:eyeofthetiger::noseofthetiger::eyeofthetiger2:

Makefile

@@ -10,7 +10,7 @@ ARCH= -gencode arch=compute_20,code=[sm_20,sm_21] \
       -gencode arch=compute_52,code=[sm_52,compute_52]
 
 # This is what I use, uncomment if you know your arch and want to specify
-# ARCH=  -gencode arch=compute_52,code=compute_52
+ARCH=  -gencode arch=compute_52,code=compute_52
 
 VPATH=./src/
 EXEC=darknet
@@ -48,10 +48,10 @@ CFLAGS+= -DCUDNN
 LDFLAGS+= -lcudnn
 endif
 
-OBJ=gemm.o utils.o cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o super.o voxel.o tree.o
+OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o regressor.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o lsd.o super.o voxel.o tree.o
 ifeq ($(GPU), 1) 
 LDFLAGS+= -lstdc++ 
-OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o
+OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o
 endif
 
 OBJS = $(addprefix $(OBJDIR), $(OBJ))

cfg/coco.data

@@ -1,7 +1,7 @@
 classes= 80
 train  = /home/pjreddie/data/coco/trainvalno5k.txt
-#valid  = coco_testdev
-valid = data/coco_val_5k.list
+valid  = coco_testdev
+#valid = data/coco_val_5k.list
 names = data/coco.names
 backup = /home/pjreddie/backup/
 eval=coco

cfg/go.cfg

@@ -0,0 +1,135 @@
+[net]
+batch=512
+subdivisions=1
+height=19
+width=19
+channels=1
+momentum=0.9
+decay=0.0005
+
+burn_in=1000
+learning_rate=0.1
+policy=poly
+power=4
+max_batches=10000000
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=relu
+batch_normalize=1
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=relu
+batch_normalize=1
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=relu
+batch_normalize=1
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=relu
+batch_normalize=1
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=relu
+batch_normalize=1
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=relu
+batch_normalize=1
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=relu
+batch_normalize=1
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=relu
+batch_normalize=1
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=relu
+batch_normalize=1
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=relu
+batch_normalize=1
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=relu
+batch_normalize=1
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=relu
+batch_normalize=1
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=relu
+batch_normalize=1
+
+[convolutional]
+filters=1
+size=1
+stride=1
+pad=1
+activation=linear
+
+[reorg]
+extra=1
+stride=1
+
+[softmax]
+
+[cost]
+type=sse
+

cfg/go.test.cfg

@@ -7,123 +7,126 @@ channels=1
 momentum=0.9
 decay=0.0005
 
-learning_rate=0.1
+learning_rate=0.01
 policy=poly
 power=4
-max_batches=400000
+max_batches=100000
 
 [convolutional]
-filters=192
+filters=256
 size=3
 stride=1
 pad=1
 activation=relu
 batch_normalize=1
 
 [convolutional]
-filters=192
+filters=256
 size=3
 stride=1
 pad=1
 activation=relu
 batch_normalize=1
 
 [convolutional]
-filters=192
+filters=256
 size=3
 stride=1
 pad=1
 activation=relu
 batch_normalize=1
 
 [convolutional]
-filters=192
+filters=256
 size=3
 stride=1
 pad=1
 activation=relu
 batch_normalize=1
 
 [convolutional]
-filters=192
+filters=256
 size=3
 stride=1
 pad=1
 activation=relu
 batch_normalize=1
 
 [convolutional]
-filters=192
+filters=256
 size=3
 stride=1
 pad=1
 activation=relu
 batch_normalize=1
 
 [convolutional]
-filters=192
+filters=256
 size=3
 stride=1
 pad=1
 activation=relu
 batch_normalize=1
 
 [convolutional]
-filters=192
+filters=256
 size=3
 stride=1
 pad=1
 activation=relu
 batch_normalize=1
 
 [convolutional]
-filters=192
+filters=256
 size=3
 stride=1
 pad=1
 activation=relu
 batch_normalize=1
 
 [convolutional]
-filters=192
+filters=256
 size=3
 stride=1
 pad=1
 activation=relu
 batch_normalize=1
 
 [convolutional]
-filters=192
+filters=256
 size=3
 stride=1
 pad=1
 activation=relu
 batch_normalize=1
 
 [convolutional]
-filters=192
+filters=256
 size=3
 stride=1
 pad=1
 activation=relu
 batch_normalize=1
 
 [convolutional]
-filters=192
+filters=256
 size=3
 stride=1
 pad=1
 activation=relu
 batch_normalize=1
 
-
 [convolutional]
 filters=1
 size=1
 stride=1
 pad=1
 activation=linear
 
+[reorg]
+extra=1
+stride=1
+
 [softmax]
 
 [cost]

cfg/tiny-yolo-voc.cfg

@@ -12,7 +12,7 @@ exposure = 1.5
 hue=.1
 
 learning_rate=0.001
-max_batches = 40100
+max_batches = 40200
 policy=steps
 steps=-1,100,20000,30000
 scales=.1,10,.1,.1