Joint Detection and Identification Feature Learning for Person Search

Last update: Dec 17, 2022

Related tags

Overview

Person Search Project

This repository hosts the code for our paper Joint Detection and Identification Feature Learning for Person Search. The code is modified from the py-faster-rcnn written by Ross Girshick.

Request the dataset from lishuang[at]mit.edu or tong.xiao.work[at]gmail.com (academic only).
Due to licensing issues, please send us your request using your university email.

Installation

Clone this repo recursively

git clone --recursive https://github.com/ShuangLI59/person_search.git

Build Caffe with python layers and interface

We modified caffe based on Yuanjun's fork, which supports multi-gpu and memory optimization.

Apart from the official installation prerequisites, we have several other dependencies:

cudnn-v5.1
1.7.4 < openmpi < 2.0.0
boost >= 1.55 (A tip for Ubuntu 14.04: sudo apt-get autoremove libboost1.54* then sudo apt-get install libboost1.55-all-dev)

Then compile and install the caffe with

cd caffe
mkdir build && cd build
cmake .. -DUSE_MPI=ON -DCUDNN_INCLUDE=/path/to/cudnn/include -DCUDNN_LIBRARY=/path/to/cudnn/lib64/libcudnn.so
make -j8 && make install
cd ../..

Please refer to this page for detailed installation instructions and troubleshooting.

Build the Cython modules

Install some Python packages you might not have: Cython, python-opencv, easydict (>=1.6), PyYAML, protobuf, mpi4py. Then

cd lib && make && cd ..

Demo

Download our trained model to output/psdb_train/resnet50/, then

python2 tools/demo.py --gpu 0

Or you can use CPU only by setting --gpu -1.

Experiments

Request the dataset from sli [at] mit.edu or tong.xiao.work[at]gmail.com (academic only). Then

experiments/scripts/prepare_data.sh /path/to/the/downloaded/dataset.zip

Download an ImageNet pretrained ResNet-50 model to data/imagenet_models.
Training with GPU=0

experiments/scripts/train.sh 0 --set EXP_DIR resnet50

It will finish in around 18 hours, or you may directly download a trained model to output/psdb_train/resnet50/

Evaluation

By default we use 8 GPUs for faster evaluation. Please adjust the experiments/scripts/eval_test.sh with your hardware settings. For example, to use only one GPU, remove the mpirun -n 8 in L14 and change L16 to --gpu 0.
```
experiments/scripts/eval_test.sh resnet50 50000 resnet50
```
The result should be around
```
search ranking:
  mAP = 75.47%
  top- 1 = 78.62%
  top- 5 = 90.24%
  top-10 = 92.38%
```
Visualization

The evaluation will also produce a json file output/psdb_test/resnet50/resnet50_iter_50000/results.json for visualization. Just copy it to vis/ and run python2 -m SimpleHTTPServer. Then open a browser and go to http://localhost:8000/vis.

Citation

@inproceedings{xiaoli2017joint,
  title={Joint Detection and Identification Feature Learning for Person Search},
  author={Xiao, Tong and Li, Shuang and Wang, Bochao and Lin, Liang and Wang, Xiaogang},
  booktitle={CVPR},
  year={2017}
}

Repo History

The first version of our paper was published in 2016. We have made substantial improvements since then and published a new version of paper in 2017. The original code was moved to branch v1 and the new code has been merged to master. If you have checked out our code before, please be careful on this and we recommend clone recursively into a new repo instead.

Comments

ERROR : Run demo.py --gpu 0

Spec : cuda8.0 cudnn5.1

Errors occured below

cudnn_cov_lay.cu:33] check failed: status == CUDNN_STATUS_SUCCESS( 5 vs. 0 ) CUDN_STATUS_INVALID_VALUE

how can i run demo.py by gpu

opened by jhj7905 30
Stuck at demo_detect

Hi there~

I got a problem here running tools/demo.py. The script stuck at demo_detect(net, query_img) tracing back to blobs_out = net.forward(**forward_kwargs) and self._forward(start_ind, end_ind)

There is no other information printed while the python process keeps running(checked by top). This problem won't appear when running on CPU(--gpu -1).

