img1 = torch.zeros(1,3,64,64) https://www.adrianbulat.com/face-alignment ResNet50, ImageNet-1k print(get_compiling_cuda_version()) %cd mmclassification /usr/local/libpointmatcher MMDetection, 2144-2151, doi: 10.1109/ICCVW.2011.6130513. Papers With Code FaceForensics++ : Karkkainen, Kimmo and Joo, Jungseock, , binvox, cuda_voxelizer , binbox URL: https://www.patrickmin.com/binvox/. Pascal VOC http://phototour.cs.washington.edu/datasets/ applications of deep neural networks, (object parts) sudo git clone --recursive https://github.com/ray-cast/RabbitToolbox Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and arXiv, 2104.13586, 2021. transforms.ToTensor(), MMDetection https://doi.org/10.1007/978-3-642-33712-3_49, Prelinger archiveYouTubeGoogle video , 5 Places365-Challenge-201669620 Windows MobileNetV3 http://sebastianruder.com/optimizing-gradient-descent/, Keras : https://keras.io/api/optimizers/ Pascal VOC 2012 --config configs/skeleton/posec3d/slowonly_r50_u48_240e_ntu120_xsub_keypoint.py \ like this: For a detailed guide about writing training loops, see the for line in result: fcheckpoint = 'https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p32_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-9cea8599.pth' top5_prob, top5_catid = torch.topk(probabilities, 5) Windows , GitHub: https://github.com/facebookresearch/pytorch3d, Avatar Erik Linder-Norn GitHub PyTorch-GAN , PyTorch-GAN : https://github.com/eriklindernoren/PyTorch-GAN, URL: https://pytorch.org/vision/stable/models.html , Dlib, FaceNet, SphereFace 3 Iris Pandas mixamo pycocotools COCO Python transforms.ToTensor(), for line in result: wsl --unregister , Hyper-VLinux Windows , !python3 scripts/demo.py --vid_file data/sample_video.mp4 --output_folder logs/demo Pandas // liboctave 2 FPN (Feature Pyramid Network), ResNeXt101 ResNeXt50 ResNet101 ResNet50 COLAMD: column approximate minimum degree MMFewShot IEEE transactions on pattern analysis and machine intelligence, Inception-v4, Inception-ResNet , Residual Networks --label-map tools/data/skeleton/label_map_ntu120.txt In ECCV, 2016, m.add(Dense(units=)) Ubuntu SQLite 3 : baloonfine tuning () --config RELEASE https://download.pytorch.org/whl/cu117/torch_stable.html CVPR 2018, also CoRR, https://arxiv.org/abs/1803.01534v4, from sklearn.datasets import load_iris meshroom URL Meshroom : https://alicevision.org/ del CMakeCache.txt sample redistribution , R Shiny : result = inference_detector(model, fimg) , Keras MobileNetV2 MobileNetV2 1, ArcFace , cv::destroyAllWindows(); , Bulat, Adrian and Tzimiropoulos, Georgios, http://www2.imm.dtu.dk/pubdb/pubs/3160-full.html, M. M. Nordstr{\o}m and M. Larsen and J. Sierakowski and M. B. Stegmann, In: Computer Vision and Pattern Recognition (CVPR), 2013 IEEE #define DIM2 21 , also CoRR, abs/1708.02002v2 pip pillow , , , PyTorch HUB : c:\libpointmatcher display(support_images) 124 , URL: https://www.mapillary.com/dataset/vistas. cmake --build . NVIDIA CUDA 20--36, 2016. libiconv 1548-1558, 2021, GitHub : https://github.com/dchen236/FairFace. https://pytorch.org/hub/pytorch_vision_resnet/ , ResNeXt ResNet redidual unit grouped conv3x3 conv2x1 . !curl -O https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531-f11027c5.pth , 2to3 Python 2 Python 3 . scikit-learnModel selection Multi-pie, Image and Vision Computing, 28(5):807813, 2010. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2873597/, 68 , (facial landmark), Multi View Stereo m.add(Dropout('0.05')) Bolei Zhou Aditya Khosla Agata Lapedriza Aude Oliva Antonio Torralba, UTF-8 target Save and categorize content based on your preferences. Papers With Code Places365 : https://paperswithcode.com/dataset/places365 c:\libnabo Seesaw LossCascade Mask R-CNN, R-101-FPN, Lr schd = 2x, LVIS v1 HELEN , response = requests.get(url) To save weights manually, use tf.keras.Model.save_weights. MMSelfSup: OpenMMLab Self-Supervised Learning Toolbox and Benchmark, notepad a.py NVIDIA cuDNN Python, !apt remove python3-pycocotools ShapeNet: An Information-Rich 3D Model Repository, git : https://git-scm.com/ model = init_detector(fconfig, fcheckpoint, device=device) Inception-ResNet image inpainting, image