diff --git a/app/src/main/assets/cfg/yolo-voc.2.0.cfg b/app/src/main/assets/cfg/yolo-voc.2.0.cfg
diff --git a/app/src/main/assets/cfg/yolov1/tiny-yolo.cfg → app/src/main/assets/cfg/yolov1-tiny.cfg b/app/src/main/assets/cfg/yolov1/tiny-yolo.cfg → app/src/main/assets/cfg/yolov1-tiny.cfg
@@ -1,6 +1,10 @@
 [net]
-batch=64
-subdivisions=2
+# Testing
+batch=1
+subdivisions=1
+# Training
+# batch=64
+# subdivisions=8
 height=448
 width=448
 channels=3

diff --git a/app/src/main/assets/cfg/yolov1/yolo.cfg → app/src/main/assets/cfg/yolov1.cfg b/app/src/main/assets/cfg/yolov1/yolo.cfg → app/src/main/assets/cfg/yolov1.cfg
@@ -1,6 +1,10 @@
 [net]
+# Testing
 batch=1
 subdivisions=1
+# Training
+# batch=64
+# subdivisions=8
 height=448
 width=448
 channels=3

diff --git a/app/src/main/assets/cfg/yolov1/tiny-coco.cfg b/app/src/main/assets/cfg/yolov1/tiny-coco.cfg
diff --git a/app/src/main/assets/cfg/yolov1/xyolo.test.cfg b/app/src/main/assets/cfg/yolov1/xyolo.test.cfg
diff --git a/app/src/main/assets/cfg/yolov1/yolo-coco.cfg b/app/src/main/assets/cfg/yolov1/yolo-coco.cfg
diff --git a/app/src/main/assets/cfg/yolov1/yolo-small.cfg b/app/src/main/assets/cfg/yolov1/yolo-small.cfg
diff --git a/app/src/main/assets/cfg/yolov1/yolo.train.cfg b/app/src/main/assets/cfg/yolov1/yolo.train.cfg
diff --git a/app/src/main/assets/cfg/yolov1/yolo2.cfg b/app/src/main/assets/cfg/yolov1/yolo2.cfg
diff --git a/app/src/main/assets/cfg/tiny-yolo-voc.cfg → app/src/main/assets/cfg/yolov2-tiny-voc.cfg b/app/src/main/assets/cfg/tiny-yolo-voc.cfg → app/src/main/assets/cfg/yolov2-tiny-voc.cfg
@@ -1,6 +1,10 @@
 [net]
-batch=64
-subdivisions=8
+# Testing
+batch=1
+subdivisions=1
+# Training
+# batch=64
+# subdivisions=2
 width=416
 height=416
 channels=3

diff --git a/app/src/main/assets/cfg/tiny-yolo.cfg → app/src/main/assets/cfg/yolov2-tiny.cfg b/app/src/main/assets/cfg/tiny-yolo.cfg → app/src/main/assets/cfg/yolov2-tiny.cfg
@@ -1,10 +1,10 @@
 [net]
-# Training
-# batch=64
-# subdivisions=2
 # Testing
 batch=1
 subdivisions=1
+# Training
+# batch=64
+# subdivisions=2
 width=416
 height=416
 channels=3

diff --git a/app/src/main/assets/cfg/yolo-voc.cfg → app/src/main/assets/cfg/yolov2-voc.cfg b/app/src/main/assets/cfg/yolo-voc.cfg → app/src/main/assets/cfg/yolov2-voc.cfg
diff --git a/app/src/main/assets/cfg/yolo.cfg → app/src/main/assets/cfg/yolov2.cfg b/app/src/main/assets/cfg/yolo.cfg → app/src/main/assets/cfg/yolov2.cfg
@@ -5,8 +5,8 @@ subdivisions=1
 # Training
 # batch=64
 # subdivisions=8
-width=608
-height=608
+width=416
+height=416
 channels=3
 momentum=0.9
 decay=0.0005

diff --git a/app/src/main/assets/cfg/yolov3-spp.cfg b/app/src/main/assets/cfg/yolov3-spp.cfg
diff --git a/app/src/main/assets/cfg/yolo.2.0.cfg → app/src/main/assets/cfg/yolov3-tiny.cfg b/app/src/main/assets/cfg/yolo.2.0.cfg → app/src/main/assets/cfg/yolov3-tiny.cfg
@@ -1,6 +1,10 @@
 [net]
+# Testing
 batch=1
 subdivisions=1
+# Training
+# batch=64
+# subdivisions=2
 width=416
 height=416
 channels=3
@@ -12,14 +16,15 @@ exposure = 1.5
 hue=.1
 
 learning_rate=0.001
-max_batches = 120000
+burn_in=1000
+max_batches = 500200
 policy=steps
-steps=-1,100,80000,100000
-scales=.1,10,.1,.1
+steps=400000,450000
+scales=.1,.1
 
 [convolutional]
 batch_normalize=1
-filters=32
+filters=16
 size=3
 stride=1
 pad=1
@@ -31,7 +36,7 @@ stride=2
 
 [convolutional]
 batch_normalize=1
-filters=64
+filters=32
 size=3
 stride=1
 pad=1
@@ -41,25 +46,9 @@ activation=leaky
 size=2
 stride=2
 