The CUDA version is 8.0. Didn't compile with cudnn 'cuz there would be a Check failed: status == CUDNN_STATUS_SUCCESS (8 vs. 0) CUDNN_STATUS_EXECUTION_FAILED issue when running this script.

Thank you.

opened by dichen-cd 15
compile the caffe

hello,when I run the make -j8 && make install，it shows the error following:

[ 87%] [ 88%] make[2]: *** No rule to make target /path/to/cudnn/lib64/libcudnn.so', needed bylib/libcaffe.so'. Stop. make[2]: *** Waiting for unfinished jobs.... Building CXX object src/caffe/CMakeFiles/caffe.dir/data_transformer.cpp.o Building CXX object src/caffe/CMakeFiles/caffe.dir/syncedmem.cpp.o make[1]: *** [src/caffe/CMakeFiles/caffe.dir/all] Error 2 make: *** [all] Error 2

I wonder if it is the error of the path? Another question: Can I unuse the "cudnn"and "openmpi"?I only have one server but have 4 gpus，Ｉwonder if I can use the "-gpu al" to replace the "openmpi". thanks!

opened by Jcdidi 15
I can't run Ur code correctly. [id_loss = 87.3365 (* 1 = 87.3365 loss)]

I also get the same problem:

I0430 00:03:16.667212 32035 solver.cpp:240] Iteration 0, loss = 45.2935 I0430 00:03:16.667526 32035 solver.cpp:255] Train net output #0: det_accuracy = 0.03125 I0430 00:03:16.667642 32035 solver.cpp:255] Train net output #1: det_loss = 0.693147 (* 1 = 0.693147 loss) I0430 00:03:16.667735 32035 solver.cpp:255] Train net output #2: id_accuracy = 0 I0430 00:03:16.667831 32035 solver.cpp:255] Train net output #3: id_loss = 87.3365 (* 1 = 87.3365 loss) I0430 00:03:16.667927 32035 solver.cpp:255] Train net output #4: loss_bbox = 0 (* 1 = 0 loss) I0430 00:03:16.668015 32035 solver.cpp:255] Train net output #5: rpn_bbox_loss = 0.472784 (* 1 = 0.472784 loss) I0430 00:03:16.668109 32035 solver.cpp:255] Train net output #6: rpn_cls_loss = 0.693147 (* 1 = 0.693147 loss) I0430 00:03:16.668198 32035 solver.cpp:640] Iteration 0, lr = 0.001 I0430 00:04:09.829497 32035 solver.cpp:240] Iteration 20, loss = nan I0430 00:04:09.829540 32035 solver.cpp:255] Train net output #0: det_accuracy = 0.929688 I0430 00:04:09.829557 32035 solver.cpp:255] Train net output #1: det_loss = 0.618617 (* 1 = 0.618617 loss) I0430 00:04:09.829567 32035 solver.cpp:255] Train net output #2: id_accuracy = -nan I0430 00:04:09.829576 32035 solver.cpp:255] Train net output #3: id_loss = 0 (* 1 = 0 loss) I0430 00:04:09.829586 32035 solver.cpp:255] Train net output #4: loss_bbox = nan (* 1 = nan loss) I0430 00:04:09.829603 32035 solver.cpp:255] Train net output #5: rpn_bbox_loss = 0.0646796 (* 1 = 0.0646796 loss) I0430 00:04:09.829617 32035 solver.cpp:255] Train net output #6: rpn_cls_loss = 0.679301 (* 1 = 0.679301 loss) I0430 00:04:09.829638 32035 solver.cpp:640] Iteration 20, lr = 0.001

I have tried set: RNG_SEED = 1 or 2, but I still can not solve it. P.S: I didn't modify any code.

