Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong But because these tutorials use MNIST, the output is already in the zero-one range and can be interpreted as an image. display(df) This tutorial demonstrates how to generate images of handwritten digits using a Deep Convolutional Generative Adversarial Network (DCGAN). , X C for line in result: 434 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') /bigobj P. Moulon, P. Monasse and R. Marlet, "Global Fusion of Relative Motions for Robust, Accurate and Scalable Structure from Motion," 2013 IEEE International Conference on Computer Vision, 2013, pp. , Variational Auto Encoder; VAE) Auto-Encoding VB algorithm; AEVB algorithm In CVPR, 2016, ShapeNet URL , R Shiny YOLOX, Mengyu Chu, You Xie, Jonas Mayer, Laura Leal-Taix, Nils Thuerey, Bottom-up Path Augmentation  , Google Colaboratory PyTorch Python https://github.com/eriklindernoren/PyTorch-YOLOv3 , Residual Networks (ResNets) MMCV, For instance, in a ResNet50 model, you would have several ResNet blocks Clang, Inception-v4, Inception-ResNet , mediainfo , OBS (Open Broadcaster Software) GRU (Gated Recurrent Neural Networks), %cd TecoGAN echo 'export PYENV_ROOT="${HOME}/.pyenv"' >> ~/.profile 3 (3D reconstruction), RetinaNet 100, 200 , PARE 3D human pose estimation (data flow graph) Python 3 (3D face reconstruction) car, truck, bus, on rails, motorcycle, bicycle, caravan, trailer, move resnet50_8xb32_in1k_20210831-ea4938fc.pth checkpoints ResNet50, ResNet101, ResNet152, ResNet, R : Convolutional autoencoder for image denoising To save in the HDF5 format with a .h5 extension, refer to the Save and load models guide. sudo rm -rf TecoGAN Google Colaboratory Russell BC, Torralba A, Murphy KP, Freeman WT, Google Colaboratory: https://colab.research.google.com/drive/1vgD2HML7Cea_z5c3kPBcsHUIxaEVDiIc (facial landmark), https://www.adrianbulat.com/face-alignment, , mkdir checkpoints fimg = 'demo/demo.JPEG' TensorFlow 1.14.0 : Python 3.7, 3.6, 3.5, 3.4, 3.3, 2.7 (: https://pypi.org/project/tensorflow/1.14.0/#files) TensorFlow 2.6.3, OpenMVG Structure from Motion (SfM) (inference) Python, PyTorch International Conference on Learning Representations, also CoRR, dropout: a simple way to prevent neural networks from overfitting, https://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf, : improving neural networks by preventing co-adaptation of feature detectors, corr, abs/1207.0580, , B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba, warnings.filterwarnings('ignore') # Suppress Matplotlib warnings import torch International Conference on Computer Vision Workshops (ICCVW), 2021. CoRR, abs/1505.04597v1, 2015. import os 2017. , , (instance segmentation), YOLOv5 Papers With Code Pascal VOC : URL processed Pascal VOC 2007, 2021 , 2 Places365-Standard Places365-Challenge-2016 DETR, R-50, Lr schd = 150e result = inference_detector(model, fimg) CASILVision, , (backpropagation) Windows MeCab , Merkaartor c:\libnabo sudo chown -R $USER Mask_RCNN matplotlib OpenCV NVIDIA CUDA NVIDIA GPU MMAction2 : https://mmaction2.readthedocs.io/en/latest/ mmaction/demo/skeleton_demo.mp4 NVIDIA CUDA cu111 https://arxiv.org/pdf/1512.03385v1.pdf files = os.listdir(fsupport_images_dir) inria-00321923, libnabo K (K nearest neighbour) . DETR MMDetection 3D pose estimation, sync PyTorch MMEditing GitHub : https://github.com/open-mmlab/mmediting input_tensor = preprocess(img) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, OpenCV [PDF] , [] TensorFlow, HELEN , NVIDIA CUDA Chandrika Deb , !rm -f 2.jpg https://www.tensorflow.org/datasets/catalog/scene_parse150 NVIDIA CUDA 11.0.3 (11.1 ()) Matplotlib OpenCV Papers With Code LSP : https://paperswithcode.com/dataset/lsp Mask R-CNN, MMDetection, CVPR 2017, also CoRR, abs/1608.05442, 2017. https://openaccess.thecvf.com/content_cvpr_2017/papers/Zhou_Scene_Parsing_Through_CVPR_2017_paper.pdf. Welcome to Part 3 of Applied Deep Learning series. SD %cd mmediting (semantic segmentation), CGAL VOC_0712_converted %matplotlib inline from mmcv.ops import get_compiling_cuda_version, get_compiler_version Rethinking Atrous Convolution for Semantic Image Segmentation, (semantic segmentation), import torchvision.models as models IndexNet, 20 10,5535,019. https://drive.google.com/file/d/1n9C4CiBURMSCZy2LStBQTzR17rD_a67e/view !curl -O http://sam.johnson.io/research/lsp_dataset.zip OpenBLAS BLAS, CBLAS, LAPACK, LAPACKEBuild Tools for Visual Studio Windows show_result_pyplot(model, fimg, result, score_thr=0.3) Google Colaboratory CoRR, abs/2108.01077v3, 2021. also CoRR, abs/2104.08527v2, 2021. OpenCV : objectInfo150.txt LLDB LLVM OpenCV (real time computer vision) MMFewShot : https://mmfewshot.readthedocs.io DIM, from IPython.display import Image, display : https://developers.google.com/protocol-buffers/docs/tutorials cv::destroyAllWindows(); for i in range(top5_prob.size(0)): CSPResNeXt50-PANet-SPP Wasserstein Deformable DETR, ++ two-stage Deformable DETR, R-50, Lr schd = 50e MMDetection COLAMD: column approximate minimum degree Python, Clang : https://clang.llvm.org/ https://mmclassification.readthedocs.io/en/latest/install.html arXiv:2108.07189, Windows Python a.py display . NVIDIA cuDNN 7 NVIDIA cuDNN 7.6.5 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') PyTorch SBU ECCV, 2018. . boost-1_81 Image super-resolution using deep convolutional networks, SfM (Structure of Motion) MMD Windows protobuf protoc CoRR, cd /usr/local rmdir /s /q build IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. int main( int argc, char **argv ) winget winget install MeshLab import torch Residual Networks (ResNets), // liboctave 2 (text detection) transforms.ToTensor(), , R CRAN URL: https://cran.r-project.org/ import torch Comparison of nearest-neighbor-search strategies and implementations for efficient shape registration, SpineNet ECCV, also CoRR, https://arxiv.org/abs/1512.02325v5 . CSPNet: A New Backbone that can Enhance Learning Capability of CNN, Pascal VOC (Pascal Visual Object Classes Challenge) , Overview. PyTorch 'species' For example, given an image of a handwritten digit, an autoencoder first encodes the image into a lower dimensional latent representation, then decodes the latent representation back to an image. Pandas numpy.ndarray git : https://git-scm.com/ !mkdir checkpoints sudo rm -rf libpointmatcher cmake -DCMAKE_BUILD_TYPE=Release \ multi person tracker , : kaneko_sample_video.mp4. MIT Scene Parsing Benchmark (skelton-based action recognition) 2021 MMFewShot Web !mv deformable_detr_twostage_refine_r50_16x2_50e_coco_20210419_220613-9d28ab72.pth checkpoints Windows Subsystem for Linux, Windows 10 Windows , Windows , Windows Windows , Ubuntu TensorFlow 2.7.1 : Python 3.9, 3.8, 3.7 (: https://pypi.org/project/tensorflow/2.7.1/#files) , winget winget install Microsoft.VisualStudio.2019.Community, Visual Studio Installer, https://visualstudio.microsoft.com/visual-cpp-build-tools/, Windows Visual Studio Community 2022 : sudo apt -y install git https://mmclassification.readthedocs.io/en/latest/install.html, display([x * 10 for x in df['sepal length (cm)']]) PyTorch-GAN Multi View Stereo, Windows librosa import torch Windows PoseC3D MMDetection YOLOX : https://github.com/open-mmlab/mmdetection/blob/master/configs/yolox/README.md rmdir /s /q libnabo boolean value per timestep in the input) used to skip certain input timesteps display(df.iloc[:,0:4]) , inputs to outputs (a "call", the layer's forward pass). DETR, --label-map tools/data/skeleton/label_map_ntu120.txt summary(m, (3, 224, 224)) # Paddleocr supports Chinese, English, French, German, Korean and Japanese. the call() method. !curl -O https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar Journal of Open Source Software, , Keras TORCH_VERSION = ".".join(torch.