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
 [convolutional]
 batch_normalize=1
 filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
 size=3
 stride=1
 pad=1
@@ -69,25 +58,9 @@ activation=leaky
 size=2
 stride=2
 
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
 [convolutional]
 batch_normalize=1
 filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
 size=3
 stride=1
 pad=1
@@ -97,37 +70,17 @@ activation=leaky
 size=2
 stride=2
 
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
 [convolutional]
 batch_normalize=1
 filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
 size=3
 stride=1
 pad=1
 activation=leaky
 
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
+[maxpool]
+size=2
+stride=2
 
 [convolutional]
 batch_normalize=1
@@ -139,7 +92,7 @@ activation=leaky
 
 [maxpool]
 size=2
-stride=2
+stride=1
 
 [convolutional]
 batch_normalize=1
@@ -149,96 +102,81 @@ stride=1
 pad=1
 activation=leaky
 
+###########
+
 [convolutional]
 batch_normalize=1
-filters=512
+filters=256
 size=1
 stride=1
 pad=1
 activation=leaky
 
 [convolutional]
 batch_normalize=1
-filters=1024
+filters=512
 size=3
 stride=1
 pad=1
 activation=leaky
 
 [convolutional]
-batch_normalize=1
-filters=512
 size=1
 stride=1
 pad=1
-activation=leaky
+filters=255
+activation=linear
 
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
 
 
-#######
+[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
 
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
+[route]
+layers = -4
 
 [convolutional]
 batch_normalize=1
-size=3
+filters=128
+size=1
 stride=1
 pad=1
-filters=1024
 activation=leaky
 
-[route]
-layers=-9
-
-[reorg]
+[upsample]
 stride=2
 
 [route]
-layers=-1,-3
+layers = -1, 8
 
 [convolutional]
 batch_normalize=1
+filters=256
 size=3
 stride=1
 pad=1
-filters=1024
 activation=leaky
 
 [convolutional]
 size=1
 stride=1
 pad=1
-filters=425
+filters=255
 activation=linear
 
-[region]
-anchors = 0.738768,0.874946,  2.42204,2.65704,  4.30971,7.04493,  10.246,4.59428,  12.6868,11.8741
-bias_match=1
+[yolo]
+mask = 1,2,3
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
 classes=80
-coords=4
-num=5
-softmax=1
-jitter=.2
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=0
+num=6
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
diff --git a/app/src/main/assets/cfg/yolov3-voc.cfg b/app/src/main/assets/cfg/yolov3-voc.cfg
diff --git a/app/src/main/assets/cfg/yolov3.cfg b/app/src/main/assets/cfg/yolov3.cfg
diff --git a/app/src/main/assets/data/kite.jpg b/app/src/main/assets/data/kite.jpg
diff --git a/app/src/main/cpp/darknet/Makefile b/app/src/main/cpp/darknet/Makefile
@@ -1,7 +1,7 @@
 GPU=0
 CUDNN=0
-OPENCV=1
-OPENMP=1
+OPENCV=0
+OPENMP=0
 DEBUG=0
 
 ARCH= -gencode arch=compute_30,code=sm_30 \
@@ -26,7 +26,7 @@ ARFLAGS=rcs
 OPTS=-Ofast
 LDFLAGS= -lm -pthread 
 COMMON= -Iinclude/ -Isrc/
-CFLAGS=-Wall -Wno-unknown-pragmas -Wfatal-errors -fPIC
+CFLAGS=-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -fPIC
 
 ifeq ($(OPENMP), 1) 
 CFLAGS+= -fopenmp
@@ -57,8 +57,8 @@ CFLAGS+= -DCUDNN
 LDFLAGS+= -lcudnn
 endif
 
-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 detection_layer.o route_layer.o box.o normalization_layer.o avgpool_layer.o layer.o local_layer.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o crnn_layer.o demo.o batchnorm_layer.o region_layer.o reorg_layer.o tree.o  lstm_layer.o
-EXECOBJA=captcha.o lsd.o super.o art.o tag.o cifar.o go.o rnn.o segmenter.o regressor.o classifier.o coco.o yolo.o detector.o nightmare.o attention.o darknet.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 detection_layer.o route_layer.o upsample_layer.o box.o normalization_layer.o avgpool_layer.o layer.o local_layer.o shortcut_layer.o logistic_layer.o activation_layer.o rnn_layer.o gru_layer.o crnn_layer.o demo.o batchnorm_layer.o region_layer.o reorg_layer.o tree.o  lstm_layer.o l2norm_layer.o yolo_layer.o iseg_layer.o
+EXECOBJA=captcha.o lsd.o super.o art.o tag.o cifar.o go.o rnn.o segmenter.o regressor.o classifier.o coco.o yolo.o detector.o nightmare.o instance-segmenter.o darknet.o
 ifeq ($(GPU), 1) 
 LDFLAGS+= -lstdc++ 
 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 avgpool_layer_kernels.o
@@ -68,8 +68,8 @@ EXECOBJ = $(addprefix $(OBJDIR), $(EXECOBJA))
 OBJS = $(addprefix $(OBJDIR), $(OBJ))
 DEPS = $(wildcard src/*.h) Makefile include/darknet.h
 
-#all: obj backup results $(SLIB) $(ALIB) $(EXEC)
-all: obj  results $(SLIB) $(ALIB) $(EXEC)
+all: obj backup results $(SLIB) $(ALIB) $(EXEC)
+#all: obj  results $(SLIB) $(ALIB) $(EXEC)
 