opened by ghost 14
Caffe compile error

[ 83%] Building CXX object src/caffe/CMakeFiles/caffe.dir/util/channel.cpp.o [ 83%] Building CXX object src/caffe/CMakeFiles/caffe.dir/util/cudnn.cpp.o [ 84%] Building CXX object src/caffe/CMakeFiles/caffe.dir/util/db.cpp.o [ 84%] Building CXX object src/caffe/CMakeFiles/caffe.dir/util/db_leveldb.cpp.o [ 86%] Building CXX object src/caffe/CMakeFiles/caffe.dir/util/db_lmdb.cpp.o [ 86%] Building CXX object src/caffe/CMakeFiles/caffe.dir/util/im2col.cpp.o [ 87%] Building CXX object src/caffe/CMakeFiles/caffe.dir/util/insert_splits.cpp.o [ 87%] Building CXX object src/caffe/CMakeFiles/caffe.dir/util/io.cpp.o [ 87%] Building CXX object src/caffe/CMakeFiles/caffe.dir/util/math_functions.cpp.o [ 88%] Building CXX object src/caffe/CMakeFiles/caffe.dir/util/mpi_functions.cpp.o [ 88%] make[2]: *** No rule to make target /path/to/cudnn/lib64/libcudnn.so', needed bylib/libcaffe.so'. Stop. make[2]: *** Waiting for unfinished jobs.... Building CXX object src/caffe/CMakeFiles/caffe.dir/util/upgrade_proto.cpp.o make[1]: *** [src/caffe/CMakeFiles/caffe.dir/all] Error 2 make: *** [all] Error 2

opened by foqin 13
Why I got the nan loss_bbox when i train and eval?

when i train the model ,the loss_bbox is after iteration 20 I0411 17:16:45.481537 19413 solver.cpp:240] Iteration 0, loss = 89.1661 I0411 17:16:45.481566 19413 solver.cpp:255] Train net output #0: det_accuracy = 0.25 I0411 17:16:45.481573 19413 solver.cpp:255] Train net output #1: det_loss = 0.693147 (* 1 = 0.693147 loss) I0411 17:16:45.481577 19413 solver.cpp:255] Train net output #2: id_accuracy = 0 I0411 17:16:45.481581 19413 solver.cpp:255] Train net output #3: id_loss = 87.3365 (* 1 = 87.3365 loss) I0411 17:16:45.481586 19413 solver.cpp:255] Train net output #4: loss_bbox = 0.646189 (* 1 = 0.646189 loss) I0411 17:16:45.481590 19413 solver.cpp:255] Train net output #5: rpn_bbox_loss = 0.0912708 (* 1 = 0.0912708 loss) I0411 17:16:45.481595 19413 solver.cpp:255] Train net output #6: rpn_cls_loss = 0.693147 (* 1 = 0.693147 loss) I0411 17:16:45.481600 19413 solver.cpp:640] Iteration 0, lr = 0.001

I0411 17:17:08.716578 19413 solver.cpp:240] Iteration 20, loss = nan I0411 17:17:08.716603 19413 solver.cpp:255] Train net output #0: det_accuracy = 0.898438 I0411 17:17:08.716612 19413 solver.cpp:255] Train net output #1: det_loss = 0.628226 (* 1 = 0.628226 loss) I0411 17:17:08.716616 19413 solver.cpp:255] Train net output #2: id_accuracy = 0 I0411 17:17:08.716621 19413 solver.cpp:255] Train net output #3: id_loss = 87.3365 (* 1 = 87.3365 loss) I0411 17:17:08.716625 19413 solver.cpp:255] Train net output #4: loss_bbox = nan (* 1 = nan loss) I0411 17:17:08.716629 19413 solver.cpp:255] Train net output #5: rpn_bbox_loss = 0.266092 (* 1 = 0.266092 loss) I0411 17:17:08.716634 19413 solver.cpp:255] Train net output #6: rpn_cls_loss = 0.684319 (* 1 = 0.684319 loss) I0411 17:17:08.716639 19413 solver.cpp:640] Iteration 20, lr = 0.001

when i run experiments/scripts/eval_test.sh resnet50 50000 resnet50 there are errors. /lib/datasets/psdb.py" lines 150 for gt, det in zip(gt_roidb, gallery_det):

det=[[ nan nan nan nan 0.1167134] [ nan nan nan nan 0.1167134] [ nan nan nan nan 0.1167134] ..., [ nan nan nan nan 0.1167134] [ nan nan nan nan 0.1167134] [ nan nan nan nan 0.1167134]] ([], [])

opened by andongchen 12
The MPI_Comm_rank() function was called before MPI_INIT was invoked.
Hi, I tried to run the training but face the following error. It complaint the MPI_Comm_rank() function was called before MPI_INIT was invoked.

