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advanced-perception-stock-price-predict

automl

Google Brain AutoML

awesome-crowd-counting

Awesome Crowd Counting

awesome-phenotyping-deep-learning

awesome deep learning for field phenotyping

ceit-transformers

contrastive_sensor_fusion

Open-source code for the paper "Representation Learning for Remote Sensing: An Unsupervised Sensor Fusion Approach ".

convd

c2f to FasterNet

cotton-phenology-dataset

cottonweeds-

深度迁移学习在棉花生产系统中常见杂草多类识别中的性能评价

counting-seedlings

codes necessary for the replication of the article's experiments

data-augmented-deep-learning-model-for-abnormal-road-manhole-cover-detection

dcw

DeepWeed Benchmark 一种用于棉花生产系统中多类杂草检测的YOLO目标检测器的新基准

deep-learning-for-image-processing

deep learning for image processing including classification and object-detection etc.

deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

deepseedling

深度幼苗:基于深度卷积网络和卡尔曼滤波的田间植物幼苗检测和计数

deepweeds

A Multiclass Weed Species Image Dataset for Deep Learning

edcstfn

An Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion

edx-app-

edx-app-android

edx-app-android

edx-app-android-2

edx-app-android3

ema-attention-module

Implementation Code for the ICCASSP 2023 paper " Efficient Multi-Scale Attention Module with Cross-Spatial Learning" and is available at: https://arxiv.org/abs/2305.13563v2

emergence-evaluation-framework

使用基于无人机的图像和深度学习评估棉花出苗率 A framework for cotton seedlings emergence evaluation (stand count and canopy size mapping)

exquisitenetv2

faster-rcnn-keras

这是一个faster-rcnn的keras实现的库，可以利用voc数据集格式的数据进行训练。

focal-eiou

Implementation of the Focal EIoU for the YOLOv8. Replace the following files in the ultralytics/utils folder. Set Focal=False in the case of EIoU and for Focal-EIoU set Focal=True

free-shared-spatiotemporal-method-of-remote-sensing-

spatiotemporal fusion method

ganstfm

A Flexible Spatiotemporal Fusion Model for Remote Sensing Images With Conditional Generative Adversarial Network

gittset

test 4 git repository

graph-signal-processing

Graph Signal Processing using pygsp