![]() The catalytic residues of OspI and AvrPphB are shown as sticks, colored in blue and grey, respectively. OspI with that of AvrPphB (pdb ID: 1UKF). (B) Structural superimposition of the catalytic residues of wild-type. The residues and the secondary structures undergoing conformational changes are labeled as indicated. (A) Structural superimposition between Ubc13-bound (green) and wild-type free OspI (blue, pdb ID: 3B21). Conformational Changes of OspI upon Ubc13 Binding and Structural Comparison of the Catalytic Residues of OspI and AvrPphB. Loss ( 'CrossEntropy' ) smoothl1_metric = mx. collect_params (), 'sgd', ) mbox_loss = gcv. We can load dataset using RecordFileDetection Computing FLOPS, latency and fps of a model Extracting video features from pre-trained models Fine-tuning SOTA video models on your own dataset Getting Started with Pre-trained I3D Models on Kinetcis400 Distributed training of deep video models Train classifier or detector with HPO using GluonCV Auto task Train Image Classification with Auto Estimator ![]() Load web datasets with GluonCV Auto Module Prepare your dataset in ImageRecord format.Prepare the 20BN-something-something Dataset V2.Prepare custom datasets for object detection.Testing PoseNet from image sequences with pre-trained Monodepth2 Pose models ![]() Predict depth from an image sequence or a video with pre-trained Monodepth2 models Predict depth from a single image with pre-trained Monodepth2 models Multiple object tracking with pre-trained SMOT models Single object tracking with pre-trained SiamRPN models Inference on your own videos using pre-trained models Dive Deep into Training SlowFast mdoels on Kinetcis400 Getting Started with Pre-trained SlowFast Models on Kinetcis400 Dive Deep into Training I3D mdoels on Kinetcis400 Dive Deep into Training TSN mdoels on UCF101 Introducing Decord: an efficient video reader Getting Started with Pre-trained TSN Models on UCF101 Dive deep into Training a Simple Pose Model on COCO Keypoints Predict with pre-trained AlphaPose Estimation models Predict with pre-trained Simple Pose Estimation models Test with ICNet Pre-trained Models for Multi-Human Parsing Getting Started with FCN Pre-trained Models Predict with pre-trained Mask RCNN models Run an object detection model on NVIDIA Jetson module Predict with pre-trained CenterNet models Skip Finetuning by reusing part of pre-trained model Run an object detection model on your webcam Train Faster-RCNN end-to-end on PASCAL VOC Deep dive into SSD training: 3 tips to boost performance Predict with pre-trained Faster RCNN models ![]() Transfer Learning with Your Own Image Dataset Getting Started with Pre-trained Models on ImageNet Getting Started with Pre-trained Model on CIFAR10 ![]()
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