Pinned Repositories
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 ".
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)
LeViT-transformers
LeViT a Vision Transformer in ConvNet's Clothing for Faster Inference
vit-gpt2-image-captioning-api
Yet-Another-EfficientDet-Pytorch
The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.
neukaren's Repositories
neukaren/advanced-perception-stock-price-predict
neukaren/automl
Google Brain AutoML
neukaren/contrastive_sensor_fusion
Open-source code for the paper "Representation Learning for Remote Sensing: An Unsupervised Sensor Fusion Approach ".
neukaren/convd
c2f to FasterNet
neukaren/cotton-phenology-dataset
neukaren/Counting-seedlings
codes necessary for the replication of the article's experiments
neukaren/Data-Augmented-Deep-Learning-Model-for-Abnormal-Road-Manhole-Cover-Detection
neukaren/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
neukaren/deep-learning-for-image-processing
deep learning for image processing including classification and object-detection etc.
neukaren/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scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
neukaren/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
neukaren/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。
neukaren/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
neukaren/HR-FPN
neukaren/Improved_-yolov5
his paper proposes a method based on YOLOv5s with an improved attention mechanism, which can accurately detect the number of small-scale wheat spikes and better solve the problems of occlusion and cross-overlapping of wheat spike
neukaren/labelImg-
Label SLabel Studio是最灵活的开源数据标记工具，可用于图像、文本、超文本、音频、视频和时间序列数据
neukaren/lstm_vs_transformer
neukaren/mmdetection
OpenMMLab Detection Toolbox and Benchmark
neukaren/models
Models and examples built with TensorFlow
neukaren/Neural-Network-Architecture-Diagrams
Diagrams for visualizing neural network architecture (Created with diagrams.net)
neukaren/openvino_notebooks
📚 Jupyter notebook tutorials for OpenVINO™
neukaren/Past-and-present-small-object-detection
Latest paper about small object detection
neukaren/SOC-YOLO
neukaren/tensorflow
An Open Source Machine Learning Framework for Everyone
neukaren/trichomecounter
Trichomecounter
neukaren/vit-gpt2-image-captioning
neukaren/WIoU
WIoU implementation for the YOLOv8
neukaren/yolo_tracking
A collection of SOTA real-time, multi-object trackers for object detectors
neukaren/yolov5
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
neukaren/yolov8-with-focal_loss
Modify loss function of YOLOv8 model