10 Face Datasets To Start Facial Recognition Projects [Note Jan 05, 2020] Currently, the MobileNetV3 backbone model and the Full Integer Quantization model do not return correctly. Each model is adversarially trained on varying numbers of adversarial examples, with 7 points for each method compared in the figure (Color figure online) junyanz/pytorch-CycleGAN-and-pix2pix ICCV 2017 Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. Learn more, including about available controls: Cookies Policy. CelebA Publicly available scenes from the Middlebury dataset 2014 version . Confirm the structure of saved_model ssd_mobilenet_v3_large_coco_2019_08_14, 2-5-5. Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. Comments (0) Run. As of May 05, 2020. When you want to fine-tune DeepLab on other datasets, there are a few cases, [deeplab] Training deeplab model with ADE20K dataset, Running DeepLab on PASCAL VOC 2012 Semantic Segmentation Dataset, Quantize DeepLab model for faster on-device inference, https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md, https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/quantize.md, the quantized form of Shape operation is not yet implemented, Minimal code to load a trained TensorFlow model from a checkpoint and export it with SavedModelBuilder. Some tasks are inferred based on the benchmarks list. "mobilenet_v3_small_seg" Quantization-aware training, 2-3-2. This figure shows the classification errors on the test set of the CelebA dataset for Our algorithm compared to three other algorithms, sorted based on the result of Ours. . VisionDataset(root[,transforms,transform,]). Base Class For making datasets which are compatible with torchvision. module, as well as utility classes for building your own datasets. The benchmarks section lists all benchmarks using a given dataset or any of Creating the destination path for the calibration test dataset 6GB, 2-5-6-1. ssd_mobilenet_v3_small_coco_2019_08_14, 2-5-6-2. ssd_mobilenet_v3_large_coco_2019_08_14, 2-6. Image Classification is a fundamental task that attempts to comprehend an entire image as a whole. Publication Year: 2018. For training data, each category contains a huge number of images, ranging from around 120,000 to We introduce a new generative model where samples are produced via Langevin dynamics using gradients of the data distribution estimated with score matching. tensorflow2mobilenet v2, tensorflow2.4pytorch1.10. Recently, deep convolutional neural networks (CNNs) have emerged as clear winners on object classification benchmarks, in part due to training with 1.2M+ labeled classification images. A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains. For Beta features, we are committing to seeing the feature through to the Stable classification. Description CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML. i.e, they have __getitem__ and __len__ methods implemented. Because gradients can be ill-defined and hard to estimate when the data resides on low-dimensional manifolds, we perturb the data with different levels of Gaussian noise, and jointly estimate the Logs. ; loader (callable) A function to load a sample given its path. PyTorch pytorchmobilenet v23. junyanz/pytorch-CycleGAN-and-pix2pix Typically, Image Classification refers to images in which only one object appears and is analyzed. 1. InceptionV3, CelebFaces Attributes (CelebA) Dataset, [Private Datasource] Hair Color - Multi Class Classification - CelebA. pytorchpytorch_learningtensorflowtensorflow_learning. Please read the contents of the LICENSE file located directly under each folder before using the model. 