I want to train SSD net on ILSVRC2014_devkit dataset. I have already create lmdb files like VOC0712,and I change the num classes to 201 (200+1). I thought every thing is OK, BUT when I begin to train SSD on Imagenet data. I met this error, could anybody give me some advise? I1103 19:10:10.231252 11334 layer_factory.hpp:77] Creating layer data I1103 19:10:10.231623 11334 net.cpp:100] Creating Layer data I1103 19:10:10.231634 11334 net.cpp:408] data -> data I1103 19:10:10.231657 11334 net.cpp:408] data -> label I1103 19:10:10.231673 11334 data_transformer.cpp:28] Loading mean file from: examples/ILSVRC2014/mean.binaryproto I1103 19:10:10.232180 11343 db_lmdb.cpp:35] Opened lmdb examples/ILSVRC2014/ILSVRC2014_trainval_lmdb

F1103 19:10:10.262428 11334 blob.cpp:115] Check failed: data_

Check failure stack trace: @ 0x7ff8c2b6d5cd google::LogMessage::Fail() @ 0x7ff8c2b6f433 google::LogMessage::SendToLog() @ 0x7ff8c2b6d15b google::LogMessage::Flush() @ 0x7ff8c2b6fe1e google::LogMessageFatal::~LogMessageFatal() @ 0x7ff8c33632fb caffe::Blob<>::mutable_cpu_data() @ 0x7ff8c337002b caffe::Blob<>::FromProto() @ 0x7ff8c3399179 caffe::DataTransformer<>::DataTransformer() @ 0x7ff8c32ca3c9 caffe::BaseDataLayer<>::LayerSetUp() @ 0x7ff8c32ca503 caffe::BasePrefetchingDataLayer<>::LayerSetUp() @ 0x7ff8c334d257 caffe::Net<>::Init() @ 0x7ff8c334eae1 caffe::Net<>::Net() @ 0x7ff8c3357d8a caffe::Solver<>::InitTrainNet() @ 0x7ff8c3359197 caffe::Solver<>::Init() @ 0x7ff8c335953a caffe::Solver<>::Solver() @ 0x7ff8c3381453 caffe::Creator_SGDSolver<>() @ 0x40c21a train() @ 0x4088e8 main @ 0x7ff8c1080830 __libc_start_main @ 0x4091b9 _start @ (nil) (unknown) Aborted (core dumped)

and my net is I1103 19:10:10.007732 11334 caffe.cpp:217] Using GPUs 0 I1103 19:10:10.021906 11334 caffe.cpp:222] GPU 0: GeForce GTX 1070 I1103 19:10:10.229830 11334 solver.cpp:51] Initializing solver from parameters: train_net: "models/VGGNet/Imagenet/SSD_Imagenet300x300/train.prototxt" test_net: "models/VGGNet/Imagenet/SSD_Imagenet300x300/test.prototxt" test_iter: 4952 test_interval: 10000 base_lr: 0.001 display: 10 max_iter: 60000 lr_policy: "step" gamma: 0.1 momentum: 0.9 weight_decay: 0.0005 stepsize: 40000 snapshot: 40000 snapshot_prefix: "models/VGGNet/Imagenet/SSD_Imagenet300x300/VGG_Imagenet_SSD_Imagenet300x300" solver_mode: GPU device_id: 0 debug_info: false train_state { level: 0 stage: "" } snapshot_after_train: true test_initialization: false average_loss: 10 iter_size: 1 type: "SGD" eval_type: "detection" ap_version: "11point" I1103 19:10:10.229925 11334 solver.cpp:84] Creating training net from train_net file: models/VGGNet/Imagenet/SSD_Imagenet300x300/train.prototxt I1103 19:10:10.230952 11334 net.cpp:58] Initializing net from parameters: name: "VGG_Imagenet_SSD_Imagenet300x300_train" state { phase: TRAIN level: 0 stage: "" } layer { name: "data" type: "AnnotatedData" top: "data" top: "label" include { phase: TRAIN } transform_param { mirror: true mean_file: "examples/ILSVRC2014/mean.binaryproto" force_color: true resize_param { prob: 1 resize_mode: WARP height: 300 width: 300 interp_mode: LINEAR interp_mode: AREA interp_mode: NEAREST interp_mode: CUBIC interp_mode: LANCZOS4 } emit_constraint { emit_type: CENTER } } data_param { source: "examples/ILSVRC2014/ILSVRC2014_trainval_lmdb" batch_size: 32 backend: LMDB } annotated_data_param { batch_sampler { max_sample: 1 max_trials: 1 } batch_sampler { sampler { min_scale: 0.3 max_scale: 1 min_aspect_ratio: 0.5 max_aspect_ratio: 2 } sample_constraint { min_jaccard_overlap: 0.1 } max_sample: 1 max_trials: 50 } batch_sampler { sampler { min_scale: 0.3 max_scale: 1 min_aspect_ratio: 0.5 max_aspect_ratio: 2 } sample_constraint { min_jaccard_overlap: 0.3 } max_sample: 