ubuntu@ubuntu-S2600CW:~/Downloads/person_search$ experiments/scripts/train.sh 0 --set EXP_DIR resnet50

set -e

export PYTHONUNBUFFERED=True

PYTHONUNBUFFERED=True

GPU_ID=0

NET=resnet50

DATASET=psdb

array=($@)

len=4

EXTRA_ARGS='--set EXP_DIR resnet50'

EXTRA_ARGS_SLUG=--set_EXP_DIR_resnet50

case $DATASET in

TRAIN_IMDB=psdb_train

TEST_IMDB=psdb_test

PT_DIR=psdb

ITERS=50000 ++ date +%Y-%m-%d_%H-%M-%S

LOG=experiments/logs/psdb_train_resnet50_--set_EXP_DIR_resnet50.txt.2017-02-28_19-27-37

exec ++ tee -a experiments/logs/psdb_train_resnet50_--set_EXP_DIR_resnet50.txt.2017-02-28_19-27-37

echo Logging output to experiments/logs/psdb_train_resnet50_--set_EXP_DIR_resnet50.txt.2017-02-28_19-27-37 Logging output to experiments/logs/psdb_train_resnet50_--set_EXP_DIR_resnet50.txt.2017-02-28_19-27-37

python2 tools/train_net.py --gpu 0 --solver models/psdb/resnet50/solver.prototxt --weights data/imagenet_models/resnet50.caffemodel --imdb psdb_train --iters 50000 --cfg experiments/cfgs/resnet50.yml --rand --set EXP_DIR resnet50 Called with args: Namespace(cfg_file='experiments/cfgs/resnet50.yml', gpu='0', imdb_name='psdb_train', max_iters=50000, pretrained_model='data/imagenet_models/resnet50.caffemodel', previous_state=None, randomize=True, set_cfgs=['EXP_DIR', 'resnet50'], solver='models/psdb/resnet50/solver.prototxt') Using config: {'DATA_DIR': '/home/ubuntu/Downloads/person_search/data', 'DEDUP_BOXES': 0.0625, 'EPS': 1e-14, 'EXP_DIR': 'resnet50', 'GPU_ID': 0, 'MATLAB': 'matlab', 'MODELS_DIR': '/home/ubuntu/Downloads/person_search/models/pascal_voc', 'PIXEL_MEANS': array([[[ 102.9801, 115.9465, 122.7717]]]), 'RNG_SEED': 3, 'ROOT_DIR': '/home/ubuntu/Downloads/person_search', 'TEST': {'BBOX_REG': True, 'HAS_RPN': True, 'MAX_SIZE': 1000, 'NMS': 0.4, 'PROPOSAL_METHOD': 'selective_search', 'RPN_MIN_SIZE': 16, 'RPN_NMS_THRESH': 0.7, 'RPN_POST_NMS_TOP_N': 300, 'RPN_PRE_NMS_TOP_N': 6000, 'SCALES': [600], 'SVM': False}, 'TRAIN': {'ASPECT_GROUPING': True, 'BATCH_SIZE': 128, 'BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0], 'BBOX_NORMALIZE_MEANS': [0.0, 0.0, 0.0, 0.0], 'BBOX_NORMALIZE_STDS': [0.1, 0.1, 0.2, 0.2], 'BBOX_NORMALIZE_TARGETS': True, 'BBOX_NORMALIZE_TARGETS_PRECOMPUTED': True, 'BBOX_REG': True, 'BBOX_THRESH': 0.5, 'BG_THRESH_HI': 0.5, 'BG_THRESH_LO': 0.0, 'FG_FRACTION': 0.25, 'FG_THRESH': 0.5, 'HAS_RPN': True, 'IMS_PER_BATCH': 1, 'MAX_SIZE': 1000, 'PROPOSAL_METHOD': 'gt', 'RPN_BATCHSIZE': 256, 'RPN_BBOX_INSIDE_WEIGHTS': [1.0, 1.0, 1.0, 1.0], 'RPN_CLOBBER_POSITIVES': False, 'RPN_FG_FRACTION': 0.5, 'RPN_MIN_SIZE': 16, 'RPN_NEGATIVE_OVERLAP': 0.3, 'RPN_NMS_THRESH': 0.7, 'RPN_POSITIVE_OVERLAP': 0.7, 'RPN_POSITIVE_WEIGHT': -1.0, 'RPN_POST_NMS_TOP_N': 2000, 'RPN_PRE_NMS_TOP_N': 12000, 'SCALES': [600], 'SNAPSHOT_INFIX': '', 'SNAPSHOT_ITERS': 10000, 'USE_FLIPPED': True, 'USE_PREFETCH': False}, 'USE_GPU_NMS': True} *** The MPI_Comm_rank() function was called before MPI_INIT was invoked. *** This is disallowed by the MPI standard. *** Your MPI job will now abort.
opened by yclai1114 11
Error with demo.py