1. We present a class of efficient models called MobileNets for mobile and embedded vision applications. Celebrity Face Classification using Keras . [1710.10196] Progressive Growing of GANs for Improved Quality The Frechet Inception Distance score, or FID for short, is a metric that calculates the distance between feature vectors calculated for real and generated images. liuzhuang13/DenseNet The goal is to classify the image by assigning it to a specific label. Depth Estimation from Monocular/Stereo Images, Sample.1 - Object detection by video file, Sample.2 - Object detection by USB Camera, Sample.3 - Head Pose Estimation, Multi-stage inference with multi-model, Sample.4 - Semantic Segmentation, DeeplabV3-plus 256x256, Sample.5 - MediaPipe/FaceMesh, face_detection_front_128_weight_quant, face_landmark_192_weight_quant, Sample.6 - MediaPipe/Objectron, object_detection_3d_chair_640x480_weight_quant, Sample.7 - MediaPipe/Objectron, object_detection_3d_chair_640x480_openvino_FP32, Sample.8 - MediaPipe/BlazeFace, face_detection_front_128_integer_quant, Sample.9 - MediaPipe/Hand_Detection_and_Tracking(3D Hand Pose), hand_landmark_3d_256_integer_quant.tflite + palm_detection_builtin_256_integer_quant.tflite, Sample.10 - DBFace, 640x480_openvino_FP32, Sample.11 - Human_Pose_Estimation_3D, 640x480, Tensorflow.js + WebGL + Browser, Sample.12 - BlazePose Full Body, 640x480, Tensorflow.js + WebGL + Browser, Sample.13 - Facial Cartoonization, 640x480, OpenVINO Corei7 CPU only, 2-1-2. Deep Learning In this project, you will build and train a custom GAN architecture on the CelebA dataset, leveraging the different skills learned during the course. MobileNetv1v22. Learn how our community solves real, everyday machine learning problems with PyTorch. "mobilenet_v3_large_seg" Export Model, 2-2-5. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. To analyze traffic and optimize your experience, we serve cookies on this site. Synthetic dataset used in training the CREStereo architecture. Simple tool to combine onnx models. CVPR 2019. ATTRIBUTE CLASSIFICATION ERROR ON CELEBA. This "mobilenet_v3_large_seg" Float32 regular training, 2-2. abstract_reasoning (manual) bigearthnet; caltech101; celeb_a; flic; Image-generation. Leaf: A Benchmark for Federated Settings In the experiment section, we conduct facial attribute classifications on CelebA and UTK Face datasets (Liu et al. How to restore Tensorflow model from .pb file in python? There was a problem preparing your codespace, please try again. "mobilenet_v3_small_seg" Float32 regular training, 2-1-3. Image Classification In contrast, object detection involves both classification and localization tasks, and is used to analyze **** DQ = Dynamic Range Quantization. * WQ = Weight Quantization 009_multi-scale_local_planar_guidance_for_monocular_depth_estimation, 012_Fast_Accurate_and_Lightweight_Super-Resolution, 022_Learning_to_See_Moving_Objects_in_the_Dark, 034_ssd_mobilenet_v2_mnasfpn_shared_box_predictor, 063_3d-bounding-box-estimation-for-autonomous-driving, 099_efficientnet_anomaly_detection_segmentation, 124_person-attributes-recognition-crossroad-0230, 125_person-attributes-recognition-crossroad-0234, 126_person-attributes-recognition-crossroad-0238, 139_PSD-Principled-Synthetic-to-Real-Dehazing-Guided-by-Physical-Priors, 151_object_detection_mobile_object_localizer, 154_driver-action-recognition-adas-0002-encoder, 155_driver-action-recognition-adas-0002-decoder, 175_face-recognition-resnet100-arcface-onnx, 181_models_edgetpu_checkpoint_and_tflite_vision_segmentation-edgetpu_tflite_default_argmax, 182_models_edgetpu_checkpoint_and_tflite_vision_segmentation-edgetpu_tflite_fused_argmax, 184_pedestrian-and-vehicle-detector-adas-0001, 185_person-vehicle-bike-detection-crossroad-0078, 186_person-vehicle-bike-detection-crossroad-1016, 