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- convert_mnist_data.cpp
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- lenet.prototxt
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- train_lenet.sh
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- bvlc_caffenet_full_conv.prototxt
- conv.prototxt
- pascal_multilabel_datalayers.py
- pyloss.py
- caffenet.py
- linreg.prototxt
- tools.py
- convert_mnist_siamese_data.cpp
- create_mnist_siamese.sh
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- CMakeLists.txt
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- absval_layer.hpp
- accuracy_layer.hpp
- argmax_layer.hpp
- base_conv_layer.hpp
- base_data_layer.hpp
- batch_norm_layer.hpp
- batch_reindex_layer.hpp
- bias_layer.hpp
- bn_layer.hpp
- bnll_layer.hpp
- concat_layer.hpp
- contrastive_loss_layer.hpp
- conv_layer.hpp
- crop_layer.hpp
- cudnn_conv_layer.hpp
- cudnn_deconv_layer.hpp
- cudnn_lcn_layer.hpp
- cudnn_lrn_layer.hpp
- cudnn_pooling_layer.hpp
- cudnn_relu_layer.hpp
- cudnn_sigmoid_layer.hpp
- cudnn_softmax_layer.hpp
- cudnn_tanh_layer.hpp
- data_layer.hpp
- deconv_layer.hpp
- dropout_layer.hpp
- dummy_data_layer.hpp
- eltwise_layer.hpp
- elu_layer.hpp
- embed_layer.hpp
- euclidean_loss_layer.hpp
- exp_layer.hpp
- filter_layer.hpp
- flatten_layer.hpp
- hdf5_data_layer.hpp
- hdf5_output_layer.hpp
- hinge_loss_layer.hpp
- im2col_layer.hpp
- image_data_layer.hpp
- infogain_loss_layer.hpp
- inner_product_layer.hpp
- input_layer.hpp
- interp_layer.hpp
- log_layer.hpp
- loss_layer.hpp
- lrn_layer.hpp
- lstm_layer.hpp
- memory_data_layer.hpp
- multinomial_logistic_loss_layer.hpp
- mvn_layer.hpp
- neuron_layer.hpp
- parameter_layer.hpp
- pooling_layer.hpp
- power_layer.hpp
- prelu_layer.hpp
- python_layer.hpp
- recurrent_layer.hpp
- reduction_layer.hpp
- relu_layer.hpp
- reshape_layer.hpp
- rnn_layer.hpp
- scale_layer.hpp
- sigmoid_cross_entropy_loss_layer.hpp
- sigmoid_layer.hpp
- silence_layer.hpp
- slice_layer.hpp
- softmax_layer.hpp
- softmax_loss_layer.hpp
- split_layer.hpp
- spp_layer.hpp
- swish_layer.hpp
- tanh_layer.hpp
- threshold_layer.hpp
- tile_layer.hpp
- window_data_layer.hpp
- test_caffe_main.hpp
- test_gradient_check_util.hpp
- benchmark.hpp
- blocking_queue.hpp
- cudnn.hpp
- db.hpp
- db_leveldb.hpp
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- device_alternate.hpp
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- gpu_util.cuh
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- insert_splits.hpp
- interp.hpp
- io.hpp
- math_functions.hpp
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- rng.hpp
- signal_handler.h
- upgrade_proto.hpp
- blob.hpp
- caffe.hpp
- common.cuh
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- internal_thread.hpp
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- ilsvrc_2012_mean.mat
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- ordinal_decode_layer.py
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- absval_layer.cpp
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- accuracy_layer.cu
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- cudnn_pooling_layer.cpp
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- cudnn_relu_layer.cpp
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- cudnn_sigmoid_layer.cpp
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- cudnn_softmax_layer.cpp
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- cudnn_tanh_layer.cpp