when I ran python2 tools/demo.py --gpu 0, the following error was encountered. I1014 13:40:42.606418 19932 net.cpp:1256] blob 159 name det_score data idx 0 I1014 13:40:42.606422 19932 net.cpp:1266] blob 159 name det_score diff idx 2 I1014 13:40:42.606426 19932 net.cpp:1256] blob 160 name pid_prob data idx -1 I1014 13:40:42.606431 19932 net.cpp:1266] blob 160 name pid_prob diff idx -1 I1014 13:40:42.606433 19932 net.cpp:1256] blob 161 name bbox_pred data idx -1 I1014 13:40:42.606437 19932 net.cpp:1266] blob 161 name bbox_pred diff idx -1 I1014 13:40:42.606442 19932 net.cpp:1256] blob 162 name feat_lowdim data idx 0 I1014 13:40:42.606446 19932 net.cpp:1266] blob 162 name feat_lowdim diff idx 0 I1014 13:40:42.606451 19932 net.cpp:1256] blob 163 name feat data idx -1 I1014 13:40:42.606454 19932 net.cpp:1266] blob 163 name feat diff idx -1 I1014 13:40:42.606457 19932 net.cpp:1272] storage memory slot 0 size 3211264 I1014 13:40:42.606462 19932 net.cpp:1272] storage memory slot 1 size 3211264 I1014 13:40:42.606465 19932 net.cpp:1272] storage memory slot 2 size 3211264 I1014 13:40:42.606468 19932 net.cpp:1272] storage memory slot 3 size 8192 I1014 13:40:42.606472 19932 net.cpp:1277] raw memory 304337048 opt memory 10848392 Init end Traceback (most recent call last): File "tools/demo.py", line 114, in main(args) File "tools/demo.py", line 42, in main net = caffe.Net(args.probe_def, args.caffemodel, caffe.TEST) RuntimeError: basic_string::append

please help me solve this problem。Thanks！

opened by gitrobin 10
stuck at the make process

Hello Xiao I met a problem when I run make -j8 && make install at the terminal, it stuck and showed the following problem .../person_search/caffe/src/caffe/data_transformer.cpp:438:51: error: 'CV_INTER_NN' was not declared in this scope .../person_search/caffe/src/caffe/data_transformer.cpp: In member function 'void caffe::DataTransformer<Dtype>::Transform(const cv::Mat&, caffe::Blob<Dtype>*)': .../data_transformer.cpp:681:84: error: 'CV_INTER_CUBIC' was not declared in this scope make[2]: *** [src/caffe/CMakeFiles/caffe.dir/data_transformer.cpp.o] Error 1 make[2]: *** waiting for unfinished jobs.... make[2]: *** [src/caffe/CMakeFiles/caffe.dir/all] Error 2 make: ***[all] Error 2 I don't know if it's because there are two caffe on the server I run, but the caffe is put under the file'person_search', I have installed cudnn v5.1 and openmpi 1.8.8 . But I didn't modify the caffe after downloading it. Or is it becuase some packages I haven't installed?

