Download here. Classification Synthetic Dataset Classification. all 15, Deep Residual Learning for Image Recognition, Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, Image-to-Image Translation with Conditional Adversarial Networks, StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation, U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation, Semantic Image Synthesis with Spatially-Adaptive Normalization, High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs, Multimodal Unsupervised Image-to-Image Translation, StarGAN v2: Diverse Image Synthesis for Multiple Domains. junyanz/pytorch-CycleGAN-and-pix2pix 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. Face Generation 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. # models/research/deeplab/core/feature_extractor.py, # change this as per how you have saved the model, # change input_image to node.name if you know the name, 'Optimized graph converted to SavedModel! 35 benchmarks The resulting directory structure should be: As mentioned, the discriminator, \(D\), is a binary classification network that takes an image as input and outputs a scalar probability that the input image is real (as opposed to fake). It is not necessary if all procedures described in Google Colaboratory are performed in a PC environment. Image MobileNetv1v2 2. Image Classification is a fundamental task that attempts to comprehend an entire image as a whole. Logs. We present a class of efficient models called MobileNets for mobile and embedded vision applications. Large-scale CelebFaces Attributes (CelebA) Dataset, German Traffic Sign Recognition Benchmark (GTSRB). nvlabs/MUNIT 2717 papers with code The goal is to classify the image by assigning it to a specific label. face attributes provided with the CelebA database **Image Classification** is a fundamental task that attempts to comprehend an entire image as a whole. clovaai/stargan-v2 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. torchvision Torchvision master documentation MobileNetV2+DeeplabV3+coco/voc - Post-training quantization, 2-5. As a next step, you might like to experiment with a different dataset, for example the Large-scale Celeb Faces Attributes (CelebA) dataset available on Kaggle. KITTI dataset from the 2015 stereo evaluation benchmark. CelebFaces Attributes dataset contains 202,599 face images of the size 178218 from 10,177 celebrities, each annotated with 40 binary labels indicating facial attributes like hair color, gender and age. Work fast with our official CLI. all 36, Deep Residual Learning for Image Recognition, Very Deep Convolutional Networks for Large-Scale Image Recognition, MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, CSPNet: A New Backbone that can Enhance Learning Capability of CNN, MobileNetV2: Inverted Residuals and Linear Bottlenecks, Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization, An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks, Rethinking the Inception Architecture for Computer Vision. For training data, each category contains a huge number of images, ranging from around 120,000 to . CVPR 2018. In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Classification An experiment with two annotators independently assigning values shows that only 12 of 40 commonly-used attributes are assigned values with >= 95% consistency, and that three (high cheekbones, pointed nose, oval face) have random consistency (50%). To address this limitation, we propose StarGAN, a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model. MobileNetV3+DeeplabV3+Cityscaps - Quantization-aware training, 2-3-1. 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. Comments (1) Run. The LSUN classification dataset contains 10 scene categories, such as dining room, bedroom, chicken, outdoor church, and so on. NVlabs/SPADE Confirm the structure of saved_model ssd_mobilenet_v3_large_coco_2019_08_14, 2-5-5. 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+. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML. please see www.lfprojects.org/policies/. 17 Apr 2017. Logs. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs). 2 input and 1 output. The images in this dataset cover large pose variations and background clutter. On aarch64 OS, performance is about 4 times higher than on armv7l OS. CVPR 2018. Histogram A generic data loader where the images are arranged in this way by default: . If nothing happens, download Xcode and try again. tensorflow/tensorflow A tag already exists with the provided branch name. CelebA 128 x 128 COCO-GAN See all. CelebA has large diversities, large quantities, and rich annotations, including 10,177 number of identities, 202,599 number of face images, and 5 arrow_right_alt. tensorflow/tpu Are you sure you want to create this branch? **** DQ = Dynamic Range Quantization. Hence, they can all be passed to a torch.utils.data.DataLoader CVPR 2019. transform and target_transform to transform the input and target respectively. to Implement the Frechet Inception Distance 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. - mobilenet_v3_large_integer_quant.tflite", './ssd_mobilenet_v3_large_coco_2019_08_14/mobilenet_v3_large_full_integer_quant.tflite', "Full Integer Quantization complete! 10 Face Datasets To Start Facial Recognition Projects 147 benchmarks They all have two common arguments: Dataset interface for Scene Flow datasets. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. ICCV 2017. ICLR 2020. Country211(root[,split,transform,]), DTD(root[,split,partition,transform,]), EuroSAT(root[,transform,target_transform,]), FakeData([size,image_size,num_classes,]), A fake dataset that returns randomly generated images and returns them as PIL images, FashionMNIST(root[,train,transform,]), Flickr8k(root,ann_file[,transform,]), Flickr30k(root,ann_file[,transform,]), Flowers102(root[,split,transform,]). How to restore Tensorflow model from .pb file in python? Celeba; Overview: Image Dataset based on the Large-scale CelebFaces Attributes Dataset; Details: 9343 users (we exclude celebrities with less than 5 images) Task: Image Classification (Smiling vs. Not smiling) Synthetic Dataset; Overview: We propose a process to generate synthetic, challenging federated datasets. * WQ = Weight Quantization CelebA (CelebFaces Attributes Dataset) Introduced by Liu et al. Datasets Torchvision 0.14 documentation CVPR 2017. LSUN LSUN (The Large-scale Scene Understanding) contains close to one million labeled images for each of 10 scene categories and 20 object categories. CelebA-Spoof has several appealing properties. StarGAN v2: Diverse Image Synthesis for Multiple Domains Large-scale CelebFaces Attributes (CelebA) Dataset Dataset. KITTI dataset from the 2012 stereo evaluation benchmark. Caltech101(root[,target_type,transform,]). LSUN ATTRIBUTE CLASSIFICATION ERROR ON CELEBA. This CVPR 2017. ; loader (callable) A function to load a sample given its path. We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Generate saved_model, tfjs, tf-trt, EdgeTPU, CoreML, quantized tflite, ONNX, OpenVINO, Myriad Inference Engine blob and .pb from .tflite. Simple tool to combine onnx models. google-research/vision_transformer Some tasks are inferred based on the benchmarks list. There are 5 landmarks: left eye, right eye, nose, left mouth, right mouth; listattrceleba.csv: Attribute labels for each image. LEAF - Carnegie Mellon University NVIDIA/pix2pixHD ; extensions (tuple[string]) A list of allowed extensions. tensorflow/models Torchvision provides many built-in datasets in the torchvision.datasets module, as well as utility classes for building your own datasets.. Built-in datasets. Keras: Learn to build neural networks and convolutional neural networks with Keras.
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