Trained only on 73257 training data. Other 600000 not used. Accuracy: 0.88 (3rd place https://inclass.kaggle.com/c/svhn-mipt2/leaderboard/private)
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SVHN by Caffe
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| name: "WinnySvhnTrainTest" | |
| input: "data" | |
| input_dim: 10 | |
| input_dim: 3 | |
| input_dim: 32 | |
| input_dim: 32 | |
| layers { | |
| bottom: "data" | |
| top: "conv1/5x5_s1" | |
| name: "conv1/5x5_s1" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 5 | |
| stride: 1 | |
| pad: 2 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.0001 | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "conv1/5x5_s1" | |
| top: "conv1/5x5_s1" | |
| name: "conv1/relu_5x5" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv1/5x5_s1" | |
| top: "pool1/3x3_s2" | |
| name: "pool1/3x3_s2" | |
| type: POOLING | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| bottom: "pool1/3x3_s2" | |
| top: "conv2/5x5_s1" | |
| name: "conv2/5x5_s1" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 5 | |
| stride: 1 | |
| pad: 2 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "conv2/5x5_s1" | |
| top: "conv2/5x5_s1" | |
| name: "conv2/relu_5x5" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv2/5x5_s1" | |
| top: "pool2/3x3_s2" | |
| name: "pool2/3x3_s2" | |
| type: POOLING | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| bottom: "pool2/3x3_s2" | |
| top: "conv3/5x5_s1" | |
| name: "conv3/5x5_s1" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 5 | |
| stride: 1 | |
| pad: 2 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "conv3/5x5_s1" | |
| top: "conv3/5x5_s1" | |
| name: "conv3/relu_5x5" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv3/5x5_s1" | |
| top: "pool3/3x3_s2" | |
| name: "pool3/3x3_s2" | |
| type: POOLING | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| bottom: "pool3/3x3_s2" | |
| top: "ip1/3072" | |
| name: "ip1/3072" | |
| type: INNER_PRODUCT | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| inner_product_param { | |
| num_output: 3072 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "ip1/3072" | |
| top: "ip1/3072" | |
| name: "ip1/relu_5x5" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "ip1/3072" | |
| top: "ip2/2048" | |
| name: "ip2/2048" | |
| type: INNER_PRODUCT | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| inner_product_param { | |
| num_output: 2048 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "ip2/2048" | |
| top: "ip2/2048" | |
| name: "ip2/relu_5x5" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "ip2/2048" | |
| top: "ip3/10" | |
| name: "ip3/10" | |
| type: INNER_PRODUCT | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| inner_product_param { | |
| num_output: 10 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| } | |
| } | |
| layers { | |
| name: "prob" | |
| type: SOFTMAX | |
| bottom: "ip3/10" | |
| top: "prob" | |
| } | |
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| net: "/home/deploy/opt/SVHN/svhn/winny-f/winny_f_svhn.prototxt" | |
| test_iter: 1 | |
| test_interval: 700 | |
| base_lr: 0.01 | |
| momentum: 0.9 | |
| weight_decay: 0.004 | |
| lr_policy: "inv" | |
| gamma: 0.0001 | |
| power: 0.75 | |
| solver_type: NESTEROV | |
| display: 100 | |
| max_iter: 77000 | |
| snapshot: 700 | |
| snapshot_prefix: "/mnt/home/deploy/opt/SVHN/svhn/snapshots/winny_net/winny-F" | |
| solver_mode: GPU | |
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| name: "WinnyNet-F" | |
| layers { | |
| name: "svhn-rgb" | |
| type: IMAGE_DATA | |
| top: "data" | |
| top: "label" | |
| image_data_param { | |
| source: "/home/deploy/opt/SVHN/train-rgb-b.txt" | |
| batch_size: 128 | |
| shuffle: true | |
| } | |
| transform_param { | |
| mean_file: "/home/deploy/opt/SVHN/svhn/winny_net5/mean.binaryproto" | |
| } | |
| include: { phase: TRAIN } | |
| } | |
| layers { | |
| name: "svhn-rgb" | |
| type: IMAGE_DATA | |
| top: "data" | |
| top: "label" | |
| image_data_param { | |
| source: "/home/deploy/opt/SVHN/test-rgb-b.txt" | |
| batch_size: 120 | |
| } | |
| transform_param { | |
| mean_file: "/home/deploy/opt/SVHN/svhn/winny_net5/mean.binaryproto" | |
| } | |
| include: { phase: TEST } | |
| } | |
| layers { | |
| bottom: "data" | |
| top: "conv1/5x5_s1" | |
| name: "conv1/5x5_s1" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 5 | |
| stride: 1 | |
| pad: 2 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.0001 | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "conv1/5x5_s1" | |
| top: "conv1/5x5_s1" | |
| name: "conv1/relu_5x5" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv1/5x5_s1" | |
| top: "pool1/3x3_s2" | |
| name: "pool1/3x3_s2" | |
| type: POOLING | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| bottom: "pool1/3x3_s2" | |
| top: "conv2/5x5_s1" | |
| name: "conv2/5x5_s1" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 5 | |
| stride: 1 | |
| pad: 2 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "conv2/5x5_s1" | |
| top: "conv2/5x5_s1" | |
| name: "conv2/relu_5x5" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv2/5x5_s1" | |
| top: "pool2/3x3_s2" | |
| name: "pool2/3x3_s2" | |
| type: POOLING | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| bottom: "pool2/3x3_s2" | |
| top: "conv3/5x5_s1" | |
| name: "conv3/5x5_s1" | |
| type: CONVOLUTION | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 5 | |
| stride: 1 | |
| pad: 2 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.01 | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "conv3/5x5_s1" | |
| top: "conv3/5x5_s1" | |
| name: "conv3/relu_5x5" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "conv3/5x5_s1" | |
| top: "pool3/3x3_s2" | |
| name: "pool3/3x3_s2" | |
| type: POOLING | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layers { | |
| bottom: "pool3/3x3_s2" | |
| top: "ip1/3072" | |
| name: "ip1/3072" | |
| type: INNER_PRODUCT | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| inner_product_param { | |
| num_output: 3072 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "ip1/3072" | |
| top: "ip1/3072" | |
| name: "ip1/relu_5x5" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "ip1/3072" | |
| top: "ip2/2048" | |
| name: "ip2/2048" | |
| type: INNER_PRODUCT | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| inner_product_param { | |
| num_output: 2048 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layers { | |
| bottom: "ip2/2048" | |
| top: "ip2/2048" | |
| name: "ip2/relu_5x5" | |
| type: RELU | |
| } | |
| layers { | |
| bottom: "ip2/2048" | |
| top: "ip3/10" | |
| name: "ip3/10" | |
| type: INNER_PRODUCT | |
| blobs_lr: 1 | |
| blobs_lr: 2 | |
| inner_product_param { | |
| num_output: 10 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| } | |
| } | |
| layers { | |
| name: "accuracy" | |
| type: ACCURACY | |
| bottom: "ip3/10" | |
| bottom: "label" | |
| top: "accuracy" | |
| include: { phase: TEST } | |
| } | |
| layers { | |
| name: "loss" | |
| type: SOFTMAX_LOSS | |
| bottom: "ip3/10" | |
| bottom: "label" | |
| top: "loss" | |
| } |
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Hey could you add your code how did you use data from mat file for Caffe model?