restoration, image matting, (super resolution), %cd TecoGAN Our VAE will be a subclass of Model, built as a nested composition of layers 1.m4a from torchvision import transforms gradient step (face identification) Cholesky factorization Tero Karras (NVIDIA), Samuli Laine (NVIDIA), Timo Aila (NVIDIA), 10LFW 40 MMClassification, AR, : Windows, Linux, Mac OS, iOS, Android, ch_PP-OCRv2_det_infer.tar, , Moblile & Server , ch_ppocr_server_v2.0_det_infer.tar, , Moblile & Server , en_number_mobile_v2.0_rec_slim_infer.tar, , Slim pruned and quantized lightweight model, supporting English and number recognition, japan_mobile_v2.0_rec_infer.tar, , Lightweight model for Japanese recognition, ch_ppocr_mobile_v2.0_cls_infer.tar, , (text detection), /content/logs/demo/sample_video_ , , 1,4641,449.  , * cmake \ OpenCV : img echo ' export PATH=${PYENV_ROOT}/bin:$PATH' >> ~/.profile Google Colaboratory pyenv , (super resolution), MMDetection SSD . panoptic segmentation, (face recognition), GAN (Generative Adversarial Network), The Model class has the same API as Layer, with the following differences: Effectively, the Layer class corresponds to what we refer to in the Xintao Wang and Liangbin Xie and Chao Dong and Ying Shan, the shape of the weights w and b in __init__(): In many cases, you may not know in advance the size of your inputs, and you img = Image.open(filename) The code is written using the Keras Sequential API with a tf.GradientTape training loop.. What are GANs? , AFLW (Annotated Facial Landmarks in the Wild) Git : International Conference on Computer Vision Workshops (ICCVW), 2021. } Searching for MobileNetV3, ICCV 2019, MPII Human Pose fair_face_models SpineNet, categories = [s.strip() for s in f.readlines()] device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') Ubuntu rmdir /s /q ADEChallengeData2016 RetinaNet, (face detection) 34, no. (face identification), fimage = 'demo/demo_detection_images/query_images/demo_query.jpg' : # The output has unnormalized scores. %cd pytorch-image-models from torchsummary import summary Global Structure from Motion GRU (Gated Recurrent Neural Networks), https://arxiv.org/pdf/1801.03924v2.pdf https://download.pytorch.org/whl/lts/1.8/torch_lts.html, ResNeXt, https://github.com/libsdl-org/SDL , import torch , Liang-Chieh Chen, George Papandreou, Florian Schroff, Hartwig Adam, vcpkg Adam , Diederik Kingma and Jimmy Ba, (Google Colaboratory): Vighnesh Birodkar, Zhichao Lu, Siyang Li, Vivek Rathod, Jonathan Huang, transforms.CenterCrop(224), OpenCV C++ !git clone https://github.com/open-mmlab/mmfewshot.git cmake : https://cmake.org/download/ model = init_model(fconfig, fcheckpoint, device=device) cv::destroyAllWindows(); sudo git clone --recursive https://github.com/laurentkneip/opengv git : https://git-scm.com/ ocr = PaddleOCR(use_angle_cls=True, det_model_dir="./ch_ppocr_server_v2.0_det_infer/", cls_model_dir='./ch_ppocr_mobile_v2.0_cls_infer/', lang='japan') # need to run only once to download and load model into memory txts = [line[1][0] for line in result] df['species'] = iris.target_names[iris.target] import timm Zheng Zhu, Guan Huang, Jiankang Deng, Yun Ye, Junjie Huang, Xinze Chen, Jiagang Zhu, Tian Yang, Jia Guo, Jiwen, Lu, Dalong Du, and Jie Zhou. arXiv:1906.07155, 2019. Keras , show_result_pyplot(model, fimg, result, score_thr=0.3) response = requests.get(url) These losses also work seamlessly with fit() (they get automatically summed also CoRR, abs/1708.02002v2 One of the central abstraction in Keras is the Layer class. --config RELEASE --target INSTALL MMCV, !git clone https://github.com/rwightman/pytorch-image-models.git MMDetection SSD : https://github.com/open-mmlab/mmdetection/blob/master/configs/ssd/README.md LReLU, residual function (), # Python Python Python benchmark method. Windows SQLite 3 : LSTM (Long Short-Term Memory), fconfig = 'configs/vision_transformer/vit-base-p32_ft-64xb64_in1k-384.py' https://arxiv.org/pdf/1502.00046v1.pdf A mask is a boolean tensor (one (face verification), python build\software\SfM\tutorial_demo.py pyenv install -l | grep 3.6 sudo apt install python3-statsmodels SlikSVN https://sliksvn.com/ 64-bit from IPython.display import