技术标签:变化检测ChangeNetChangeDetection孪生神经网络 查看原文 系统介绍|围填海项目遥感监视监测系统介绍4——影像变化检测 类型、面积、百分比等变化矩阵信息,同时可得到一个单波段的变化分类图像和变化矢量结果。 图1 ENVI变化监测流程工具 2、遥感影像批量变化检测本研究使用ENVI提供的直接比较法变化检测方法进行填海范...
技术标签:变化检测ChangeNet-GANGAN 查看原文 【文献阅读】用GAN来做遥感图像的变化检测(M. A. Lebedev等人,ISPRS,2018) real season-varying remote sensingimages.一篇用CGAN来做变化检测的文章。变化检测试验包含三种,没有目标相对变化的合成图像的变化检测,目标相对较小变化的合成图像的变化检测,和真实季节变化遥感图...
Visual ChangeNet-Classification is an NVIDIA-developed classification change detection model and is included in the TAO Toolkit. Visual ChangeNet supports the following tasks: train evaluate inference export These tasks can be invoked from the TAO Toolkit Launcher using the following convention on the co...
tao deploy visual_changenet inference -e /path/to/spec.yaml \ results_dir=$RESULTS_DIR \ inference.trt_engine=/path/to/engine/file \ model.classify.eval_margin=0.5 Required Arguments -e, --experiment_spec_file: The path to the experiment spec file. This should be the same as the tao...
Visual ChangeNet-SegmentationSiamese Network204893.7589.7884.791.7299.17 Real-time Inference Performance The inference is run on the provided unpruned model at FP16 precision. The inference performance is run usingtrtexecon Jetson AGX Xavier, Xavier NX, Orin, Orin NX and NVIDIA T4, and Ampere GPUs. ...
checkpoint: ${results_dir}/train/changenet_classify.pth onnx_file: ${results_dir}/export/changenet-classify.onnx on_cpu: false input_channel: 3 input_width: 128 input_height: 512 opset_version: 12 batch_size: ${dataset.classify.batch_size} verbose: false gen_trt_engine: results...
Visual ChangeNet-SegmentationSiamese Network68688.6485.977.8887.1595.77 Real-time Inference Performance The inference is run on the provided unpruned model at FP16 precision. The inference performance is run usingtrtexecon Jetson AGX Xavier, Xavier NX, Orin, Orin NX and NVIDIA T4, and Ampere GPUs. ...
Visual ChangeNet-Segmentation Data Input for VisualChangeNet Creating a Training Experiment Spec File train Model Dataset Training the Model Creating Testing Experiment Spec File Evaluating the Model Running Inference on the Model Exporting the Model TensorRT Engine Generation, Validation, and int8 ...
tao deploy visual_changenet gen_trt_engine -e /path/to/spec.yaml \ results_dir=/path/to/result_dir \ gen_trt_engine.onnx_file=/path/to/onnx/file \ gen_trt_engine.trt_engine=/path/to/engine/file \ gen_trt_engine.tensorrt.data_type=<data_type> Required Arguments -e, --experiment...
VisualChangeNet does not support INT8 calibration. trtexec --onnx=/path/to/model.onnx \ --maxShapes=input_1:16x3x400x100,input_2:16x3x400x100 \ --minShapes=input_1:1x3x400x100,input_2:1x3x400x100 \ --optShapes=input_1:8x3x400x100,input_2:8x3x400x100 \ --fp16 \ --saveEngine=...