### Datasets:
59.26TB of research data: http://academictorrents.com/
ImageNet Torrent: http://academictorrents.com/browse.php?search=imagenet&page=0
25 thousand datasets on Kaggle: https://www.kaggle.com/datasets
BDD100K - Diverse Driving Video: https://bair.berkeley.edu/blog/2018/05/30/bdd/
Pascal VOC: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html
MS COCO: http://cocodataset.org/#download
ImageNet: http://imagenet.stanford.edu/download.php
ImageNet (ILSVRC2012): http://www.image-net.org/challenges/LSVRC/2012/nonpub-downloads
ImageNet (ILSVRC2015): http://image-net.org/small/download.php
ImageNet VID: http://bvisionweb1.cs.unc.edu/ilsvrc2015/download-videos-3j16.php
Open Images: https://storage.googleapis.com/openimages/web/download.html
Cityscapes: https://www.cityscapes-dataset.com/
Object Tracking Benchmark: http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html
MOT (Multiple object tracking benchmark): https://motchallenge.net/
VOT (Visual object tracking): http://www.votchallenge.net/challenges.html
FREE FLIR Thermal Dataset (infrared): https://www.flir.eu/oem/adas/adas-dataset-form/
MARS: http://www.liangzheng.com.cn/Project/project_mars.html
Market-1501: http://www.liangzheng.org/Project/project_reid.html
German Traffic Sign Recognition Benchmark: http://benchmark.ini.rub.de/
Labeled Faces in the Wild: http://vis-www.cs.umass.edu/lfw/
Core50: https://vlomonaco.github.io/core50/
Visual Question Answering: https://visualqa.org/download.html
Large Movie Review Dataset: http://ai.stanford.edu/~amaas/data/sentiment/
KITTI (for autonomous driving): http://www.cvlibs.net/datasets/kitti/
nuScenes (for autonomous driving): https://www.nuscenes.org/overview
----
Wikipedia's List of datasets: https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research
Other datasets (Music, Natural Images, Artificial Datasets, Faces, Text, Speech, Recommendation Systems, Misc): http://deeplearning.net/datasets/
25 datasets: https://www.analyticsvidhya.com/blog/2018/03/comprehensive-collection-deep-learning-datasets/
List of datasets: https://riemenschneider.hayko.at/vision/dataset/index.php
Another list of datasets: http://homepages.inf.ed.ac.uk/rbf/CVonline/Imagedbase.htm
Pedestrian DATASETs for Vision based Detection and Tracking: https://hemprasad.wordpress.com/2014/11/08/pedestrian-datasets-for-vision-based-detection-and-tracking/
TrackingNet: https://tracking-net.org/
RGB, RGBD, Texture-mapped 3D mesh models: http://www.ycbbenchmarks.com/
没有合适的资源?快使用搜索试试~ 我知道了~
温馨提示
在日益增长的城市安全需求中,及时发现并响应火灾事故变得至关重要。为此,我们开发了一套基于深度学习的火灾与烟雾智能检测系统,利用先进的YOLO(You Only Look Once)目标检测算法,旨在实时、准确地识别出火灾和烟雾的迹象,从而大幅度提升公共场所的安全性和响应效率。 本系统的核心优势在于其快速且精准的目标检测能力。YOLO算法能够直接从图像中同时预测物体的位置和类别,无需依赖复杂的区域提议网络,这使得我们的系统能够在毫秒级的时间内完成对监控视频流的分析,即时警报潜在的火灾风险。此外,通过大量的标注数据训练,我们的模型已经具备了高度的泛化能力,即使在光线条件不佳或复杂背景的情况下也能保持高精度的检测效果。 为了适应不同的应用场景,我们还设计了灵活的部署方案。无论是室内环境如商场、医院、学校,还是室外场所如森林、仓库,只需简单配置即可将系统接入现有的监控网络。同时,系统支持云端和边缘计算,可根据实际需求选择最合适的部署模式,确保在任何情况下都能提供稳定可靠的服务。 除了技术上的创新,我们还重视系统的用户友好性。直观的图形界面让非专业人员也能轻松操作,实时的数据可视化工具则帮
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yolo实现的火灾检测,烟雾检测系统项目 (330个子文件)
gemm.c 103KB
parser.c 84KB
data.c 73KB
detector.c 72KB
convolutional_layer.c 59KB
conv_lstm_layer.c 53KB
image.c 47KB
network.c 45KB
classifier.c 44KB
yolo_layer.c 39KB
gaussian_yolo_layer.c 36KB
box.c 29KB
go.c 26KB
lstm_layer.c 25KB
utils.c 22KB
region_layer.c 22KB
blas.c 22KB
darknet.c 19KB
dark_cuda.c 16KB
batchnorm_layer.c 16KB
gru_layer.c 15KB
coco.c 14KB
connected_layer.c 14KB
maxpool_layer.c 14KB
rnn.c 14KB
crnn_layer.c 14KB
demo.c 14KB
getopt.c 13KB
layer.c 12KB
shortcut_layer.c 12KB
yolo.c 12KB
detection_layer.c 12KB
activations.c 11KB
captcha.c 11KB
compare.c 11KB
softmax_layer.c 10KB
rnn_layer.c 10KB
nightmare.c 9KB
kmeansiou.c 9KB
local_layer.c 9KB
cifar.c 8KB
matrix.c 8KB
rnn_vid.c 7KB
deconvolutional_layer.c 6KB
normalization_layer.c 6KB
route_layer.c 5KB
scale_channels_layer.c 5KB
voxel.c 5KB
tag.c 4KB
writing.c 4KB
cost_layer.c 4KB
im2col.c 4KB
super.c 4KB
tree.c 4KB
dice.c 4KB
col2im.c 4KB
sam_layer.c 3KB
option_list.c 3KB
upsample_layer.c 3KB
reorg_layer.c 3KB
reorg_old_layer.c 3KB
dropout_layer.c 3KB
crop_layer.c 3KB
swag.c 2KB
cpu_gemm.c 2KB
avgpool_layer.c 2KB
list.c 2KB
activation_layer.c 2KB
art.c 2KB
gettimeofday.c 1KB
yolov4-fire.cfg 12KB
FindPThreads_windows.cmake 4KB
FindCUDNN.cmake 3KB
FindStb.cmake 701B
win_get_imagenet_valid.cmd 1KB
win_get_imagenet_train_48hours.cmd 725B
win_get_otb_datasets.cmd 559B
win_cifar.cmd 435B
win_install_cygwin.cmd 268B
run_log_parser_windows.cmd 187B
image_opencv.cpp 51KB
yolo_console_dll.cpp 29KB
http_stream.cpp 24KB
yolo_v2_class.cpp 12KB
blas_kernels.cu 85KB
im2col_kernels.cu 84KB
convolutional_kernels.cu 59KB
network_kernels.cu 25KB
activation_kernels.cu 24KB
maxpool_layer_kernels.cu 11KB
dropout_layer_kernels.cu 11KB
crop_layer_kernels.cu 7KB
col2im_kernels.cu 6KB
deconvolutional_kernels.cu 4KB
avgpool_layer_kernels.cu 2KB
darknet 6.3MB
fire.data 243B
vcpkg_windows_cuda.diff 67B
vcpkg_linux_cuda.diff 65B
vcpkg_windows.diff 56B
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