opened by kanikel 9
Fail to compile caffe

\u2018 and \u2019 means ' ' \u2018 and \u2019是引号，复制过来的时候显示错误 CXX src/caffe/common.cpp src/caffe/common.cpp:146:1: error: ’Caffe does not name a type Caffe::Caffe() ^ src/caffe/common.cpp:177:1: error: Caffe does not name a type Caffe::~Caffe() { ^ src/caffe/common.cpp:184:6: error: \u2018Caffe\u2019 has not been declared void Caffe::set_random_seed(const unsigned int seed) { ^ src/caffe/common.cpp: In function \u2018void set_random_seed(unsigned int)\u2019: src/caffe/common.cpp:187:11: error: \u2018Get\u2019 was not declared in this scope if (Get().curand_generator_) { ^ In file included from ./include/caffe/common.hpp:34:0, from src/caffe/common.cpp:5: src/caffe/common.cpp:188:70: error: \u2018curand_generator\u2019 was not declared in this scope CURAND_CHECK(curandSetPseudoRandomGeneratorSeed(curand_generator(), ^ ./include/caffe/util/device_alternate.hpp:64:29: note: in definition of macro \u2018CURAND_CHECK\u2019 curandStatus_t status = condition;
^ src/caffe/common.cpp:190:60: error: \u2018curand_generator\u2019 was not declared in this scope CURAND_CHECK(curandSetGeneratorOffset(curand_generator(), 0)); ^ ./include/caffe/util/device_alternate.hpp:64:29: note: in definition of macro \u2018CURAND_CHECK\u2019 curandStatus_t status = condition;
^ src/caffe/common.cpp:199:7: error: \u2018Get\u2019 was not declared in this scope Get().random_generator_.reset(new RNG(seed)); ^ src/caffe/common.cpp:199:37: error: expected type-specifier before \u2018RNG\u2019 Get().random_generator_.reset(new RNG(seed)); ^ src/caffe/common.cpp: At global scope: src/caffe/common.cpp:202:6: error: \u2018Caffe\u2019 has not been declared void Caffe::SetDevice(const int device_id) { ^ src/caffe/common.cpp: In function \u2018void SetDevice(int)\u2019: src/caffe/common.cpp:206:42: error: \u2018Get\u2019 was not declared in this scope if (current_device == device_id && Get().cublas_handle_ && Get().curand_generator_) { ^ src/caffe/common.cpp:212:11: error: \u2018Get\u2019 was not declared in this scope if (Get().cublas_handle_) CUBLAS_CHECK(cublasDestroy(Get().cublas_handle_)); ^ src/caffe/common.cpp:213:11: error: \u2018Get\u2019 was not declared in this scope if (Get().curand_generator_) { ^ In file included from ./include/caffe/common.hpp:34:0, from src/caffe/common.cpp:5: src/caffe/common.cpp:216:34: error: \u2018Get\u2019 was not declared in this scope CUBLAS_CHECK(cublasCreate(&Get().cublas_handle_)); ^ ./include/caffe/util/device_alternate.hpp:57:29: note: in definition of macro \u2018CUBLAS_CHECK\u2019 cublasStatus_t status = condition;
^ src/caffe/common.cpp:217:43: error: \u2018Get\u2019 was not declared in this scope CURAND_CHECK(curandCreateGenerator(&Get().curand_generator_, ^ ./include/caffe/util/device_alternate.hpp:64:29: note: in definition of macro \u2018CURAND_CHECK\u2019 curandStatus_t status = condition;
^ src/caffe/common.cpp:219:55: error: \u2018Get\u2019 was not declared in this scope CURAND_CHECK(curandSetPseudoRandomGeneratorSeed(Get().curand_generator_, ^ ./include/caffe/util/device_alternate.hpp:64:29: note: in definition of macro \u2018CURAND_CHECK\u2019 curandStatus_t status = condition;
^ src/caffe/common.cpp:220:23: error: \u2018cluster_seedgen\u2019 was not declared in this scope cluster_seedgen())); ^ ./include/caffe/util/device_alternate.hpp:64:29: note: in definition of macro \u2018CURAND_CHECK\u2019 curandStatus_t status = condition;
^ src/caffe/common.cpp:220:23: note: suggested alternative: cluster_seedgen())); ^ ./include/caffe/util/device_alternate.hpp:64:29: note: in definition of macro \u2018CURAND_CHECK\u2019 curandStatus_t status = condition;
^ src/caffe/common.cpp:13:9: note: \u2018caffe::cluster_seedgen\u2019 int64_t cluster_seedgen(bool sync) { ^ src/caffe/common.cpp: At global scope: src/caffe/common.cpp:226:6: error: \u2018Caffe\u2019 has not been declared void Caffe::DeviceQuery() { ^ src/caffe/common.cpp:262:7: error: \u2018Caffe\u2019 has not been declared class Caffe::RNG::Generator { ^ src/caffe/common.cpp:262:29: error: expected unqualified-id before \u2018{\u2019 token class Caffe::RNG::Generator { ^ make: *** [.build_release/src/caffe/common.o] Error 1