187_vehicle-attributes-recognition-barrier-0039, 188_vehicle-attributes-recognition-barrier-0042, 189_vehicle-license-plate-detection-barrier-0106, 233_HRNet-for-Fashion-Landmark-Estimation, 285_Decoupled-Low-light-Image-Enhancement, 297x__OpenVINO_2021.4.582__OpenVINO_2022.1.0, 7. On aarch64 OS, performance is about 4 times higher than on armv7l OS. Simple Constant value Shrink for ONNX. GitHub All datasets are subclasses of torch.utils.data.Dataset i.e, they have __getitem__ and __len__ methods implemented. *** CM = CoreML My model conversion scripts are released under the MIT license, but the license of the source model itself is subject to the license of the provider repository. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Kitti2015Stereo(root[,split,transforms]). PyTorchDCGAN Hence, they can all be passed to a torch.utils.data.DataLoader [Tensorflow Lite] Various Neural Network Model quantization methods for Tensorflow Lite (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization, EdgeTPU). KITTI dataset from the 2012 stereo evaluation benchmark. There are 40 attributes. Confirm the structure of saved_model ssd_mobilenet_v3_small_coco_2019_08_14, 2-5-4. CelebA has large diversities, large quantities, and rich annotations, including 10,177 number of identities, 202,599 number of face images, and 5 Deep Convolutional Generative Adversarial Network Simple Network Combine Tool for ONNX. 1.1.1.l l World Development Indicators l l Zill A repository for storing models that have been inter-converted between various frameworks. Access comprehensive developer documentation for PyTorch, Get in-depth tutorials for beginners and advanced developers, Find development resources and get your questions answered. Image-to-image translation is the task of taking images from one domain and transforming them so they have the style (or characteristics) of images from another domain. clovaai/stargan-v2 Torchvision provides many built-in datasets in the torchvision.datasets German Traffic Sign Recognition Benchmark (GTSRB) Dataset. **Image Classification** is a fundamental task that attempts to comprehend an entire image as a whole. Download here. KITTI dataset from the 2015 stereo evaluation benchmark. Datasets Torchvision 0.14 documentation Dataset interface for Scene Flow datasets. CelebA 128 x 128 COCO-GAN See all. For example, ImageNet 3232 Mrinal Kanti Bhowmik, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu, Mahantapas Kundu .Human Face Recognition using Line Features. tensorlow2mobilenet v2AlexNetVGGGoogLeNetResNet Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation CVPR 2018. PhotoTour(root,name[,train,transform,]). Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks. These results Possible values are 'name_of_my_model', Configure input_map when importing a tensorflow model from metagraph file, How to install Ubuntu 19.10 aarch64 (64bit) on RaspberryPi4, https://github.com/rwightman/posenet-python.git, https://github.com/sayakpaul/Adventures-in-TensorFlow-Lite.git, person-attributes-recognition-crossroad-0230, person-attributes-recognition-crossroad-0234, person-attributes-recognition-crossroad-0238, vehicle-attributes-recognition-barrier-0039, vehicle-attributes-recognition-barrier-0042, TextBoxes++ with dense blocks, separable convolution and Focal Loss, ssss_s2d/320x320,640x640,960x960,1280x1280, nano,tiny,s,m,l,x/256x320,320x320,416x416,480x640,544x960,736x1280,1088x1920, Fisheye, cepdof/habbof/mw_r, 608x608/1024x1024, 256x256,PriorBoxClustered->ndarray(0.npy), 512x512,PriorBoxClustered->ndarray(0.npy), pedestrian-and-vehicle-detector-adas-0001, person-vehicle-bike-detection-crossroad-0078, 