display , (depth) # , ImageDataset_SceauxCastle\images files.download('accompaniment.wav') 500 alpha matte Trimap , URL: https://drive.google.com/drive/folders/1IyPiYJUp-KtOoa-Hsm922VU3aCcidjjz. cd mmdetection fimg = 'demo/demo.jpg' for i in range(top5_prob.size(0)): MobileNetV3 pp. pd.set_option('display.max_rows', None), Dlib Functional model, you can optionally implement a get_config() MNIST , Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, TinaFace, Cholesky factorization , TensorFlow tf.train.Optimizer . display(Image('./demo_files/lq_sequences/city/00000000.png')) display(Image('./outputs/city_BasicVSR/00000001.png')) target ResNet50, ImageNet-1k FairFace, CoRR, abs/2108.01077v3, 2021. https://github.com/FFTW/fftw3 , https://pytorch.org/vision/stable/datasets.html#torchvision.datasets.LSUN Raspberry Pi cmdline.txt init=/bin/sh cd src ICCV 2017, also, CoRR, . !apt -y install libturbojpeg --pose-checkpoint https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth \ Python : , Python, pip, Python Python , from IPython.display import Image, display Compute matches show_result_pyplot(model, fimg, result) Docker Docker PyTorch, torchvision ResNeXt101 32x8d , PDF PyTorch, torchvision , R (Rdatasets) input_tensor = preprocess(img) !pip3 install -U keras==2.3.1 Dlib , Instruction for the Instance Segmentation Task: Setup import numpy as np import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers Introduction. echo 5. ResNet50, ResNet101, ResNet152, import torchvision.models as models !python3 demo/demo_skeleton.py demo/ntu_sample.avi demo/skeleton_demo.mp4 \ : Windows : python -m pip install lpips !curl -O https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-c8551505.pth https://mmclassification.readthedocs.io/en/latest/install.html NVIDIA CUDA 10.0 .\a.exe Shiny URL (facial landmark), Large-scale CelebFaces Attributes (CelebA) URL , URL: https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html. R URL OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark, Win32 GNU , Google Google Generative Adversatial Network on MNIST: Train a simple generative adversarial network on the MNIST dataset. RetinaNet, abs/1312.6114, 2013. (skelton-based action recognition), https://mmcv.readthedocs.io/en/latest/get_started/installation.html The Keras functional API is a way to create models that are more flexible than the tf.keras.Sequential API. cmake -G "Unix Makefiles" \ rwightman PyTorch Image Models (TIMM) Windows OpenMVS DenseNet121, DenseNet169 TensorFlow Places365 : pip install -U tensorflow-gpu==1.15.5 cd /tmp sudo pip3 list Papers With Code LSP : https://paperswithcode.com/dataset/lsp cd /usr/local matplotlib Windows Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation. x f x x , Variational Auto Encoder; VAE) for the weights of any inner layer: These losses are meant to be taken into account when writing training loops, 4. http://phototour.cs.washington.edu/patches/default.htm no horizon, enclosed area, man-made, socializing, indoor lighting, cloth, congregating, eating, working International Journal of Computer Vision, 111(1), 98-136, 2015. face alignment, Ross Wightman pytorch-image-models (GitHub) : Excel xlsx CSV (in2csv) : in2csv a.xlsx > a.csv, CSV JSON (csvjson) : csvjson a.csv > a.json, BRDF : BRDF the Oren-Nayar model the Koenderink et al. CSPResNet50-PANet-SPP, Global Structure from Motion python py -3.7 ImageNet ResNeXt101 32x8d (instance segmentation) https://download.pytorch.org/whl/cu113/torch_stable.html Python https://arxiv.org/pdf/2003.10580v4.pdf. -DEIGEN_INCLUDE_DIR="c:/eigen/include/eigen3" ^ Python show_result_pyplot(model, fimg, result) cd /usr/local/PARE rwightman PyTorch Image Models (TIMM) , https://github.com/deepinsight/insightface/tree/master/detection/scrfd, SDL Simple DirectMedia Layer MMClassification: https://mmclassification.readthedocs.io/en/latest/model_zoo.html Like this: The __call__() method of your layer will automatically run build the first time python setup.py install Real-ESRGAN : https://colab.research.google.com/drive/1k2Zod6kSHEvraybHl50Lys0LerhyTMCo?usp=sharing#scrollTo=7IMD5vhOYp68
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