move old converted models to legacy, add converted tiny, coco and small

The models converted from a old version of Darknet v1 have been moved in
the 'legacy' path and three new models (yolo_tiny, yolo_small and
coco_tiny) have been converted from the most recent Darknet v1 code.

prototxt/coco_tiny_deploy.prototxt

@@ -0,0 +1,336 @@
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param {
+    shape {
+      dim: 1
+      dim: 3
+      dim: 448
+      dim: 448
+    }
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  convolution_param {
+    num_output: 16
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "conv1_scale"
+  type: "Scale"
+  bottom: "conv1"
+  top: "conv1_scale"
+  scale_param {
+    axis: 1
+    bias_term: true
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1_scale"
+  top: "conv1_scale"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1_scale"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  convolution_param {
+    num_output: 32
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "conv2_scale"
+  type: "Scale"
+  bottom: "conv2"
+  top: "conv2_scale"
+  scale_param {
+    axis: 1
+    bias_term: true
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2_scale"
+  top: "conv2_scale"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "conv2_scale"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "conv3_scale"
+  type: "Scale"
+  bottom: "conv3"
+  top: "conv3_scale"
+  scale_param {
+    axis: 1
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3_scale"
+  top: "conv3_scale"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "pool3"
+  type: "Pooling"
+  bottom: "conv3_scale"
+  top: "pool3"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "pool3"
+  top: "conv4"
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "conv4_scale"
+  type: "Scale"
+  bottom: "conv4"
+  top: "conv4_scale"
+  scale_param {
+    axis: 1
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4_scale"
+  top: "conv4_scale"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "pool4"
+  type: "Pooling"
+  bottom: "conv4_scale"
+  top: "pool4"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "pool4"
+  top: "conv5"
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "conv5_scale"
+  type: "Scale"
+  bottom: "conv5"
+  top: "conv5_scale"
+  scale_param {
+    axis: 1
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5_scale"
+  top: "conv5_scale"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5_scale"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv6"
+  type: "Convolution"
+  bottom: "pool5"
+  top: "conv6"
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "conv6_scale"
+  type: "Scale"
+  bottom: "conv6"
+  top: "conv6_scale"
+  scale_param {
+    axis: 1
+    bias_term: true
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "conv6_scale"
+  top: "conv6_scale"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "pool6"
+  type: "Pooling"
+  bottom: "conv6_scale"
+  top: "pool6"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv7"
+  type: "Convolution"
+  bottom: "pool6"
+  top: "conv7"
+  convolution_param {
+    num_output: 1024
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "conv7_scale"
+  type: "Scale"
+  bottom: "conv7"
+  top: "conv7_scale"
+  scale_param {
+    axis: 1
+    bias_term: true
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "conv7_scale"
+  top: "conv7_scale"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "conv8"
+  type: "Convolution"
+  bottom: "conv7_scale"
+  top: "conv8"
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "conv8_scale"
+  type: "Scale"
+  bottom: "conv8"
+  top: "conv8_scale"
+  scale_param {
+    axis: 1
+    bias_term: true
+  }
+}
+layer {
+  name: "relu8"
+  type: "ReLU"
+  bottom: "conv8_scale"
+  top: "conv8_scale"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "fc9"
+  type: "InnerProduct"
+  bottom: "conv8_scale"
+  top: "result"
+  inner_product_param {
+    num_output: 4655
+  }
+}