1024x1024,PriorBoxClustered->ndarray(0.npy), person-vehicle-bike-detection-crossroad-1016, vehicle-license-plate-detection-barrier-0106, 300x300,PriorBoxClustered->ndarray(0.npy), 180x320,240x320,320x480,480x640,544x544,720x1280, YOLOX/nano,tiny,s,m,l,x,mot17,ablation/128x320,192x320,192x448,192x640,256x320,256x448,256x640,384x640,512x1280,736x1280, 180x320,240x320,270x480,360x480,360x480,360x640,480x640,720x1280, 180x320,256x320,320x480,352x352,352x640,480x640,736x1280, MediaPipe/camera,chair,chair_1stage,cup,sneakers,sneakers_1stage,ssd_mobilenetv2_oidv4_fp16, 3D BoundingBox estimation for autonomous driving, MobileNetV2/V3, 320x320,480x640,640x960,800x1280, Real-time Fine-Grained Estimation for Wide Range Head Pose, yolov5n_0.5,yolov5n_face,yolov5s_face/256x320,480x640,736x1280, 6D HeadPose,Multi-Model-Fused,224x224,PINTO's custom models, RGB,180x320,240x320,360x640,480x640,720x1280, MediaPipe,Integrate 058_BlazePose_Full_Keypoints, lightning,192x192,192x256,256x256,256x320,320x320,480x640,720x1280,1280x1920, 3D,192x192/256x256/320x320/416x416/480x640/512x512, 192x320,256x320,320x480,384x640,480x640,512x512,576x960,736x1280/Bottom-Up, Multi-Scale Local Planar Guidance for Monocular Depth Estimation, 128x160,224x224,256x256,256x320,320x320,480x640,512x512,768x1280, ddad/kitti,Convert all ResNet18 backbones only, kitti/nyu,192x320/256x320/368x640/480x640/720x1280, nyu,180x320/240x320/360x640/480x640/720x1280, 192x320,240x320,256x256,352x480,368x480,368x640,480x640,720x1280,1280x1920, Real-time-self-adaptive-deep-stereo (perform only inference mode, no-backprop, kitti), 180x320,216x384,240x320,270x480,360x480,360x640,480x640,720x1280, 192x320,256x320,256x832,384x640,480x640,736x1280, dpt-hybrid,480x640,ViT,ONNX 96x128/256x320/384x480/480x640, NVSmall_321x1025,NVTiny_161x513,ResNet18_321x1025,ResNet18_2d_257x513, finetune2_kitti/sceneflow,maxdisp192,320x480/480x640, kitti/nyu,320x320,320x480,480x640,640x800, Left/180x320,240x320,320x480,360x640,480x640, Stereo only/192x320,256x320,320x480,480x640, Stereo KITTI only/256x320,384x480,480x640,736x1280, Kitti,NYU/192x320,320x480,384x640,480x640,736x1280,non-commercial use only, 180x320,240x320,300x400,360x640,384x512,480x640,720x960,720x1280, sceneflow,kitti/240x320,320x480,384x640,480x640,544x960,720x1280, ITER2,ITER5,ITER10,ITER20/240x320,320x480,360x640,480x640,480x640,720x1280, 192x320,240x320,320x480,368x640,480x640,720x1280, 192x320,256x320,320x480,368x640,480x640,736x1280, 240x320,360x480,360x640,360x1280,480x640,720x1280, 384x384,384x576,384x768,384x960,576x768,768x1344, MediaPipe,MobileNet0.50/0.75/1.00,ResNet50, models_edgetpu_checkpoint_and_tflite_vision_segmentation-edgetpu_tflite_default_argmax, models_edgetpu_checkpoint_and_tflite_vision_segmentation-edgetpu_tflite_fused_argmax, PaddleSeg/modnet_mobilenetv2,modnet_hrnet_w18,modnet_resnet50_vd/256x256,384x384,512x512,640x640, 192x384,384x384,384x576,576x576,576x768,768x1344, RSB,VGG/240x320,256x320,320x480,360x640,384x480,384x640,480x640,720x1280, Mbnv3,ResNet50/192x320,240x320,320x480,384x640,480x640,720x1280,1088x1920,2160x3840, 21,53/180x320,240x320,320x480,360x640,480x640,720x1280, 180x320,240x320,320x480,360x640,480x640,540x960,720x1280,1080x1920, r50_giam_aug/192x384,384x384,384x576,384x768,576x576,576x768,768x1344, 180x320,240x320,320x480,360x640,480x640,720x1280,1080x1920,1080x2048,2160x4096,N-batch,Dynamic-HeightxWidth, Efficientnet_Anomaly_Detection_Segmentation, Fast_Accurate_and_Lightweight_Super-Resolution, Learning_to_See_Moving_Objects_in_the_Dark, Low-light Image Enhancement/40x40,80x80,120x120,120x160,120x320,120x480,120x640,120x1280,180x480,180x640,180x1280,180x320,240x320,240x480,360x480,360x640,480x640,720x1280, inception/mobilenetv2:256x256,320x320,480x640,736x1280,1024x1280, 