 
 $(EXEC): $(EXECOBJ) $(ALIB)
@@ -97,5 +97,5 @@ results:
 .PHONY: clean
 
 clean:
-	rm -rf $(OBJS) $(SLIB) $(ALIB) $(EXEC) $(EXECOBJ)
+	rm -rf $(OBJS) $(SLIB) $(ALIB) $(EXEC) $(EXECOBJ) $(OBJDIR)/*
 
diff --git a/app/src/main/cpp/darknet/Makefile.bak b/app/src/main/cpp/darknet/Makefile.bak
diff --git a/app/src/main/cpp/darknet/README.md b/app/src/main/cpp/darknet/README.md
@@ -1,6 +1,6 @@
 ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png)
 
-#Darknet#
+# Darknet #
 Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.
 
 For more information see the [Darknet project website](http://pjreddie.com/darknet).

diff --git a/app/src/main/cpp/darknet/examples/art.c b/app/src/main/cpp/darknet/examples/art.c
@@ -24,7 +24,6 @@ void demo_art(char *cfgfile, char *weightfile, int cam_index)
     while(1){
         image in = get_image_from_stream(cap);
         image in_s = resize_image(in, net->w, net->h);
-        show_image(in, window);
 
         float *p = network_predict(net, in_s.data);
 
@@ -45,10 +44,9 @@ void demo_art(char *cfgfile, char *weightfile, int cam_index)
         }
         printf("]\n");
 
+        show_image(in, window, 1);
         free_image(in_s);
         free_image(in);
-
-        cvWaitKey(1);
     }
 #endif
 }

diff --git a/app/src/main/cpp/darknet/examples/classifier.c b/app/src/main/cpp/darknet/examples/classifier.c
@@ -44,11 +44,17 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
     list *options = read_data_cfg(datacfg);
 
     char *backup_directory = option_find_str(options, "backup", "/backup/");
+    int tag = option_find_int_quiet(options, "tag", 0);
     char *label_list = option_find_str(options, "labels", "data/labels.list");
     char *train_list = option_find_str(options, "train", "data/train.list");
+    char *tree = option_find_str(options, "tree", 0);
+    if (tree) net->hierarchy = read_tree(tree);
     int classes = option_find_int(options, "classes", 2);
 
-    char **labels = get_labels(label_list);
+    char **labels = 0;
+    if(!tag){
+        labels = get_labels(label_list);
+    }
     list *plist = get_paths(train_list);
     char **paths = (char **)list_to_array(plist);
     printf("%d\n", plist->size);
@@ -76,7 +82,11 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
     args.n = imgs;
     args.m = N;
     args.labels = labels;
-    args.type = CLASSIFICATION_DATA;
+    if (tag){
+        args.type = TAG_DATA;
+    } else {
+        args.type = CLASSIFICATION_DATA;
+    }
 
     data train;
     data buffer;
@@ -151,7 +161,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
     pthread_join(load_thread, 0);
 
     free_network(net);
-    free_ptrs((void**)labels, classes);
+    if(labels) free_ptrs((void**)labels, classes);
     free_ptrs((void**)paths, plist->size);
     free_list(plist);
     free(base);
@@ -385,15 +395,13 @@ void validate_classifier_single(char *datacfg, char *filename, char *weightfile)
             }
         }
         image im = load_image_color(paths[i], 0, 0);
-        image resized = resize_min(im, net->w);
-        image crop = crop_image(resized, (resized.w - net->w)/2, (resized.h - net->h)/2, net->w, net->h);
+        image crop = center_crop_image(im, net->w, net->h);
         //show_image(im, "orig");
         //show_image(crop, "cropped");
         //cvWaitKey(0);
         float *pred = network_predict(net, crop.data);
         if(net->hierarchy) hierarchy_predictions(pred, net->outputs, net->hierarchy, 1, 1);
 
-        if(resized.data != im.data) free_image(resized);
         free_image(im);
         free_image(crop);
         top_k(pred, classes, topk, indexes);
@@ -403,6 +411,7 @@ void validate_classifier_single(char *datacfg, char *filename, char *weightfile)
             if(indexes[j] == class) avg_topk += 1;
         }
 
+        printf("%s, %d, %f, %f, \n", paths[i], class, pred[0], pred[1]);
         printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
     }
 }
@@ -577,6 +586,8 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi
         }
         image im = load_image_color(input, 0, 0);
         image r = letterbox_image(im, net->w, net->h);
+        //image r = resize_min(im, 320);
+        //printf("%d %d\n", r.w, r.h);
         //resize_network(net, r.w, r.h);
         //printf("%d %d\n", r.w, r.h);
 
@@ -634,6 +645,45 @@ void label_classifier(char *datacfg, char *filename, char *weightfile)
     }
 }
 
