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Solver.prototxt and Train_Val.prototxt used alongside my "rough faces" data set to improve style transfer results.
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| net: "/home/ubuntu/caffe/models/vgg16_finetune/vgg16_train_val.prototxt" | |
| test_iter: 50 | |
| test_interval: 100 | |
| # lr for fine-tuning should be lower than when starting from scratch | |
| base_lr: 0.00003 | |
| lr_policy: "step" | |
| #gamma: 0.1 | |
| gamma: 0.1 | |
| # decrease lr each 20000 iterations | |
| #stepsize: 20000 | |
| stepsize: 5000 | |
| display: 10 | |
| max_iter: 600000 | |
| momentum: 0.9 | |
| weight_decay: 0.000005 | |
| snapshot: 1000 | |
| snapshot_prefix: "examples/imagenet/VGG16_SOD_finetune_rough_faces" | |
| solver_mode: GPU | |
| #debug_info: true |
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| name: "VGG16_SOD_finetune" | |
| layers { | |
| top: "data" | |
| top: "label" | |
| name: "data" | |
| type: DATA | |
| data_param { | |
| source: "/home/ubuntu/caffe/examples/imagenet/faces_train_lmdb" | |
| backend: LMDB | |
| batch_size: 64 | |
| } | |
| transform_param { | |
| crop_size: 224 | |
| #mirror: true | |
| mean_file: "/home/ubuntu/caffe/examples/imagenet/faces_train_mean.binaryproto" | |
| } | |
| include: { phase: TRAIN } | |
| } | |
| layers { | |
| top: "data" | |
| top: "label" | |
| name: "data" | |
| type: DATA | |
| data_param { | |
| source: "/home/ubuntu/caffe/examples/imagenet/faces_val_lmdb" | |
| backend: LMDB | |
| batch_size: 8 | |
| } | |
| transform_param { | |
| crop_size: 224 | |
| #mirror: false | |
| mean_file: "/home/ubuntu/caffe/examples/imagenet/faces_val_mean.binaryproto" | |
| } | |
| include: { phase: TEST } | |
| } | |
| layers { | |
| bottom: "data" | |
| top: "conv1_1" | |
| name: "conv1_1" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0 | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "conv1_1" | |
| top: "conv1_1" | |
| name: "relu1_1" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv1_1" | |
| top: "conv1_2" | |
| name: "conv1_2" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0 | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "conv1_2" | |
| top: "conv1_2" | |
| name: "relu1_2" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv1_2" | |
| top: "pool1" | |
| name: "pool1" | |
| type: POOLING | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| bottom: "pool1" | |
| top: "conv2_1" | |
| name: "conv2_1" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0 | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "conv2_1" | |
| top: "conv2_1" | |
| name: "relu2_1" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv2_1" | |
| top: "conv2_2" | |
| name: "conv2_2" | |
| type: CONVOLUTION | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0 | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "conv2_2" | |
| top: "conv2_2" | |
| name: "relu2_2" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv2_2" | |
| top: "pool2" | |
| name: "pool2" | |
| type: POOLING | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| bottom: "pool2" | |
| top: "conv3_1" | |
| name: "conv3_1" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0 | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "conv3_1" | |
| top: "conv3_1" | |
| name: "relu3_1" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv3_1" | |
| top: "conv3_2" | |
| name: "conv3_2" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0 | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "conv3_2" | |
| top: "conv3_2" | |
| name: "relu3_2" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv3_2" | |
| top: "conv3_3" | |
| name: "conv3_3" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0 | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "conv3_3" | |
| top: "conv3_3" | |
| name: "relu3_3" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv3_3" | |
| top: "pool3" | |
| name: "pool3" | |
| type: POOLING | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| bottom: "pool3" | |
| top: "conv4_1" | |
| name: "conv4_1" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0 | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "conv4_1" | |
| top: "conv4_1" | |
| name: "relu4_1" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv4_1" | |
| top: "conv4_2" | |
| name: "conv4_2" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0 | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "conv4_2" | |
| top: "conv4_2" | |
| name: "relu4_2" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv4_2" | |
| top: "conv4_3" | |
| name: "conv4_3" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0 | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "conv4_3" | |
| top: "conv4_3" | |
| name: "relu4_3" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv4_3" | |
| top: "pool4" | |
| name: "pool4" | |
| type: POOLING | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| bottom: "pool4" | |
| top: "conv5_1" | |
| name: "conv5_1" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0 | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "conv5_1" | |
| top: "conv5_1" | |
| name: "relu5_1" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv5_1" | |
| top: "conv5_2" | |
| name: "conv5_2" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0 | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "conv5_2" | |
| top: "conv5_2" | |
| name: "relu5_2" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv5_2" | |
| top: "conv5_3" | |
| name: "conv5_3" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0 | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "conv5_3" | |
| top: "conv5_3" | |
| name: "relu5_3" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv5_3" | |
| top: "pool5" | |
| name: "pool5" | |
| type: POOLING | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| name: "fc6" | |
| type: INNER_PRODUCT | |
| bottom: "pool5" | |
| top: "fc6" | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layers { | |
| name: "relu6" | |
| type: RELU | |
| bottom: "fc6" | |
| top: "fc6" | |
| } | |
| layers { | |
| name: "drop6" | |
| type: DROPOUT | |
| bottom: "fc6" | |
| top: "fc6" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layers { | |
| name: "fc7" | |
| type: INNER_PRODUCT | |
| bottom: "fc6" | |
| top: "fc7" | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layers { | |
| name: "relu7" | |
| type: RELU | |
| bottom: "fc7" | |
| top: "fc7" | |
| } | |
| layers { | |
| name: "drop7" | |
| type: DROPOUT | |
| bottom: "fc7" | |
| top: "fc7" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layers { | |
| bottom: "fc7" | |
| top: "fc8_faces" | |
| name: "fc8_faces" | |
| type: INNER_PRODUCT | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| weight_decay: 1 | |
| weight_decay: 0 | |
| inner_product_param { | |
| num_output: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| mean: 0 | |
| std: 0.05 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| #layers { | |
| # bottom: "fc8_faces" | |
| # top: "prob" | |
| # name: "prob" | |
| # type: SOFTMAX | |
| #} | |
| layers { | |
| name: "accuracy" | |
| type: ACCURACY | |
| bottom: "fc8_faces" | |
| bottom: "label" | |
| top: "accuracy" | |
| include: { phase: TEST } | |
| } | |
| layers { | |
| bottom: "fc8_faces" | |
| bottom: "label" | |
| name: "loss" | |
| type: SOFTMAX_LOSS | |
| include: { phase: TRAIN } | |
| } | |
| layers { | |
| name: "accuracy/top1" | |
| type: ACCURACY | |
| bottom: "fc8_faces" | |
| bottom: "label" | |
| top: "accuracy@1" | |
| include: { phase: TEST } | |
| accuracy_param { | |
| top_k: 1 | |
| } | |
| } | |
| layers { | |
| name: "accuracy/top5" | |
| type: ACCURACY | |
| bottom: "fc8_faces" | |
| bottom: "label" | |
| top: "accuracy@2" | |
| include: { phase: TEST } | |
| accuracy_param { | |
| top_k: 2 | |
| } | |
| } |
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