16x16,32x32,64x64,128x128,240x320,256x256,320x320,480x640, sony/fuji, 240x320,360x480,360x640,480x640, 120x160,128x128,240x320,256x256,480x640,512x512, 64x64,96x96,128x128,256x256,240x320,480x640, Low-light Image/Video Enhancement,180x240,240x320,360x640,480x640,720x1280, Low-light Image/Video Enhancement,256x256,256x384,384x512,512x640,768x768,768x1280, DeBlur,DeNoise,DeRain/256x320,320x480,480x640, Low-light Image/Video Enhancement,180x320,240x320,360x640,480x640,720x1280, Low-light Image/Video Enhancement,180x320,240x320,360x640,480x640,720x1280,No-LICENSE, DeRain,180x320,240x320,360x640,480x640,720x1280, Dehazing,192x320,240x320,320x480,384x640,480x640,720x1280,No-LICENSE, DeBlur+SuperResolution,x4/64x64,96x96,128x128,192x192,240x320,256x256,480x640,720x1280, Low-light Image Enhancement/180x320,240x320,320x480,360x640,480x640,720x1280, Low-light Image Enhancement/192x320,240x320,320x480,368x640,480x640,720x1280, DeHazing/180x320,240x320,320x480,360x640,480x640,720x1280, Low-light Image Enhancement/180x320,240x320,320x480,360x640,480x640,720x1280,No-LICENSE, Low-light Image Enhancement/256x256,256x384,256x512,384x640,512x640,768x1280, Low-light Image Enhancement/180x320,240x320,320x480,360x640,480x640, DeHazing/192x320,240x320,320x480,360x640,480x640,720x1280,No-LICENSE, DeHazing/192x320,240x320,320x480,384x640,480x640,720x1280, DeBlur/180x320,240x320,320x480,360x640,480x640,720x1280,No-LICENSE, DeNoise/180x320,240x320,320x480,360x640,480x640,720x1280, x2,x4/64x64,96x96,128x128,160x160,180x320,240x320,No-LICENSE, Low-light Image Enhancement/180x320,240x320,320x480,480x640,720x1280,No-LICENSE, Low-light Image Enhancement/180x320,240x320,320x480,360x640,480x640,720x1280,academic use only, 2x,3x,4x/64x64,96x96,128x128,120x160,160x160,180x320,240x320, Low-light Image Enhancement/128x256,240x320,240x640,256x512,480x640,512x1024,720x1280, DeRain,DeHaizing,DeSnow/192x320,256x320,320x480,384x640,480x640,736x1280, v4_SPA,v4_rain100H,v4_rain1400/192x320,256x320,320x480,384x640,480x640,608x800,736x1280, Low-light Image Enhancement/192x320,256x320,320x480,384x640,480x640,544x960,720x1280, DeHaizing/192x320,256x320,384x640,480x640,720x1280,1080x1920,No-LICENSE, DeHaizing/192x320,240x320,384x480,480x640,512x512,720x1280,1088x1920, x4/64x64,96x96,128x128,120x160,160x160,180x320,192x192,256x256,180x320,240x320,360x640,480x640, Low-light Image Enhancement/180x320,240x320,360x480,360x640,480x640,720x1280, Skeleton-based/FineGYM,NTU60_XSub,NTU120_XSub,UCF101,HMDB51/1x20x48x64x64, Skeleton-based/Kinetics,NTU60,NTU120/1x3xTx25x2, DeRain/180x320,240x320,240x360,320x480,360x640,480x640,720x1280, PSD-Principled-Synthetic-to-Real-Dehazing-Guided-by-Physical-Priors, driver-action-recognition-adas-0002-encoder, driver-action-recognition-adas-0002-decoder, 192x320,256x320,320x480,384x640,480x640,736x1280, small,chairs,kitti,sintel,things/iters=10,20/240x320,360x480,480x640, 1x1x257x100,200,500,1000,2000,3000,5000,7000,8000,10000, L1,Style,VGG/256x256,180x320,240x320,360x640,480x640,720x1280,1080x1920, ResNet/128x320,192x320,192x448,192x640,256x320,256x448,256x640,320x448,384x640,480x640,512x1280,736x1280, chairs,kitti,things/iters=10,20/192x320,240x320,320x480,384x640,480x640,736x1280, anchor_HxW.npy/256x384,256x512,384x512,384x640,384x1024,512x640,768x1280,1152x1920, StereoDepth+OpticalFlow,/192x320,256x320,384x640,512x640,512x640,768x1280, Line Parsing/ALL/192x320,256x320,320x480,384x640,480x640,736x1280, Reflection-Removal/180x320,240x320,360x480,360x640,480x640,720x1280, 180x320,240x320,360x480,360x640,480x640,720x1280, OpticalFlow/192x320,240x320,320x480,360x640,480x640,720x1280, forgery detection/180x320,240x320,320x480,360x640,480x640,720x1280, Approximately 14FPS ~ 15FPS for all processes