good-ish training results finally

cfg/yolov3-visdrone.cfg

@@ -17,7 +17,7 @@ hue=.1
 
 learning_rate=0.001
 burn_in=100
-max_batches = 502000
+max_batches = 505000
 policy=steps
 steps=40000,45000
 scales=.1,.1

darknet_scripts/plot_yolo_log.py

-import argparse 
+import argparse
 import sys
 import matplotlib.pyplot as plt
 
@@ -15,34 +15,50 @@ def main(argv):
     args = parser.parse_args()
 
     f = open(args.log_file)
-    
+
     lines  = [line.rstrip("\n") for line in f.readlines()]
-    
+
     numbers = {'1','2','3','4','5','6','7','8','9'}
 
     iters = []
     loss = []
-    
+    mAP = []
+
     fig,ax = plt.subplots()
 
-    prev_line = ""
     for line in lines:
         args = line.lstrip().split(' ')
-        if args[0][-1:]==':' and args[0][0] in numbers :
-            iters.append(int(args[0][:-1]))            
-            loss.append(float(args[2]))
-             
-    ax.plot(iters,loss)
-    plt.xlabel('iters')
-    plt.ylabel('loss')
+        if args[0][-1:]==':' and args[0][0] in numbers:
+            iters.append(int(args[0][:-1]))
+            loss.append(min(float(args[2]),250.0))
+        if args[0]=='Last':
+            mAP.append(float(args[4]))
+
+    # make map and iters equal with zero padding
+    if (len(iters) > len(mAP)):
+        mAP = [0.0]*(len(iters) - len(mAP)) + mAP
+    elif (len(iters) < len(mAP)):
+        mAP = [0.0]*(len(mAP) - len(iters)) + mAP
+
+    axM = ax.twinx()  # instantiate a second axes that shares the same x-axis
+
+    ax.plot(iters,loss, 'b')
+    ax.set_xlabel('iters')
+    ax.set_ylabel('loss', color='b')
+
+    axM.plot(iters,mAP, 'r')
+    axM.set_xlabel('iters')
+    axM.set_ylabel('mAP', color='r')
+
     plt.grid()
 
     ticks = range(0,250,10)
 
+    fig.tight_layout()  # otherwise the right y-label is slightly clipped
     plt.savefig('loss_plot.jpg')
-    
+
     #ax.set_yticks(ticks)
     #plt.show()
-    
+
 if __name__ == "__main__":
     main(sys.argv)