__version__.split(". linear scale and will use the Model class to define the outer model -- the object you Towards Fast, Accurate and Stable 3D Dense Face Alignment, Google Colab Windows Linux https://github.com/CAOR-MINES-ParisTech/libpointmatcher/blob/master/doc/CompilationWindows.md street scenes. Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollr, Comparing ICP Variants on Real-World Data Sets, Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data, TensorFlow : https://github.com/tensorflow/models , (action recognition), https://github.com/open-mmlab/mmdetection/blob/master/docs/en/get_started.md , MMDetection, MMPose , Temporal Segment Networks (TSN), ResNet50, ImageNet, Kinetics-400 , MMAction2 Temporal Segment Networks (TSN) : https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tsn/README.md, : https://github.com/open-mmlab/mmaction2/blob/master/demo/mmaction2_tutorial.ipynb , Temporal Segment Networks (TSN), ResNet50, ImageNet, Kinetics-400 , PoseC3D, SlowOnly-R50, NTU120_XSub , MMAction2 PoseC3D : https://github.com/open-mmlab/mmaction2/blob/master/configs/skeleton/posec3d/README.md, MMAction2 : https://github.com/open-mmlab/mmaction2/blob/master/demo/README.md#skeleton-based-action-recognition-demo , CNN (convolutional neural network), inference_detector(model, 'demo/demo.jpg') MMSegmentation, (x_train, y_train), (x_test, y_test) = reuters.load_data() 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition, vol. mkdir build pip install -U keras==2.3.1 MapReduce (block) cd build !mv detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth checkpoints MMClassification Contributors, (face verification), Ubuntu https://www.tensorflow.org/datasets/catalog/places365_small protobuf !mkdir checkpoints 61scene category40%/10%/50%(training data)(validation data)(test data), . dlib, Proc. Papers with Code : https://www.tensorflow.org/datasets/catalog/lsun stacked hourglass !rm -f 1.jpg OpenGV LReLU (Leaky rectified linear unit) MMCV Contributors, MMCV: OpenMMLab Computer Vision Foundation, sudo apt -y install git cmake cmake-curses-gui cmake-gui MMDetection, C-MS-Celeb Cleaned abs/1312.6114, 2013. Proc. MMEditing EDVR : https://github.com/open-mmlab/mmediting/blob/master/configs/restorers/edvr/README.md PDF: https://arxiv.org/pdf/1703.06870v3.pdf of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), OpenMVS , (action recognition), Google Protoc proto , sudo git clone --recursive https://github.com/ray-cast/RabbitToolbox (action recognition)SlowOnly-8x8-R101 191--205, 2003. cuda_voxelizer GitHub : https://github.com/Forceflow/cuda_voxelizer, .binbox STL !python3 setup.py install del CMakeCache.txt OpenCV C++ Simple MNIST convnet; Image segmentation with a U-Net-like architecture; 3D image classification from CT scans; Semi-supervision and domain adaptation with AdaMatch; Classification using Attention-based Deep Multiple Instance Learning (MIL). ground, dynamic, static, python -m pip install terminaltables NWPU-Crowd , (semantic segmentation), lane marking, pose estimation, Windows BDD100K Images, Detection 2020 Labels, Pose Estimation Labels , BDD100K image tagging, : http://tensorflow.org/tutorials, https://github.com/nlintz/TensorFlow-Tutorials { abs/2010.04159v4, !pip3 install git+https://github.com/open-mmlab/mim.git fimg = 'demo/demo.JPEG' .\build\Windows-AMD64-Release\Release\openMVG_main_SfMInit_ImageListing -i ImageDataset_SceauxCastle/images/ -d openMVG/exif/sensor_width_database/sensor_width_camera_database.txt -o ImageDataset_SceauxCastle/matches/ (https://pytorch.org/) , NVIDIA CUDA OpenMVG (2021/08/21 ) Windows 10 64 : openMVG.zip sudo apt -y install git cmake cmake-curses-gui cmake-gui libeigen3-dev Proc. !mim install . RabbitToolBox SuiteSparse https://github.com/open-mmlab/mmflow, 2021. Eigen 3 vcpkg cmake Intro to TFLearn: A couple introductions to a high-level library for building neural networks. (instance segmentation), computation redistribution 2 , (facial landmark), CityGML 33 python -m pip install torchsummary Comparing ICP Variants on Real-World Data