from pre-processing, inference, post-processing, and display, Approximately 12FPS for all processes from pre-processing, inference, post-processing, and display, [Model.1] MobileNetV2-SSDLite dm=0.5 300x300, Integer Quantization, [Model.2] Head Pose Estimation 128x128, Integer Quantization, Approximately 13FPS for all processes from pre-processing, inference, post-processing, and display, DeeplabV3-plus (MobileNetV2) Decoder 256x256, Integer Quantization, Approximately 8.5 FPS for all processes from pre-processing, inference, post-processing, and display, Tensorflow-GPU v1.15.2 or Tensorflow v2.3.1+. Work fast with our official CLI. SintelStereo(root[,pass_name,transforms]). Large-scale CelebFaces Attributes (CelebA) Dataset Dataset. MobileNetV2+DeeplabV3+coco/voc - Post-training quantization, 2-5. 29 datasets. to Implement the Frechet Inception Distance This paper presents a simple method for "do as I do" motion transfer: given a source video of a person dancing, we can transfer that performance to a novel (amateur) target after only a few minutes of the target subject performing standard moves. CVPR 2016. Simple Network Extraction for ONNX. While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. Gender Classifier in Google Colab using TensorFlow torchvision StarGAN v2: Diverse Image Synthesis for Multiple Domains To translate an image to another domain, we recombine its content code with a random style code sampled from the style space of the target domain. Carla simulator data linked in the CREStereo github repo. which can load multiple samples in parallel using torch.multiprocessing workers. CelebA-Spoof ; extensions (tuple[string]) A list of allowed extensions. 1021.2s - GPU P100. listlandmarksalign_celeba.csv: Image landmarks and their respective coordinates. Parameters: root (string) Root directory path. NVlabs/SPADE project, which has been established as PyTorch Project a Series of LF Projects, LLC. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation. Typically, Image Classification refers to images in which only one object appears and is analyzed. Keras: Learn to build neural networks and convolutional neural networks with Keras. A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or simply to separate onnx files to any size you want. Become familiar with generative adversarial networks (GANs) by learning how to build and train different GANs architectures to generate new images. Kitti2012Stereo(root[,split,transforms]). https://colab.research.google.com/drive/1TtCJ-uMNTArpZxrf5DCNbZdn08DsiW8F. CelebA (CelebA)20 VGGFace2 VGGFace2362 Discover, build, and train architectures such as DCGAN, CycleGAN, ProGAN, and StyleGAN on diverse datasets including the MNIST dataset, Summer2Winter Yosemite dataset, or CelebA dataset. It was released in 1999 and is used for classification tasks. Multimodal Unsupervised Image-To-Image Translation, Papers With Code is a free resource with all data licensed under, tasks/28ae529f-161e-4e8c-9230-765fe09aecc1.png, Unpaired Image-to-Image Translation See the original Large-scale CelebFaces Attributes Dataset. Classification CelebA Dataset [Tensorflow Lite] Various Neural Network Model quantization methods for Tensorflow Lite (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization, EdgeTPU). Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection. The blue line is "deeplab_mnv3_large_cityscapes_trainfine" loss. ECCV 2018. We describe a new training methodology for generative adversarial networks. For collecting images, we use more than 10 different input tensors, including phones, pads and personal computers (PC). face attributes provided with the CelebA database
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