+void csv_classifier(char *datacfg, char *cfgfile, char *weightfile)
+{
+    int i,j;
+    network *net = load_network(cfgfile, weightfile, 0);
+    srand(time(0));
+
+    list *options = read_data_cfg(datacfg);
+
+    char *test_list = option_find_str(options, "test", "data/test.list");
+    int top = option_find_int(options, "top", 1);
+
+    list *plist = get_paths(test_list);
+
+    char **paths = (char **)list_to_array(plist);
+    int m = plist->size;
+    free_list(plist);
+    int *indexes = calloc(top, sizeof(int));
+
+    for(i = 0; i < m; ++i){
+        double time = what_time_is_it_now();
+        char *path = paths[i];
+        image im = load_image_color(path, 0, 0);
+        image r = letterbox_image(im, net->w, net->h);
+        float *predictions = network_predict(net, r.data);
+        if(net->hierarchy) hierarchy_predictions(predictions, net->outputs, net->hierarchy, 1, 1);
+        top_k(predictions, net->outputs, top, indexes);
+
+        printf("%s", path);
+        for(j = 0; j < top; ++j){
+            printf("\t%d", indexes[j]);
+        }
+        printf("\n");
+
+        free_image(im);
+        free_image(r);
+
+        fprintf(stderr, "%lf seconds, %d images, %d total\n", what_time_is_it_now() - time, i+1, m);
+    }
+}
 
 void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_layer)
 {
@@ -704,6 +754,44 @@ void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_
     }
 }
 
+void file_output_classifier(char *datacfg, char *filename, char *weightfile, char *listfile)
+{
+    int i,j;
+    network *net = load_network(filename, weightfile, 0);
+    set_batch_network(net, 1);
+    srand(time(0));
+
+    list *options = read_data_cfg(datacfg);
+
+    //char *label_list = option_find_str(options, "names", "data/labels.list");
+    int classes = option_find_int(options, "classes", 2);
+
+    list *plist = get_paths(listfile);
+
+    char **paths = (char **)list_to_array(plist);
+    int m = plist->size;
+    free_list(plist);
+
+    for(i = 0; i < m; ++i){
+        image im = load_image_color(paths[i], 0, 0);
+        image resized = resize_min(im, net->w);
+        image crop = crop_image(resized, (resized.w - net->w)/2, (resized.h - net->h)/2, net->w, net->h);
+
+        float *pred = network_predict(net, crop.data);
+        if(net->hierarchy) hierarchy_predictions(pred, net->outputs, net->hierarchy, 0, 1);
+
+        if(resized.data != im.data) free_image(resized);
+        free_image(im);
+        free_image(crop);
+
+        printf("%s", paths[i]);
+        for(j = 0; j < classes; ++j){
+            printf("\t%g", pred[j]);
+        }
+        printf("\n");
+    }
+}
+
 
 void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
 {
@@ -820,8 +908,7 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i
         }
 
         if(1){
-            show_image(out, "Threat");
-            cvWaitKey(10);
+            show_image(out, "Threat", 10);
         }
         free_image(in_s);
         free_image(in);
@@ -873,7 +960,6 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde
 
         image in = get_image_from_stream(cap);
         image in_s = resize_image(in, net->w, net->h);
-        show_image(in, "Threat Detection");
 
         float *predictions = network_predict(net, in_s.data);
         top_predictions(net, top, indexes);
@@ -898,11 +984,10 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde
             }
         }
 
+        show_image(in, "Threat Detection", 10);
         free_image(in_s);
         free_image(in);
 
-        cvWaitKey(10);
-
         gettimeofday(&tval_after, NULL);
         timersub(&tval_after, &tval_before, &tval_result);
         float curr = 1000000.f/((long int)tval_result.tv_usec);
@@ -914,6 +999,8 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde
 void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
 {
 #ifdef OPENCV
+    char *base = basecfg(cfgfile);
+    image **alphabet = load_alphabet();
     printf("Classifier Demo\n");
     network *net = load_network(cfgfile, weightfile, 0);
     set_batch_network(net, 1);
@@ -922,22 +1009,33 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind
     srand(2222222);
     CvCapture * cap;
 
+    int w = 1280;
+    int h = 720;
+
     if(filename){
         cap = cvCaptureFromFile(filename);
     }else{
         cap = cvCaptureFromCAM(cam_index);
     }
 
+    if(w){
+        cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_WIDTH, w);
+    }
+    if(h){
+        cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_HEIGHT, h);
+    }
+
     int top = option_find_int(options, "top", 1);
 
-    char *name_list = option_find_str(options, "names", 0);
+    char *label_list = option_find_str(options, "labels", 0);
+    char *name_list = option_find_str(options, "names", label_list);
     char **names = get_labels(name_list);
 
     int *indexes = calloc(top, sizeof(int));
 
     if(!cap) error("Couldn't connect to webcam.\n");
-    cvNamedWindow("Classifier", CV_WINDOW_NORMAL); 
-    cvResizeWindow("Classifier", 512, 512);
+    cvNamedWindow(base, CV_WINDOW_NORMAL); 
+    cvResizeWindow(base, 512, 512);
     float fps = 0;
     int i;
 
@@ -946,8 +1044,8 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind
         gettimeofday(&tval_before, NULL);
 
         image in = get_image_from_stream(cap);
-        image in_s = resize_image(in, net->w, net->h);
-        show_image(in, "Classifier");
+        //image in_s = resize_image(in, net->w, net->h);
+        image in_s = letterbox_image(in, net->w, net->h);
 
         float *predictions = network_predict(net, in_s.data);
         if(net->hierarchy) hierarchy_predictions(predictions, net->outputs, net->hierarchy, 1, 1);
@@ -957,16 +1055,27 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind
         printf("\033[1;1H");
         printf("\nFPS:%.0f\n",fps);
 
