Balancing between obj and noobj part of confidence loss. Computation …

…of mAP by class-wise average of APs.

config/yolov3-tiny.cfg

@@ -0,0 +1,206 @@
+[net]
+# Testing
+batch=1
+subdivisions=1
+# Training
+# batch=64
+# subdivisions=2
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+# 0
+[convolutional]
+batch_normalize=1
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# 1
+[maxpool]
+size=2
+stride=2
+
+# 2
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# 3
+[maxpool]
+size=2
+stride=2
+
+# 4
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# 5
+[maxpool]
+size=2
+stride=2
+
+# 6
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# 7
+[maxpool]
+size=2
+stride=2
+
+# 8
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# 9
+[maxpool]
+size=2
+stride=2
+
+# 10
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# 11
+[maxpool]
+size=2
+stride=1
+
+# 12
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+###########
+
+# 13
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+# 14
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# 15
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+
+# 16
+[yolo]
+mask = 3,4,5
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=80
+num=6
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+# 17
+[route]
+layers = -4
+
+# 18
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+# 19
+[upsample]
+stride=2
+
+# 20
+[route]
+layers = -1, 8
+
+# 21
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# 22
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+# 23
+[yolo]
+mask = 1,2,3
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=80
+num=6
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1

models.py

@@ -47,6 +47,19 @@ def create_modules(module_defs):
             if module_def["activation"] == "leaky":
                 modules.add_module("leaky_%d" % i, nn.LeakyReLU(0.1))
 
+        elif module_def["type"] == "maxpool":
+            kernel_size = int(module_def["size"])
+            stride = int(module_def["stride"])
+            if kernel_size == 2 and stride == 1:
+                padding = nn.ZeroPad2d((0, 1, 0, 1))
+                modules.add_module("_debug_padding_%d" % i, padding)
+            maxpool = nn.MaxPool2d(
+                kernel_size=int(module_def["size"]),
+                stride=int(module_def["stride"]),
+                padding=int((kernel_size - 1) // 2),
+            )
+            modules.add_module("maxpool_%d" % i, maxpool)
+
         elif module_def["type"] == "upsample":
             upsample = nn.Upsample(scale_factor=int(module_def["stride"]), mode="nearest")
             modules.add_module("upsample_%d" % i, upsample)
@@ -230,7 +243,7 @@ def forward(self, x, targets=None):
         self.losses = defaultdict(float)
         layer_outputs = []
         for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
-            if module_def["type"] in ["convolutional", "upsample"]:
+            if module_def["type"] in ["convolutional", "upsample", "maxpool"]:
                 x = module(x)
             elif module_def["type"] == "route":
                 layer_i = [int(x) for x in module_def["layers"].split(",")]

weights/download_weights_tiny.sh

@@ -0,0 +1,3 @@
+#! /usr/bin/env bash
+
+wget https://pjreddie.com/media/files/yolov3-tiny.weights