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- data_layer.cpp
- deconv_layer.cpp
- deconv_layer.cu
- dropout_layer.cpp
- dropout_layer.cu
- dummy_data_layer.cpp
- eltwise_layer.cpp
- eltwise_layer.cu
- elu_layer.cpp
- elu_layer.cu
- embed_layer.cpp
- embed_layer.cu
- euclidean_loss_layer.cpp
- euclidean_loss_layer.cu
- exp_layer.cpp
- exp_layer.cu
- filter_layer.cpp
- filter_layer.cu
- flatten_layer.cpp
- hdf5_data_layer.cpp
- hdf5_data_layer.cu
- hdf5_output_layer.cpp
- hdf5_output_layer.cu
- hinge_loss_layer.cpp
- im2col_layer.cpp
- im2col_layer.cu
- image_data_layer.cpp
- infogain_loss_layer.cpp
- inner_product_layer.cpp
- inner_product_layer.cu
- input_layer.cpp
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- log_layer.cpp
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- lstm_layer.cpp
- lstm_unit_layer.cpp
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- memory_data_layer.cpp
- multinomial_logistic_loss_layer.cpp
- mvn_layer.cpp
- mvn_layer.cu
- neuron_layer.cpp
- parameter_layer.cpp
- pooling_layer.cpp
- pooling_layer.cu
- power_layer.cpp
- power_layer.cu
- prelu_layer.cpp
- prelu_layer.cu
- recurrent_layer.cpp
- recurrent_layer.cu
- reduction_layer.cpp
- reduction_layer.cu
- relu_layer.cpp
- relu_layer.cu
- reshape_layer.cpp
- rnn_layer.cpp
- scale_layer.cpp
- scale_layer.cu
- sigmoid_cross_entropy_loss_layer.cpp
- sigmoid_cross_entropy_loss_layer.cu
- sigmoid_layer.cpp
- sigmoid_layer.cu
- silence_layer.cpp
- silence_layer.cu
- slice_layer.cpp
- slice_layer.cu
- softmax_layer.cpp
- softmax_layer.cu
- softmax_loss_layer.cpp
- softmax_loss_layer.cu
- split_layer.cpp
- split_layer.cu
- spp_layer.cpp
- swish_layer.cpp
- swish_layer.cu
- tanh_layer.cpp
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- caffe.proto
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- adagrad_solver.cpp
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- adam_solver.cpp
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- nesterov_solver.cpp
- nesterov_solver.cu
- rmsprop_solver.cpp
- rmsprop_solver.cu
- sgd_solver.cpp
- sgd_solver.cu
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- sample_data_list.txt
- solver_data.h5
- solver_data_list.txt
- CMakeLists.txt
- test_accuracy_layer.cpp
- test_argmax_layer.cpp
- test_batch_norm_layer.cpp
- test_batch_reindex_layer.cpp
- test_benchmark.cpp
- test_bias_layer.cpp
- test_blob.cpp
- test_caffe_main.cpp
- test_common.cpp
- test_concat_layer.cpp
- test_contrastive_loss_layer.cpp
- test_convolution_layer.cpp
- test_crop_layer.cpp
- test_data_layer.cpp
- test_data_transformer.cpp
- test_db.cpp
- test_deconvolution_layer.cpp
- test_dummy_data_layer.cpp
- test_eltwise_layer.cpp
- test_embed_layer.cpp
- test_euclidean_loss_layer.cpp
- test_filler.cpp
- test_filter_layer.cpp
- test_flatten_layer.cpp
- test_gradient_based_solver.cpp
- test_hdf5_output_layer.cpp
- test_hdf5data_layer.cpp
- test_hinge_loss_layer.cpp
- test_im2col_kernel.cu
- test_im2col_layer.cpp
- test_image_data_layer.cpp
- test_infogain_loss_layer.cpp
- test_inner_product_layer.cpp
- test_internal_thread.cpp
- test_io.cpp
- test_layer_factory.cpp
- test_lrn_layer.cpp
- test_lstm_layer.cpp
- test_math_functions.cpp
- test_maxpool_dropout_layers.cpp
- test_memory_data_layer.cpp
- test_multinomial_logistic_loss_layer.cpp
- test_mvn_layer.cpp
- test_net.cpp
- test_neuron_layer.cpp
- test_platform.cpp