opened by 22wei22 9
Have troble making Caffe in the project

Thank you for your work! I want to try this demo on the server，but I cannot build the caffe. I encountered an error when the step "make -j8 && make install"is performed, and the error information is "../lib/libcaffe.so: undefined reference to google::protobuf::io::CodedInputStream::ReadTagFallback(unsigned int)' ../lib/libcaffe.so: undefined reference togoogle::protobuf::internal::MergeFromFail(char const*, int)' ../lib/libcaffe.so: undefined reference to google::protobuf::internal::RepeatedPtrFieldBase::InternalExtend(int)' ../lib/libcaffe.so: undefined reference togoogle::protobuf::internal::ArenaStringPtr::AssignWithDefault(std::string const*, google::protobuf::internal::ArenaStringPtr)' collect2: error: ld returned 1 exit status make[2]: *** [examples/cpp_classification/classification] Error 1 make[1]: *** [examples/CMakeFiles/classification.dir/all] Error 2 make: *** [all] Error 2" Hope you can provide some help. Thank you!

opened by milanyummy 9
cuda 8.0 and cudnn v5.1

when i setting environment for tranning: My ubuntu version is 20.04 , but requirement cuda 8.0 + cudnn v5.1 available in ubuntu 16.04 so how to fix it? thank you so much!

opened by anhnhust 0
Caffe Installation Issue on GPU GTX 1050 Ubuntu 18.04

I have GTX 1050 but getting errors again and again in Caffe installation

src/caffe/CMakeFiles/caffe.dir/build.make:257: recipe for target 'src/caffe/CMakeFiles/caffe.dir/layers/contrastive_loss_layer.cpp.o' failed make[2]: *** [src/caffe/CMakeFiles/caffe.dir/layers/contrastive_loss_layer.cpp.o] Error 1 CMakeFiles/Makefile2:208: recipe for target 'src/caffe/CMakeFiles/caffe.dir/all' failed make[1]: *** [src/caffe/CMakeFiles/caffe.dir/all] Error 2 Makefile:129: recipe for target 'all' failed make: *** [all] Error 2

opened by khawar-islam 2
about dataset

hello ,are you in China ?I 'am in Jiangsu Provence ,recently ,I can't download the dataset of this paper ,could you send me the dataset ?thanks a lot . my mail address is [email protected].

opened by geloveyou 0
About the log file of oim loss

Hi, I'm training oim loss with resnet50, with batchsize of 1, does it matter with the different batchsize? Moreover, what is the value that oim loss could converge to? I am struggling with a high oimloss...

opened by zhongyingji 0

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