1 max_trials: 50 } batch_sampler { sampler { min_scale: 0.3 max_scale: 1 min_aspect_ratio: 0.5 max_aspect_ratio: 2 } sample_constraint { min_jaccard_overlap: 0.5 } max_sample: 1 max_trials: 50 } batch_sampler { sampler { min_scale: 0.3 max_scale: 1 min_aspect_ratio: 0.5 max_aspect_ratio: 2 } sample_constraint { min_jaccard_overlap: 0.7 } max_sample: 1 max_trials: 50 } batch_sampler { sampler { min_scale: 0.3 max_scale: 1 min_aspect_ratio: 0.5 max_aspect_ratio: 2 } sample_constraint { min_jaccard_overlap: 0.9 } max_sample: 1 max_trials: 50 } batch_sampler { sampler { min_scale: 0.3 max_scale: 1 min_aspect_ratio: 0.5 max_aspect_ratio: 2 } sample_constraint { max_jaccard_overlap: 1 } max_sample: 1 max_trials: 50 } label_map_file: "data/ILSVRC2014/labelmap_ilsvrc_det.prototxt" } } layer { name: "conv1_1" type: "Convolution" bottom: "data" top: "conv1_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 64 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu1_1" type: "ReLU" bottom: "conv1_1" top: "conv1_1" } layer { name: "conv1_2" type: "Convolution" bottom: "conv1_1" top: "conv1_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 64 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu1_2" type: "ReLU" bottom: "conv1_2" top: "conv1_2" } layer { name: "pool1" type: "Pooling" bottom: "conv1_2" top: "pool1" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv2_1" type: "Convolution" bottom: "pool1" top: "conv2_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu2_1" type: "ReLU" bottom: "conv2_1" top: "conv2_1" } layer { name: "conv2_2" type: "Convolution" bottom: "conv2_1" top: "conv2_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu2_2" type: "ReLU" bottom: "conv2_2" top: "conv2_2" } layer { name: "pool2" type: "Pooling" bottom: "conv2_2" top: "pool2" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv3_1" type: "Convolution" bottom: "pool2" top: "conv3_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu3_1" type: "ReLU" bottom: "conv3_1" top: "conv3_1" } layer { name: "conv3_2" type: "Convolution" bottom: "conv3_1" top: "conv3_2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu3_2" type: "ReLU" bottom: "conv3_2" top: "conv3_2" } layer { name: "conv3_3" type: "Convolution" bottom: "conv3_2" top: "conv3_3" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu3_3" type: "ReLU" bottom: "conv3_3" top: "conv3_3" } layer { name: "pool3" type: "Pooling" bottom: "conv3_3" top: "pool3" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv4_1" type: "Convolution" bottom: "pool3" top: "conv4_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu4_1" type: "ReLU" bottom: "conv4_1" top: "conv4_1" } layer { name: "conv4_2" type: "Convolution" bottom: "conv4_1" top: "conv4_2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu4_2" type: "ReLU" bottom: "conv4_2" top: "conv4_2" } layer { name: "conv4_3" type: "Convolution" bottom: "conv4_2" top: "conv4_3" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu4_3" type: "ReLU" bottom: "conv4_3" top: "conv4_3" } layer { name: "pool4" type: "Pooling" bottom: "conv4_3" top: "pool4" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv5_1" type: "Convolution" bottom: "pool4" top: "conv5_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu5_1" type: "ReLU" bottom: "conv5_1" top: "conv5_1" } layer { name: "conv5_2" type: "Convolution" bottom: "conv5_1" top: "conv5_2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu5_2" type: "ReLU" bottom: "conv5_2" top: "conv5_2" } layer { name: "conv5_3" type: "Convolution" bottom: "conv5_2" top: "conv5_3" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu5_3" type: "ReLU" bottom: "conv5_3" top: "conv5_3" } layer { name: "pool5" type: "Pooling" bottom: "conv5_3" top: "pool5" pooling_param { pool: MAX kernel_size: 3 stride: 1 pad: 1 } } layer { name: "fc6" type: "Convolution" bottom: "pool5" top: "fc6" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 1024 pad: 6 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } dilation: 6 } } layer { name: "relu6" type: "ReLU" bottom: "fc6" top: "fc6" } layer { name: "fc7" type: "Convolution" bottom: "fc6" top: "fc7" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 1024 kernel_size: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "relu7" type: "ReLU" bottom: "fc7" top: "fc7" } layer { name: "conv6_1" type: "Convolution" bottom: "fc7" top: "conv6_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 0 kernel_size: 1 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv6_1_relu" type: "ReLU" bottom: "conv6_1" top: "conv6_1" } layer { name: "conv6_2" type: "Convolution" bottom: "conv6_1" top: "conv6_2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 stride: 2 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv6_2_relu" type: "ReLU" bottom: "conv6_2" top: "conv6_2" } layer { name: "conv7_1" type: "Convolution" bottom: "conv6_2" top: "conv7_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv7_1_relu" type: "ReLU" bottom: "conv7_1" top: "conv7_1" } layer { name: "conv7_2" type: "Convolution" bottom: "conv7_1" top: "conv7_2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 stride: 2 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv7_2_relu" type: "ReLU" bottom: "conv7_2" top: "conv7_2" } layer { name: "conv8_1" type: "Convolution" bottom: "conv7_2" top: "conv8_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv8_1_relu" type: "ReLU" bottom: "conv8_1" top: "conv8_1" } layer { name: "conv8_2" type: "Convolution" bottom: "conv8_1" top: "conv8_2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 stride: 2 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv8_2_relu" type: "ReLU" bottom: "conv8_2" top: "conv8_2" } layer { name: "pool6" type: "Pooling" bottom: "conv8_2" top: "pool6" pooling_param { pool: AVE global_pooling: true } } layer { name: "conv4_3_norm" type: "Normalize" bottom: "conv4_3" top: "conv4_3_norm" norm_param { across_spatial: false scale_filler { type: "constant" value: 20 } channel_shared: false } } layer { name: "conv4_3_norm_mbox_loc" type: "Convolution" bottom: "conv4_3_norm" top: "conv4_3_norm_mbox_loc" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 12 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv4_3_norm_mbox_loc_perm" type: "Permute" bottom: "conv4_3_norm_mbox_loc" top: "conv4_3_norm_mbox_loc_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "conv4_3_norm_mbox_loc_flat" type: "Flatten" bottom: "conv4_3_norm_mbox_loc_perm" top: "conv4_3_norm_mbox_loc_flat" flatten_param { axis: 1 } } layer { name: "conv4_3_norm_mbox_conf" type: "Convolution" bottom: "conv4_3_norm" top: "conv4_3_norm_mbox_conf" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 603 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv4_3_norm_mbox_conf_perm" type: "Permute" bottom: "conv4_3_norm_mbox_conf" top: "conv4_3_norm_mbox_conf_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "conv4_3_norm_mbox_conf_flat" type: "Flatten" bottom: "conv4_3_norm_mbox_conf_perm" top: "conv4_3_norm_mbox_conf_flat" flatten_param { axis: 1 } } layer { name: "conv4_3_norm_mbox_priorbox" type: "PriorBox" bottom: "conv4_3_norm" bottom: "data" top: "conv4_3_norm_mbox_priorbox" prior_box_param { min_size: 30 aspect_ratio: 2 flip: true clip: true variance: 0.1 variance: 0.1 variance: 0.2 variance: 0.2 } } layer { name: "fc7_mbox_loc" type: "Convolution" bottom: "fc7" top: "fc7_mbox_loc" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 24 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "fc7_mbox_loc_perm" type: "Permute" bottom: "fc7_mbox_loc" top: "fc7_mbox_loc_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "fc7_mbox_loc_flat" type: "Flatten" bottom: "fc7_mbox_loc_perm" top: "fc7_mbox_loc_flat" flatten_param { axis: 1 } } layer { name: "fc7_mbox_conf" type: "Convolution" bottom: "fc7" top: "fc7_mbox_conf" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 1206 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "fc7_mbox_conf_perm" type: "Permute" bottom: "fc7_mbox_conf" top: "fc7_mbox_conf_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "fc7_mbox_conf_flat" type: "Flatten" bottom: "fc7_mbox_conf_perm" top: "fc7_mbox_conf_flat" flatten_param { axis: 1 } } layer { name: "fc7_mbox_priorbox" type: "PriorBox" bottom: "fc7" bottom: "data" top: "fc7_mbox_priorbox" prior_box_param { min_size: 60 max_size: 114 aspect_ratio: 2 aspect_ratio: 3 flip: true clip: true variance: 0.1 variance: 0.1 variance: 0.2 variance: 0.2 } } layer { name: "conv6_2_mbox_loc" type: "Convolution" bottom: "conv6_2" top: "conv6_2_mbox_loc" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 24 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv6_2_mbox_loc_perm" type: "Permute" bottom: "conv6_2_mbox_loc" top: "conv6_2_mbox_loc_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "conv6_2_mbox_loc_flat" type: "Flatten" bottom: "conv6_2_mbox_loc_perm" top: "conv6_2_mbox_loc_flat" flatten_param { axis: 1 } } layer { name: "conv6_2_mbox_conf" type: "Convolution" bottom: "conv6_2" top: "conv6_2_mbox_conf" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 1206 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv6_2_mbox_conf_perm" type: "Permute" bottom: "conv6_2_mbox_conf" top: "conv6_2_mbox_conf_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "conv6_2_mbox_conf_flat" type: "Flatten" bottom: "conv6_2_mbox_conf_perm" top: "conv6_2_mbox_conf_flat" flatten_param { axis: 1 } } layer { name: "conv6_2_mbox_priorbox" type: "PriorBox" bottom: "conv6_2" bottom: "data" top: "conv6_2_mbox_priorbox" prior_box_param { min_size: 114 max_size: 168 aspect_ratio: 2 aspect_ratio: 3 flip: true clip: true variance: 0.1 variance: 0.1 variance: 0.2 variance: 0.2 } } layer { name: "conv7_2_mbox_loc" type: "Convolution" bottom: "conv7_2" top: "conv7_2_mbox_loc" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 24 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv7_2_mbox_loc_perm" type: "Permute" bottom: "conv7_2_mbox_loc" top: "conv7_2_mbox_loc_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "conv7_2_mbox_loc_flat" type: "Flatten" bottom: "conv7_2_mbox_loc_perm" top: "conv7_2_mbox_loc_flat" flatten_param { axis: 1 } } layer { name: "conv7_2_mbox_conf" type: "Convolution" bottom: "conv7_2" top: "conv7_2_mbox_conf" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 1206 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv7_2_mbox_conf_perm" type: "Permute" bottom: "conv7_2_mbox_conf" top: "conv7_2_mbox_conf_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "conv7_2_mbox_conf_flat" type: "Flatten" bottom: "conv7_2_mbox_conf_perm" top: "conv7_2_mbox_conf_flat" flatten_param { axis: 1 } } layer { name: "conv7_2_mbox_priorbox" type: "PriorBox" bottom: "conv7_2" bottom: "data" top: "conv7_2_mbox_priorbox" prior_box_param { min_size: 168 max_size: 222 aspect_ratio: 2 aspect_ratio: 3 flip: true clip: true variance: 0.1 variance: 0.1 variance: 0.2 variance: 0.2 } } layer { name: "conv8_2_mbox_loc" type: "Convolution" bottom: "conv8_2" top: "conv8_2_mbox_loc" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 24 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv8_2_mbox_loc_perm" type: "Permute" bottom: "conv8_2_mbox_loc" top: "conv8_2_mbox_loc_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "conv8_2_mbox_loc_flat" type: "Flatten" bottom: "conv8_2_mbox_loc_perm" top: "conv8_2_mbox_loc_flat" flatten_param { axis: 1 } } layer { name: "conv8_2_mbox_conf" type: "Convolution" bottom: "conv8_2" top: "conv8_2_mbox_conf" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 1206 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "conv8_2_mbox_conf_perm" type: "Permute" bottom: "conv8_2_mbox_conf" top: "conv8_2_mbox_conf_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "conv8_2_mbox_conf_flat" type: "Flatten" bottom: "conv8_2_mbox_conf_perm" top: "conv8_2_mbox_conf_flat" flatten_param { axis: 1 } } layer { name: "conv8_2_mbox_priorbox" type: "PriorBox" bottom: "conv8_2" bottom: "data" top: "conv8_2_mbox_priorbox" prior_box_param { min_size: 222 max_size: 276 aspect_ratio: 2 aspect_ratio: 3 flip: true clip: true variance: 0.1 variance: 0.1 variance: 0.2 variance: 0.2 } } layer { name: "pool6_mbox_loc" type: "Convolution" bottom: "pool6" top: "pool6_mbox_loc" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 24 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "pool6_mbox_loc_perm" type: "Permute" bottom: "pool6_mbox_loc" top: "pool6_mbox_loc_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "pool6_mbox_loc_flat" type: "Flatten" bottom: "pool6_mbox_loc_perm" top: "pool6_mbox_loc_flat" flatten_param { axis: 1 } } layer { name: "pool6_mbox_conf" type: "Convolution" bottom: "pool6" top: "pool6_mbox_conf" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 1206 pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "pool6_mbox_conf_perm" type: "Permute" bottom: "pool6_mbox_conf" top: "pool6_mbox_conf_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "pool6_mbox_conf_flat" type: "Flatten" bottom: "pool6_mbox_conf_perm" top: "pool6_mbox_conf_flat" flatten_param { axis: 1 } } layer { name: "pool6_mbox_priorbox" type: "PriorBox" bottom: "pool6" bottom: "data" top: "pool6_mbox_priorbox" prior_box_param { min_size: 276 max_size: 330 aspect_ratio: 2 aspect_ratio: 3 flip: true clip: true variance: 0.1 variance: 0.1 variance: 0.2 variance: 0.2 } } layer { name: "mbox_loc" type: "Concat" bottom: "conv4_3_norm_mbox_loc_flat" bottom: "fc7_mbox_loc_flat" bottom: "conv6_2_mbox_loc_flat" bottom: "conv7_2_mbox_loc_flat" bottom: "conv8_2_mbox_loc_flat" bottom: "pool6_mbox_loc_flat" top: "mbox_loc" concat_param { axis: 1 } } layer { name: "mbox_conf" type: "Concat" bottom: "conv4_3_norm_mbox_conf_flat" bottom: "fc7_mbox_conf_flat" bottom: "conv6_2_mbox_conf_flat" bottom: "conv7_2_mbox_conf_flat" bottom: "conv8_2_mbox_conf_flat" bottom: "pool6_mbox_conf_flat" top: "mbox_conf" concat_param { axis: 1 } } layer { name: "mbox_priorbox" type: "Concat" bottom: "conv4_3_norm_mbox_priorbox" bottom: "fc7_mbox_priorbox" bottom: "conv6_2_mbox_priorbox" bottom: "conv7_2_mbox_priorbox" bottom: "conv8_2_mbox_priorbox" bottom: "pool6_mbox_priorbox" top: "mbox_priorbox" concat_param { axis: 2 } } layer { name: "mbox_loss" type: "MultiBoxLoss" bottom: "mbox_loc" bottom: "mbox_conf" bottom: "mbox_priorbox" bottom: "label" top: "mbox_loss" include { phase: TRAIN } propagate_down: true propagate_down: true propagate_down: false propagate_down: false loss_param { normalization: VALID } multibox_loss_param { loc_loss_type: SMOOTH_L1 conf_loss_type: SOFTMAX loc_weight: 1 num_classes: 201 share_location: true match_type: PER_PREDICTION overlap_threshold: 0.5 use_prior_for_matching: true background_label_id: 0 use_difficult_gt: true do_neg_mining: true neg_pos_ratio: 3 neg_overlap: 0.5 code_type: CENTER_SIZE } }

weiliu89 / caffe

traing on Imagenet dataset with Check failed: data_ error #255

F1103 19:10:10.262428 11334 blob.cpp:115] Check failed: data_