- test_pooling_layer.cpp
- test_power_layer.cpp
- test_protobuf.cpp
- test_random_number_generator.cpp
- test_reduction_layer.cpp
- test_reshape_layer.cpp
- test_rnn_layer.cpp
- test_scale_layer.cpp
- test_sigmoid_cross_entropy_loss_layer.cpp
- test_slice_layer.cpp
- test_softmax_layer.cpp
- test_softmax_with_loss_layer.cpp
- test_solver.cpp
- test_solver_factory.cpp
- test_split_layer.cpp
- test_spp_layer.cpp
- test_stochastic_pooling.cpp
- test_syncedmem.cpp
- test_tanh_layer.cpp
- test_threshold_layer.cpp
- test_tile_layer.cpp
- test_upgrade_proto.cpp
- test_util_blas.cpp
- benchmark.cpp
- blocking_queue.cpp
- cudnn.cpp
- db.cpp
- db_leveldb.cpp
- db_lmdb.cpp
- hdf5.cpp
- im2col.cpp
- im2col.cu
- insert_splits.cpp
- interp.cpp
- interp.cu
- io.cpp
- math_functions.cpp
- math_functions.cu
- signal_handler.cpp
- upgrade_proto.cpp
- blob.cpp
- CMakeLists.txt
- common.cpp
- data_transformer.cpp
- internal_thread.cpp
- layer.cpp
- layer_factory.cpp
- net.cpp
- parallel.cpp
- solver.cpp
- syncedmem.cpp
- CMakeLists.txt
- gtest-all.cpp
- gtest.h
- gtest_main.cc
- extract_seconds.py
- launch_resize_and_crop_images.sh
- parse_log.py
- parse_log.sh
- plot_log.gnuplot.example
- plot_training_log.py.example
- resize_and_crop_images.py
- summarize.py
- caffe.cpp
- CMakeLists.txt
- compute_image_mean.cpp
- convert_imageset.cpp
- extract_features.cpp
- upgrade_net_proto_binary.cpp
- upgrade_net_proto_text.cpp
- upgrade_solver_proto_text.cpp
- .Doxyfile
- .travis.yml
- caffe.cloc
- CMakeLists.txt
- CONTRIBUTING.md
- CONTRIBUTORS.md
- INSTALL.md
- LICENSE
- Makefile
- Makefile.config.example
- README.md
- demo_01.png
- demo_01.png
- KITTI.jpeg
- ROB.jpeg
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- .DS_Store
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- .DS_Store
- deploy.prototxt
- .DS_Store
- demo_01_pred.png
- demo_01_pred.png
- demo_kitti.py
- demo_nyuv2.py
- README.md
# Installation Guide
git clone https://github.com/hufu6371/DORN
Downloads the entire project code from GitHub to your computer.
cd DORN
Moves into the project folder you just downloaded.
2. Docker
Easy Recommended- Git Needed to download the project code from GitHub.
- Docker Desktop Needed to build and run containers. Install it and keep it running in the background.
docker build -f caffe/docker/cpu/Dockerfile -t dorn .
Builds a runnable image based on the Dockerfile.
docker run -p 8080:80 dorn
Runs the built image as an actual container.
3. CMake
Mediumcd caffe
This project's files live in a subfolder, so move into it first.
mkdir build && cd build
Creates a folder to hold the build output and moves into it.
cmake ..
Analyzes the source code and generates build configuration files (must be run inside the build folder).
make
Compiles the code based on the generated build configuration to produce an executable.
4. Python
Easypip install -r caffe/python/requirements.txt
Installs the Python libraries listed in requirements.txt (or similar).
jupyter notebook
Launches Jupyter in your browser so you can open and run the notebook (.ipynb) files.
5. Make
Medium- Git Needed to download the project code from GitHub.
- Make Usually pre-installed on Linux/macOS. On Windows, install separately (e.g. via MSYS2 or WSL).
cd caffe
This project's files live in a subfolder, so move into it first.
make
Compiles the code based on the generated build configuration to produce an executable.