+        int lh = in.h*.03;
+        int toph = 3*lh;
+
+        float rgb[3] = {1,1,1};
         for(i = 0; i < top; ++i){
+            printf("%d\n", toph);
             int index = indexes[i];
             printf("%.1f%%: %s\n", predictions[index]*100, names[index]);
+
+            char buff[1024];
+            sprintf(buff, "%3.1f%%: %s\n", predictions[index]*100, names[index]);
+            image label = get_label(alphabet, buff, lh);
+            draw_label(in, toph, lh, label, rgb);
+            toph += 2*lh;
+            free_image(label);
         }
 
+        show_image(in, base, 10);
         free_image(in_s);
         free_image(in);
 
-        cvWaitKey(10);
-
         gettimeofday(&tval_after, NULL);
         timersub(&tval_after, &tval_before, &tval_result);
         float curr = 1000000.f/((long int)tval_result.tv_usec);
@@ -998,12 +1107,14 @@ void run_classifier(int argc, char **argv)
     char *layer_s = (argc > 7) ? argv[7]: 0;
     int layer = layer_s ? atoi(layer_s) : -1;
     if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename, top);
+    else if(0==strcmp(argv[2], "fout")) file_output_classifier(data, cfg, weights, filename);
     else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, atoi(layer_s));
     else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear);
     else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename);
     else if(0==strcmp(argv[2], "gun")) gun_classifier(data, cfg, weights, cam_index, filename);
     else if(0==strcmp(argv[2], "threat")) threat_classifier(data, cfg, weights, cam_index, filename);
     else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights, layer);
+    else if(0==strcmp(argv[2], "csv")) csv_classifier(data, cfg, weights);
     else if(0==strcmp(argv[2], "label")) label_classifier(data, cfg, weights);
     else if(0==strcmp(argv[2], "valid")) validate_classifier_single(data, cfg, weights);
     else if(0==strcmp(argv[2], "validmulti")) validate_classifier_multi(data, cfg, weights);

diff --git a/app/src/main/cpp/darknet/examples/coco.c b/app/src/main/cpp/darknet/examples/coco.c
@@ -94,14 +94,14 @@ void train_coco(char *cfgfile, char *weightfile)
     save_weights(net, buff);
 }
 
-void print_cocos(FILE *fp, int image_id, box *boxes, float **probs, int num_boxes, int classes, int w, int h)
+static void print_cocos(FILE *fp, int image_id, detection *dets, int num_boxes, int classes, int w, int h)
 {
     int i, j;
     for(i = 0; i < num_boxes; ++i){
-        float xmin = boxes[i].x - boxes[i].w/2.;
-        float xmax = boxes[i].x + boxes[i].w/2.;
-        float ymin = boxes[i].y - boxes[i].h/2.;
-        float ymax = boxes[i].y + boxes[i].h/2.;
+        float xmin = dets[i].bbox.x - dets[i].bbox.w/2.;
+        float xmax = dets[i].bbox.x + dets[i].bbox.w/2.;
+        float ymin = dets[i].bbox.y - dets[i].bbox.h/2.;
+        float ymax = dets[i].bbox.y + dets[i].bbox.h/2.;
 
         if (xmin < 0) xmin = 0;
         if (ymin < 0) ymin = 0;
@@ -114,7 +114,7 @@ void print_cocos(FILE *fp, int image_id, box *boxes, float **probs, int num_boxe
         float bh = ymax - ymin;
 
         for(j = 0; j < classes; ++j){
-            if (probs[i][j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, probs[i][j]);
+            if (dets[i].prob[j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, dets[i].prob[j]);
         }
     }
 }
@@ -140,18 +140,12 @@ void validate_coco(char *cfg, char *weights)
 
     layer l = net->layers[net->n-1];
     int classes = l.classes;
-    int side = l.side;
 
-    int j;
     char buff[1024];
     snprintf(buff, 1024, "%s/coco_results.json", base);
     FILE *fp = fopen(buff, "w");
     fprintf(fp, "[\n");
 
-    box *boxes = calloc(side*side*l.n, sizeof(box));
-    float **probs = calloc(side*side*l.n, sizeof(float *));
-    for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
-
     int m = plist->size;
     int i=0;
     int t;
@@ -199,9 +193,11 @@ void validate_coco(char *cfg, char *weights)
             network_predict(net, X);
             int w = val[t].w;
             int h = val[t].h;
-            get_detection_boxes(l, w, h, thresh, probs, boxes, 0);
-            if (nms) do_nms_sort(boxes, probs, side*side*l.n, classes, iou_thresh);
-            print_cocos(fp, image_id, boxes, probs, side*side*l.n, classes, w, h);
+            int nboxes = 0;
+            detection *dets = get_network_boxes(net, w, h, thresh, 0, 0, 0, &nboxes);
+            if (nms) do_nms_sort(dets, l.side*l.side*l.n, classes, iou_thresh);
+            print_cocos(fp, image_id, dets, l.side*l.side*l.n, classes, w, h);
+            free_detections(dets, nboxes);
             free_image(val[t]);
             free_image(val_resized[t]);
         }
@@ -235,17 +231,13 @@ void validate_coco_recall(char *cfgfile, char *weightfile)
         snprintf(buff, 1024, "%s%s.txt", base, coco_classes[j]);
         fps[j] = fopen(buff, "w");
     }
-    box *boxes = calloc(side*side*l.n, sizeof(box));
-    float **probs = calloc(side*side*l.n, sizeof(float *));
-    for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
 
