About The Project This project focuses on CIFAR-10 object recognition in images using a simple Convolutional Neural Network (CNN) model. The CIFAR-10 dataset consists of 60,000 32x32 color images across 10 classes, such as airplanes, cars, birds, cats, and more. The goal is to build a ...
faster-rcnnface-detectionobject-detectionhuman-pose-estimationhuman-activity-recognitionmulti-object-trackinginstance-segmentationmask-rcnnyolov3deepsortfcosblazefaceyolov5detrpp-yolofairmotyoloxpicodetyolov7rt-detr UpdatedMar 28, 2025 Python extreme-assistant/CVPR2024-Paper-Code-Interpretation ...
Processing an Image Updated on2024-12-09 GMT+08:00 View PDF Share NOTICE: If you have any questions during development, post them on theIssuespage of GitHub. For details about parameters and usage of each API, seeAPI Reference. OBS can be used to process images in a stable, secure, ...
This API processes images in a stable, secure, efficient, easy to use, and cost-effective manner. If the object to be downloaded is an image, you can input the image proc
Be it Tesla Autopilot or Deebot vacuums, computing devices are fueled with novel generative AI algorithms to speed up processing powers and nomenclate physical objects. Known as object detection in a gist, this vision simulation designed with image recognition software has passed the baton of vision...
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9729-9738). [5]. Cui, Y. et al. Revisiting pre-trained models for chinese natural language processing. In Conference on Empirical Methods in Natural Language Processing: Findings, 657{668 (2020). [6]...
R-FCN: Object Detection via Region-based Fully Convolutional Networks. Jifeng Dai, Yi Li, Kaiming He, and Jian Sun. Conference on Neural Information Processing Systems (NIPS), 2016. Deep Residual Learning for Image Recognition. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. IEEE Confer...
Computer Vision and Pattern Recognition Jun 2020 Existing state-of-the-art RGB-D salient object detection methods explore RGB-D data relying on a two-stream architecture, in which an independent subnetwork is required to process depth data. This inevitably incurs extra computational costs and memory...
deep learning for image processing including classification and object-detection etc. - zhhongsh/deep-learning-for-image-processing
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