98)
-            hyp['weight_decay'] = 0.0005  # np.clip(hyp['weight_decay'], 0, 0.01)
+            hyp['momentum'] = np.clip(hyp['momentum'], 0.7, 0.98)
+            hyp['weight_decay'] = np.clip(hyp['weight_decay'], 0, 0.01)
 
             # Normalize loss components (sum to 1)
             lcf = ['xy', 'wh', 'cls', 'conf']
@@ -345,3 +346,13 @@ if __name__ == '__main__':
                 best_fitness = mutation_fitness
             else:
                 hyp = old_hyp.copy()  # reset hyp to
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+a = np.loadtxt('evolve.txt')
+x = a[:,3]
+fig = plt.figure(figsize=(14, 7))
+for i in range(1,10):
+    plt.subplot(2,5,i)
+    plt.plot(x,a[:,i+5],'.')