     int m = plist->size;
     int i=0;
 
     float thresh = .001;
     int nms = 0;
     float iou_thresh = .5;
-    float nms_thresh = .5;
 
     int total = 0;
     int correct = 0;
@@ -258,8 +250,10 @@ void validate_coco_recall(char *cfgfile, char *weightfile)
         image sized = resize_image(orig, net->w, net->h);
         char *id = basecfg(path);
         network_predict(net, sized.data);
-        get_detection_boxes(l, 1, 1, thresh, probs, boxes, 1);
-        if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms_thresh);
+
+        int nboxes = 0;
+        detection *dets = get_network_boxes(net, orig.w, orig.h, thresh, 0, 0, 1, &nboxes);
+        if (nms) do_nms_obj(dets, side*side*l.n, 1, nms);
 
         char labelpath[4096];
         find_replace(path, "images", "labels", labelpath);
@@ -270,7 +264,7 @@ void validate_coco_recall(char *cfgfile, char *weightfile)
         int num_labels = 0;
         box_label *truth = read_boxes(labelpath, &num_labels);
         for(k = 0; k < side*side*l.n; ++k){
-            if(probs[k][0] > thresh){
+            if(dets[k].objectness > thresh){
                 ++proposals;
             }
         }
@@ -279,8 +273,8 @@ void validate_coco_recall(char *cfgfile, char *weightfile)
             box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
             float best_iou = 0;
             for(k = 0; k < side*side*l.n; ++k){
-                float iou = box_iou(boxes[k], t);
-                if(probs[k][0] > thresh && iou > best_iou){
+                float iou = box_iou(dets[k].bbox, t);
+                if(dets[k].objectness > thresh && iou > best_iou){
                     best_iou = iou;
                 }
             }
@@ -289,7 +283,7 @@ void validate_coco_recall(char *cfgfile, char *weightfile)
                 ++correct;
             }
         }
-
+        free_detections(dets, nboxes);
         fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
         free(id);
         free_image(orig);
@@ -308,10 +302,6 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
     clock_t time;
     char buff[256];
     char *input = buff;
-    int j;
-    box *boxes = calloc(l.side*l.side*l.n, sizeof(box));
-    float **probs = calloc(l.side*l.side*l.n, sizeof(float *));
-    for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *));
     while(1){
         if(filename){
             strncpy(input, filename, 256);
@@ -328,17 +318,17 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
         time=clock();
         network_predict(net, X);
         printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
-        get_detection_boxes(l, 1, 1, thresh, probs, boxes, 0);
-        if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms);
-        draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, 0, coco_classes, alphabet, 80);
+
+        int nboxes = 0;
+        detection *dets = get_network_boxes(net, 1, 1, thresh, 0, 0, 0, &nboxes);
+        if (nms) do_nms_sort(dets, l.side*l.side*l.n, l.classes, nms);
+
+        draw_detections(im, dets, l.side*l.side*l.n, thresh, coco_classes, alphabet, 80);
         save_image(im, "prediction");
-        show_image(im, "predictions");
+        show_image(im, "predictions", 0);
+        free_detections(dets, nboxes);
         free_image(im);
         free_image(sized);
-#ifdef OPENCV
-        cvWaitKey(0);
-        cvDestroyAllWindows();
-#endif
         if (filename) break;
     }
 }

diff --git a/app/src/main/cpp/darknet/examples/darknet.c b/app/src/main/cpp/darknet/examples/darknet.c
@@ -12,9 +12,9 @@ extern void run_coco(int argc, char **argv);
 extern void run_captcha(int argc, char **argv);
 extern void run_nightmare(int argc, char **argv);
 extern void run_classifier(int argc, char **argv);
-extern void run_attention(int argc, char **argv);
 extern void run_regressor(int argc, char **argv);
 extern void run_segmenter(int argc, char **argv);
+extern void run_isegmenter(int argc, char **argv);
 extern void run_char_rnn(int argc, char **argv);
 extern void run_tag(int argc, char **argv);
 extern void run_cifar(int argc, char **argv);
@@ -189,6 +189,25 @@ void partial(char *cfgfile, char *weightfile, char *outfile, int max)
     save_weights_upto(net, outfile, max);
 }
 
