All Projects → cwfid → Dataset

cwfid / Dataset

Crop/Weed Field Image Dataset

Projects that are alternatives of or similar to Dataset

Pointclouddatasets
3D point cloud datasets in HDF5 format, containing uniformly sampled 2048 points per shape.
Stars: ✭ 80 (-18.37%)
Mutual labels:  dataset, classification, segmentation
All About The Gan
All About the GANs(Generative Adversarial Networks) - Summarized lists for GAN
Stars: ✭ 630 (+542.86%)
Mutual labels:  classification, paper, segmentation
Labelme
Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
Stars: ✭ 7,742 (+7800%)
Mutual labels:  classification, annotations
Gd Uap
Generalized Data-free Universal Adversarial Perturbations
Stars: ✭ 50 (-48.98%)
Mutual labels:  classification, segmentation
Pointcnn
PointCNN: Convolution On X-Transformed Points (NeurIPS 2018)
Stars: ✭ 1,120 (+1042.86%)
Mutual labels:  classification, segmentation
Body reconstruction references
Paper, dataset and code collection on human body reconstruction
Stars: ✭ 96 (-2.04%)
Mutual labels:  dataset, paper
Awesome Project Ideas
Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas
Stars: ✭ 6,114 (+6138.78%)
Mutual labels:  dataset, classification
Pytorch Classification Uncertainty
This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"
Stars: ✭ 59 (-39.8%)
Mutual labels:  classification, paper
Multi Human Parsing
🔥🔥Official Repository for Multi-Human-Parsing (MHP)🔥🔥
Stars: ✭ 507 (+417.35%)
Mutual labels:  segmentation, annotations
Recursive Cnns
Implementation of my paper "Real-time Document Localization in Natural Images by Recursive Application of a CNN."
Stars: ✭ 80 (-18.37%)
Mutual labels:  dataset, paper
Openml R
R package to interface with OpenML
Stars: ✭ 81 (-17.35%)
Mutual labels:  dataset, classification
Label Studio
Label Studio is a multi-type data labeling and annotation tool with standardized output format
Stars: ✭ 7,264 (+7312.24%)
Mutual labels:  dataset, annotations
Caffe Model
Caffe models (including classification, detection and segmentation) and deploy files for famouse networks
Stars: ✭ 1,258 (+1183.67%)
Mutual labels:  classification, segmentation
Awesome Face
😎 face releated algorithm, dataset and paper
Stars: ✭ 739 (+654.08%)
Mutual labels:  dataset, paper
Cvat
Powerful and efficient Computer Vision Annotation Tool (CVAT)
Stars: ✭ 6,557 (+6590.82%)
Mutual labels:  dataset, annotations
Php Ml
PHP-ML - Machine Learning library for PHP
Stars: ✭ 7,900 (+7961.22%)
Mutual labels:  dataset, classification
Lidar Bonnetal
Semantic and Instance Segmentation of LiDAR point clouds for autonomous driving
Stars: ✭ 465 (+374.49%)
Mutual labels:  dataset, segmentation
Tensorflow object tracking video
Object Tracking in Tensorflow ( Localization Detection Classification ) developed to partecipate to ImageNET VID competition
Stars: ✭ 491 (+401.02%)
Mutual labels:  dataset, classification
Deep Segmentation
CNNs for semantic segmentation using Keras library
Stars: ✭ 69 (-29.59%)
Mutual labels:  dataset, segmentation
Dlcv for beginners
《深度学习与计算机视觉》配套代码
Stars: ✭ 1,244 (+1169.39%)
Mutual labels:  classification, segmentation

A Crop/Weed Field Image Dataset

The Crop/Weed Field Image Dataset (CWFID) accompanies the following publication: "Sebastian Haug, Jörn Ostermann: A Crop/Weed Field Image Dataset for the Evaluation of Computer Vision Based Precision Agriculture Tasks, CVPPP 2014 Workshop, ECCV 2014"

This dataset comprises field images, vegetation segmentation masks and crop/weed plant type annotations. The paper provides details, e.g. on the field setting, acquisition conditions, image and ground truth data format.

You can download the complete dataset here: Download CWFID.

Paper

Paper available here.

Bibtex:

@inproceedings{haug15,
  author={Haug, Sebastian and Ostermann, J{\"o}rn},
  title={A Crop/Weed Field Image Dataset for the Evaluation of Computer Vision Based Precision Agriculture Tasks},
  year={2015},
  booktitle={Computer Vision - ECCV 2014 Workshops},
  doi={10.1007/978-3-319-16220-1_8},
  url={http://dx.doi.org/10.1007/978-3-319-16220-1_8},
  pages={105--116},
}

Use

All data is subject to copyright and may only be used for non-commercial research. In case of use please cite our publication.

Contact Sebastian Haug ([email protected]) for any questions.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].