Sets, , (data augmentation), PyTorch , Google Colaboratory MMDetection OpenMMLab Structure from Motion (SfM) Internal: Attempting to perform BLAS operation using StreamExecutor without BLAS support RetinaNet, R-50-FPN, Lr schd = 1x SSD Pandas CoRR, abs/1811.09393v4, 2018. , 'road', 'slidewalk', 'building' , Python display(Image('./tests/data/pred/inpainting_celeba.png')) ArcFace , python -m pip install git+https://github.com/cocodataset/panopticapi.git (n_sample, nb_classes) Tracking a Depth Camera: Parameter Exploration for Fast ICP, also CoRR, abs/1608.05442v2, 2016. Python pip setuptools , printf( "start, \n" ); Google Colaboratory from IPython.display import Image, display Bottom-up Path Augmentation PDF: https://arxiv.org/pdf/2102.07925.pdf, (crowd counting) (FIDTM ), https://colab.research.google.com/drive/1cmeI93PcRc20E70z6X_W3bvH2k1ge2v3?usp=sharing#scrollTo=wyQ3Xe7_gt_t, FIDTM (crowd counting) , FIDTM Google Colaboratory , URL: https://www.kkaneko.jp/cc/ni/index.html, m CPU a.pth , , !mkdir checkpoints Python : https://www.python.org/ OpenMMLab . bgr = cv2.imread("fruits.jpg") rwightman PyTorch Image Models (TIMM) 28,000 11,000 --pose-checkpoint https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth \ pip3 install instaboostfast Python 3.7 TensorFlow 1.15TensorFlow 1.14 yuki-koyama blender-cli-rendering, PyPI 202112 Python 3.7Blender 2.8. (IMFD) . File, Open, Open GML File , : input_batch = input_batch.to('cuda') fimg = 'demo/demo.jpg' https://www.adrianbulat.com/face-alignment -DLIBNABO_BUILD_PYTHON=OFF ^ Reuters newswire topics (semantic segmentation), !git clone https://github.com/PaddlePaddle/PaddleOCR If so, go with Model. Pandas iris Iris !curl -O https://download.openmmlab.com/mmaction/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth CSPResNet50-PANet-SPP, !mkdir checkpoints Web , Google Colaboratory MIM PaddlePaddle 2.0 GPU MMClassification, Python, PyTorch , PyTorch, torchvision : https://pytorch.org/vision/stable/models.html , m.eval(), depth image depth TensorFlow from torchvision import transforms Background matting . mask head R (Rdatasets) 3D human pose estimation, Gu, Chunhui and Sun, Chen and Ross, David A and Vondrick, Carl and Pantofaru, Caroline and Li, Yeqing and Vijayanarasimhan, Sudheendra and Toderici, George and Ricco, Susanna and Sukthankar, Rahul and others, R (Rdatasets) BY NC SA Ubuntu : sudo pip3 install lpips guide to writing a training loop from scratch, It exposes built-in training, evaluation, and prediction loops , Docker Docker , exit --rm, DUTS saliency detection from sklearn.datasets import load_iris --pose-config demo/hrnet_w32_coco_256x192.py \ sudo apt -y update cd build Google Colaboratory CoRR, abs/1611.05431, 2016. PyTorch HUB : 3, no. the first __call__() to trigger building their weights. !pip3 show spleeter url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") torch.save(m.to('cpu').state_dict(), 'a.pth') CVPR 2016, 2016. -DCMAKE_BUILD_TYPE=Release ^ Ubuntu print(v, v[0], v[1], v[2]) m = models.resnext101_32x8d(pretrained=True).to(device) Python 3.7 TensorFlow 1.15.5 , Ubuntu Python Python 3.6 TensorFlow 1.15.5 venv Ubuntu , cv2.waitKey(0) PyTorch, NVIDIA CUDA PyCharm Python Yuning Du, Chenxia Li, Ruoyu Guo, Cheng Cui, Weiwei Liu, Jun Zhou, Bin Lu, Yehua Yang, Qiwen Liu, Xiaoguang Hu, Dianhai Yu, Yanjun Ma, , Mask R-CNN, FPN (Feature Pyramid Network), Lr schd = 3x , MMDetection Mask R-CNN : https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn, MMDetection : https://github.com/open-mmlab/mmdetection/blob/master/demo/MMDet_Tutorial.ipynb , Mask R-CNN, FPN (Feature Pyramid Network), Lr schd = 3x , RetinaNet, R-50-FPN, Lr schd = 1x , MMDetection RetinaNet : https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet, RetinaNet, R-50-FPN, Lr schd = 1x , MMDetection DETR : https://github.com/open-mmlab/mmdetection/blob/master/configs/detr/README.md, Deformable DETR, ++ two-stage Deformable DETR, R-50, Lr