+void print_weights(char *cfgfile, char *weightfile, int n)
+{
+    gpu_index = -1;
+    network *net = load_network(cfgfile, weightfile, 1);
+    layer l = net->layers[n];
+    int i, j;
+    //printf("[");
+    for(i = 0; i < l.n; ++i){
+        //printf("[");
+        for(j = 0; j < l.size*l.size*l.c; ++j){
+            //if(j > 0) printf(",");
+            printf("%g ", l.weights[i*l.size*l.size*l.c + j]);
+        }
+        printf("\n");
+        //printf("]%s\n", (i == l.n-1)?"":",");
+    }
+    //printf("]");
+}
+
 void rescale_net(char *cfgfile, char *weightfile, char *outfile)
 {
     gpu_index = -1;
@@ -415,7 +434,7 @@ int main(int argc, char **argv)
     } else if (0 == strcmp(argv[1], "detector")){
         run_detector(argc, argv);
     } else if (0 == strcmp(argv[1], "detect")){
-        float thresh = find_float_arg(argc, argv, "-thresh", .24);
+        float thresh = find_float_arg(argc, argv, "-thresh", .5);
         char *filename = (argc > 4) ? argv[4]: 0;
         char *outfile = find_char_arg(argc, argv, "-out", 0);
         int fullscreen = find_arg(argc, argv, "-fullscreen");
@@ -432,10 +451,10 @@ int main(int argc, char **argv)
         predict_classifier("cfg/imagenet1k.data", argv[2], argv[3], argv[4], 5);
     } else if (0 == strcmp(argv[1], "classifier")){
         run_classifier(argc, argv);
-    } else if (0 == strcmp(argv[1], "attention")){
-        run_attention(argc, argv);
     } else if (0 == strcmp(argv[1], "regressor")){
         run_regressor(argc, argv);
+    } else if (0 == strcmp(argv[1], "isegmenter")){
+        run_isegmenter(argc, argv);
     } else if (0 == strcmp(argv[1], "segmenter")){
         run_segmenter(argc, argv);
     } else if (0 == strcmp(argv[1], "art")){
@@ -470,6 +489,8 @@ int main(int argc, char **argv)
         oneoff(argv[2], argv[3], argv[4]);
     } else if (0 == strcmp(argv[1], "oneoff2")){
         oneoff2(argv[2], argv[3], argv[4], atoi(argv[5]));
+    } else if (0 == strcmp(argv[1], "print")){
+        print_weights(argv[2], argv[3], atoi(argv[4]));
     } else if (0 == strcmp(argv[1], "partial")){
         partial(argv[2], argv[3], argv[4], atoi(argv[5]));
     } else if (0 == strcmp(argv[1], "average")){

