Caffe Pretrained Models can download from
Link:http://pan.baidu.com/s/1pL0ZIYJ
Password: xp68
snailtyan / caffe-model-zoo Goto Github PK
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caffe pretrained models and prototxt
Caffe Pretrained Models can download from
Link:http://pan.baidu.com/s/1pL0ZIYJ
Password: xp68
prototxt中最后的innerproduct层为my-classifier 但是caffemodel中却是原版的fc1000
Hi,
I want to ask that are your models all trained on Imagenet data? as I have checked the caffe inception v3 prototxt and it has output size 3. for imagenet it should be 1000. I just want to be sure about it..
I am also getting error while using the model for training. error states as:
math_functions.cu:79] Check failed: error == cudaSuccess (74 vs. 0) misaligned address
*** Check failure stack trace: ***
@ 0x7f4eb42265cd google::LogMessage::Fail()
@ 0x7f4eb4228433 google::LogMessage::SendToLog()
@ 0x7f4eb422615b google::LogMessage::Flush()
@ 0x7f4eb4228e1e google::LogMessageFatal::~LogMessageFatal()
@ 0x7f4eb4a23cba caffe::caffe_gpu_memcpy()
@ 0x7f4eb4a0536d caffe::SyncedMemory::gpu_data()
@ 0x7f4eb484e7b2 caffe::Blob<>::gpu_data()
@ 0x7f4eb4a71130 caffe::BiasLayer<>::Backward_gpu()
@ 0x7f4eb4a3a519 caffe::ScaleLayer<>::Backward_gpu()
@ 0x7f4eb49efde8 caffe::Net<>::BackwardFromTo()
@ 0x7f4eb49efe3f caffe::Net<>::Backward()
@ 0x7f4eb486f384 caffe::Solver<>::Step()
@ 0x7f4eb486ff1a caffe::Solver<>::Solve()
@ 0x40e57b train()
@ 0x40a6fd main
@ 0x7f4eb29bc830 __libc_start_main
@ 0x40b159 _start
@ (nil) (unknown)
Thanks in advance
http://pan.baidu.com/s/1pL0ZIYJ
Link does not work?Where can I get pre trained weights?
你好,我想问一下VGG16和VGG19里的my-fc8是什么,我用两个网络跑出的概率值都是[0.3334,0.3334,0.3334],sofmax之前my-fc8的输出都是[0,0,0]
When in train_val.prototxt
, use_global_stats
should be set false
, but in deploy.prototxt
, use_global_stats
should be set true
.
When training models, sometimes loss would stay stable. For example:
I0705 14:57:14.980687 320 solver.cpp:218] Iteration 44 (2.60643 iter/s, 0.383667s/1 iters), loss = 0.263664
I0705 14:57:14.980741 320 solver.cpp:237] Train net output #0: loss1/loss1 = 0.878881 (* 0.3 = 0.263664 loss)
I0705 14:57:14.980756 320 sgd_solver.cpp:105] Iteration 44, lr = 0.000956
I0705 14:57:15.365164 320 solver.cpp:218] Iteration 45 (2.60146 iter/s, 0.3844s/1 iters), loss = 20.7475
I0705 14:57:15.365226 320 solver.cpp:237] Train net output #0: loss1/loss1 = 69.1584 (* 0.3 = 20.7475 loss)
I0705 14:57:15.365243 320 sgd_solver.cpp:105] Iteration 45, lr = 0.000955
I0705 14:57:15.759548 320 solver.cpp:218] Iteration 46 (2.53612 iter/s, 0.394303s/1 iters), loss = 0
I0705 14:57:15.759609 320 solver.cpp:237] Train net output #0: loss1/loss1 = 0 (* 0.3 = 0 loss)
I0705 14:57:15.759624 320 sgd_solver.cpp:105] Iteration 46, lr = 0.000954
I0705 14:57:16.158644 320 solver.cpp:218] Iteration 47 (2.50621 iter/s, 0.39901s/1 iters), loss = 1.63756
I0705 14:57:16.158696 320 solver.cpp:237] Train net output #0: loss1/loss1 = 5.45853 (* 0.3 = 1.63756 loss)
I0705 14:57:16.158715 320 sgd_solver.cpp:105] Iteration 47, lr = 0.000953
I0705 14:57:16.546782 320 solver.cpp:218] Iteration 48 (2.57693 iter/s, 0.388058s/1 iters), loss = 3.27512
I0705 14:57:16.546838 320 solver.cpp:237] Train net output #0: loss1/loss1 = 10.9171 (* 0.3 = 3.27512 loss)
I0705 14:57:16.546855 320 sgd_solver.cpp:105] Iteration 48, lr = 0.000952
I0705 14:57:16.930493 320 solver.cpp:218] Iteration 49 (2.60667 iter/s, 0.383631s/1 iters), loss = 25.3822
I0705 14:57:16.930553 320 solver.cpp:237] Train net output #0: loss1/loss1 = 84.6073 (* 0.3 = 25.3822 loss)
I0705 14:57:16.930568 320 sgd_solver.cpp:105] Iteration 49, lr = 0.000951
I0705 14:57:17.314102 320 solver.cpp:218] Iteration 50 (2.60741 iter/s, 0.383522s/1 iters), loss = 26.201
I0705 14:57:17.314185 320 solver.cpp:237] Train net output #0: loss1/loss1 = 87.3365 (* 0.3 = 26.201 loss)
我以前在用googlenet(v1)训练时,是直接用的图像(ImageData),没有转LMDB。
name: "GoogleNet"
layer {
name: "data"
type: "ImageData"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 224
mean_value: 104
mean_value: 117
mean_value: 123
}
image_data_param {
source: "series_train_file_list.txt"
batch_size: 32
new_height: 256
new_width: 256
shuffle: true
}
}
想问一下如果用v4训练,仍然用ImageData,crop_size设为299的话,new_width和new_height应该设为多大?
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