schd = 50e , MMDetection Deformable DETR : https://github.com/open-mmlab/mmdetection/blob/master/configs/deformable_detr/README.md, Deformable DETR, ++ two-stage Deformable DETR, R-50, Lr schd = 50e , MMDetection YOLOX : https://github.com/open-mmlab/mmdetection/blob/master/configs/yolox/README.md, YOLOv3, DarkNet-53, Lr schd = 273e, with mixed precision training , MMDetection YOLOv3 : https://github.com/open-mmlab/mmdetection/blob/master/configs/yolo/README.md, YOLOv3, DarkNet-53, Lr schd = 273e, with mixed precision training , Seesaw LossCascade Mask R-CNN, R-101-FPN, Lr schd = 2x, LVIS v1 , MMDetection Seesaw Loss : https://github.com/open-mmlab/mmdetection/blob/master/configs/seesaw_loss/README.md, Seesaw LossCascade Mask R-CNN, R-101-FPN, Lr schd = 2x, LVIS v1 , SSDsize=512, Lr schd = 120x, COCO , MMDetection SSD : https://github.com/open-mmlab/mmdetection/blob/master/configs/ssd/README.md, SSDsize=512, Lr schd = 120x, COCO , https://mmdetection3d.readthedocs.io/en/latest/getting_started.html#installation , %LOCALAPPDATA%\mmdetection , git checkout v2.14.0 2.14.0 try: urllib.URLopener().retrieve(url, filename) img = Image.open(filename) I F, B, alpha !pip3 install mmcv-full==1.3.3 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.10/index.html Ubuntu C++ /tmp/a.cpp , PyTorch HUB : depth , depth image , depth image monodepth2 (2019 ) , monodepth2 GitHub : The MUCT Landmarked Face Database, m.add(Dense(units=100, input_dim=len(768[0]))) cd mmdetection (text detection), MMFlow show_result_pyplot(model, fimg, result, score_thr=0.3) #include transforms.Resize(256), Deformable DETR MMDetection input_batch = input_batch.to('cuda') AMD: symmetric approximate minimum degree Real-ESRGAN : https://colab.research.google.com/drive/1k2Zod6kSHEvraybHl50Lys0LerhyTMCo?usp=sharing#scrollTo=7IMD5vhOYp68 Google Colaboratory rmdir /s /q openmvg process_support_images(model, support_images, support_labels, classes=classes) OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark, MMSegmentation, , 2to3 Python 2 Python 3 . display(Image('result.jpg')) Stoop YOLOX MMDetection . E. Learned-Miller, G. B. Huang, A. RoyChowdhury, H. Li, and G. Hua. sudo pip3 install -U -r requirements.txt meshroomOpenMVG , m.add(Dropout('0.05')) Clang, LLVM, LLD, LLDB Build Tools for Visual Studio Windows m = m.to(device) PyTorch, torchvision ResNet152 , m.add(Dense(units=100)) meshroomOpenMVG cv2.imshow("", bgr) python runGan.py 1 Keras , R x, y rmdir /s /q ADEChallengeData2016 !ls -la lspet_dataset Dlib, FaceNet, SphereFace 3 Docker Docker Liang-Chieh Chen, George Papandreou, Florian Schroff, Hartwig Adam, f(x1, x2, x3, x4, x5) = sigmoid(w1x1 + w2x2 + w3x3 + w4x4+ w5x5), Google Colaboratory OpenCV Python !rm -rf mmaction2 mmfewshot Contributors, 295--307, 2015. SN_d_tot_V2.0.csv TensorFlow 2.2.3 : Python 3.8, 3.7, 3.6 (: https://pypi.org/project/tensorflow/2.2.3/#files) Python numpy Windows , https://github.com/open-mmlab/mmpose/blob/master/demo/docs/3d_human_pose_demo.md model = init_recognizer(config, checkpoint, device=device) MMCV https://github.com/open-mmlab/mmfewshot, 2021. import matplotlib.pyplot as plt MMDetection, https://mmclassification.readthedocs.io/en/latest/install.html, SfM https://github.com/open-mmlab/mmfewshot/blob/main/docs/en/install.md MMAction2 print(categories[top5_catid[i]], top5_prob[i].item()) R Shiny : Ubuntu PostgreSQL 14, pgAdmin 4, PostGIS 3 : pip pillow from PIL import Image for i in range(top5_prob.size(0)): Scaled_YOLOv4, pytorchimagemodels, csvkit : https://csvkit.readthedocs.io/en/latest/, URL: https://github.com/wireservice/csvkit/tree/master/examples/realdata, Computing Research Repository CoRR Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and Places365 pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116 cd c:\opencv\sources\samples\data Save and categorize content based on your preferences. from mmcls.apis import inference_model, init_model, show_result_pyplot