diff --git a/app/src/main/cpp/darknet/examples/detector.c b/app/src/main/cpp/darknet/examples/detector.c
diff --git a/app/src/main/cpp/darknet/examples/instance-segmenter.c b/app/src/main/cpp/darknet/examples/instance-segmenter.c
@@ -0,0 +1,265 @@
+#include "darknet.h"
+#include <sys/time.h>
+#include <assert.h>
+
+void train_isegmenter(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int display)
+{
+    int i;
+
+    float avg_loss = -1;
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    printf("%d\n", ngpus);
+    network **nets = calloc(ngpus, sizeof(network*));
+
+    srand(time(0));
+    int seed = rand();
+    for(i = 0; i < ngpus; ++i){
+        srand(seed);
+#ifdef GPU
+        cuda_set_device(gpus[i]);
+#endif
+        nets[i] = load_network(cfgfile, weightfile, clear);
+        nets[i]->learning_rate *= ngpus;
+    }
+    srand(time(0));
+    network *net = nets[0];
+    image pred = get_network_image(net);
+
+    int div = net->w/pred.w;
+    assert(pred.w * div == net->w);
+    assert(pred.h * div == net->h);
+
+    int imgs = net->batch * net->subdivisions * ngpus;
+
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
+    list *options = read_data_cfg(datacfg);
+
+    char *backup_directory = option_find_str(options, "backup", "/backup/");
+    char *train_list = option_find_str(options, "train", "data/train.list");
+
+    list *plist = get_paths(train_list);
+    char **paths = (char **)list_to_array(plist);
+    printf("%d\n", plist->size);
+    int N = plist->size;
+
+    load_args args = {0};
+    args.w = net->w;
+    args.h = net->h;
+    args.threads = 32;
+    args.scale = div;
+    args.num_boxes = 90;
+
+    args.min = net->min_crop;
+    args.max = net->max_crop;
+    args.angle = net->angle;
+    args.aspect = net->aspect;
+    args.exposure = net->exposure;
+    args.saturation = net->saturation;
+    args.hue = net->hue;
+    args.size = net->w;
+    args.classes = 80;
+
+    args.paths = paths;
+    args.n = imgs;
+    args.m = N;
+    args.type = ISEG_DATA;
+
+    data train;
+    data buffer;
+    pthread_t load_thread;
+    args.d = &buffer;
+    load_thread = load_data(args);
+
+    int epoch = (*net->seen)/N;
+    while(get_current_batch(net) < net->max_batches || net->max_batches == 0){
+        double time = what_time_is_it_now();
+
+        pthread_join(load_thread, 0);
+        train = buffer;
+        load_thread = load_data(args);
+
+        printf("Loaded: %lf seconds\n", what_time_is_it_now()-time);
+        time = what_time_is_it_now();
+
+        float loss = 0;
+#ifdef GPU
+        if(ngpus == 1){
+            loss = train_network(net, train);
+        } else {
+            loss = train_networks(nets, ngpus, train, 4);
+        }
+#else
+        loss = train_network(net, train);
+#endif
+        if(display){
+            image tr = float_to_image(net->w/div, net->h/div, 80, train.y.vals[net->batch*(net->subdivisions-1)]);
+            image im = float_to_image(net->w, net->h, net->c, train.X.vals[net->batch*(net->subdivisions-1)]);
+            pred.c = 80;
+            image mask = mask_to_rgb(tr);
+            image prmask = mask_to_rgb(pred);
+            show_image(im, "input", 1);
+            show_image(prmask, "pred", 1);
+            show_image(mask, "truth", 100);
+            free_image(mask);
+            free_image(prmask);
+        }
+        if(avg_loss == -1) avg_loss = loss;
+        avg_loss = avg_loss*.9 + loss*.1;
+        printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), what_time_is_it_now()-time, *net->seen);
+        free_data(train);
+        if(*net->seen/N > epoch){
+            epoch = *net->seen/N;
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
+            save_weights(net, buff);
+        }
+        if(get_current_batch(net)%100 == 0){
+            char buff[256];
+            sprintf(buff, "%s/%s.backup",backup_directory,base);
+            save_weights(net, buff);
+        }
+    }
+    char buff[256];
+    sprintf(buff, "%s/%s.weights", backup_directory, base);
+    save_weights(net, buff);
+
+    free_network(net);
+    free_ptrs((void**)paths, plist->size);
+    free_list(plist);
+    free(base);
+}
+
+void predict_isegmenter(char *datafile, char *cfg, char *weights, char *filename)
+{
+    network *net = load_network(cfg, weights, 0);
+    set_batch_network(net, 1);
+    srand(2222222);
+
+    clock_t time;
+    char buff[256];
+    char *input = buff;
+    while(1){
+        if(filename){
+            strncpy(input, filename, 256);
+        }else{
+            printf("Enter Image Path: ");
+            fflush(stdout);
+            input = fgets(input, 256, stdin);
+            if(!input) return;
+            strtok(input, "\n");
+        }
+        image im = load_image_color(input, 0, 0);
+        image sized = letterbox_image(im, net->w, net->h);
+
+        float *X = sized.data;
+        time=clock();
+        float *predictions = network_predict(net, X);
+        image pred = get_network_image(net);
+        image prmask = mask_to_rgb(pred);
+        printf("Predicted: %f\n", predictions[0]);
+        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+        show_image(sized, "orig", 1);
+        show_image(prmask, "pred", 0);
+        free_image(im);
+        free_image(sized);
+        free_image(prmask);
+        if (filename) break;
+    }
+}
+
+
+void demo_isegmenter(char *datacfg, char *cfg, char *weights, int cam_index, const char *filename)
+{
+#ifdef OPENCV
+    printf("Classifier Demo\n");
+    network *net = load_network(cfg, weights, 0);
+    set_batch_network(net, 1);
+
+    srand(2222222);
+    CvCapture * cap;
+
+    if(filename){
+        cap = cvCaptureFromFile(filename);
+    }else{
+        cap = cvCaptureFromCAM(cam_index);
+    }
+
+    if(!cap) error("Couldn't connect to webcam.\n");
+    cvNamedWindow("Segmenter", CV_WINDOW_NORMAL); 
+    cvResizeWindow("Segmenter", 512, 512);
+    float fps = 0;
+
+    while(1){
+        struct timeval tval_before, tval_after, tval_result;
+        gettimeofday(&tval_before, NULL);
+
+        image in = get_image_from_stream(cap);
+        image in_s = letterbox_image(in, net->w, net->h);
+
+        network_predict(net, in_s.data);
+
+        printf("\033[2J");
+        printf("\033[1;1H");
+        printf("\nFPS:%.0f\n",fps);
+
+        image pred = get_network_image(net);
+        image prmask = mask_to_rgb(pred);
+        show_image(prmask, "Segmenter", 10);
+
+        free_image(in_s);
+        free_image(in);
+        free_image(prmask);
+
+        gettimeofday(&tval_after, NULL);
+        timersub(&tval_after, &tval_before, &tval_result);
+        float curr = 1000000.f/((long int)tval_result.tv_usec);
+        fps = .9*fps + .1*curr;
+    }
+#endif
+}
+
+
+void run_isegmenter(int argc, char **argv)
+{
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
+    int *gpus = 0;
+    int gpu = 0;
+    int ngpus = 0;
+    if(gpu_list){
+        printf("%s\n", gpu_list);
+        int len = strlen(gpu_list);
+        ngpus = 1;
+        int i;
+        for(i = 0; i < len; ++i){
+            if (gpu_list[i] == ',') ++ngpus;
+        }
+        gpus = calloc(ngpus, sizeof(int));
+        for(i = 0; i < ngpus; ++i){
+            gpus[i] = atoi(gpu_list);
+            gpu_list = strchr(gpu_list, ',')+1;
+        }
+    } else {
+        gpu = gpu_index;
+        gpus = &gpu;
+        ngpus = 1;
+    }
+
+    int cam_index = find_int_arg(argc, argv, "-c", 0);
+    int clear = find_arg(argc, argv, "-clear");
+    int display = find_arg(argc, argv, "-display");
+    char *data = argv[3];
+    char *cfg = argv[4];
+    char *weights = (argc > 5) ? argv[5] : 0;
+    char *filename = (argc > 6) ? argv[6]: 0;
+    if(0==strcmp(argv[2], "test")) predict_isegmenter(data, cfg, weights, filename);
+    else if(0==strcmp(argv[2], "train")) train_isegmenter(data, cfg, weights, gpus, ngpus, clear, display);
+    else if(0==strcmp(argv[2], "demo")) demo_isegmenter(data, cfg, weights, cam_index, filename);
+}
+
+