Tsinghua dogs dataset Image Classi cation: A Dataset and Baselines Yue He a,1, Zheyan Shen , Peng Cui a,2, aLab of Media and Network, Room 9-316, East Main Building, Tsinghua University, Beijing 100084, P. Cats vs. Experiments show that our method has superior detection performance and is quicker than the general faster RCNN object detection framework on both datasets. In comparison to previous Upload an image to customize your repository’s social media preview. In 2012 IEEE Conference on Computer Vision and Pattern Towards Non-I. 0rc0-cp27-none-linux_x86_64. Fig. Dataset Watermarking A Pytorch image classification using the Stanford Dogs dataset to classify an image of a dog to one of 120 breeds - zrsmithson/Stanford-dogs The Stanford Dogs Dataset contains images of 120 breeds of dogs from around the world. cn). TensorFlow入门：给小狗分类 0 关于本文. 清华狗是一个细粒度的狗分类数据集，超过65% 的图像是从人们的现实生活中收集的。数据集中的每个犬种至少包含200张图像，最多包含7,449张图像，基本上与它们在中国的出现频率成比例，因此与现有数据集相比，它显着增加了每个犬种的多样性。 Networks to classify Tsinghua Dogs Dataset \n. g. It is competitive with state-of-the-art specialist traffic sign 数据集介绍 简介. We have discussed these points in the 'Discussion' section. In addition, the library provides DOI registration services for research data to scientific research institutes across the country. The dataset is provided in this link. 4k rows. cn/ThuDogs/; accessed on 22 October 2024), or extend its use to images of other animals. x版本的TF安装包安装即可. cn, xiast@sz. Specifically, the 训练二分类模型，熟悉数据读取机制，并且从kaggle中下载猫狗二分类训练数据，编写一个DogCatDataset，使得pytorch可以对猫狗二分类训练集进行读取 - ZzyChris97/cat-and-dog-binary-classification 斯坦福犬数据集包含来自世界各地的120种犬类的图像。该数据集是使用ImageNet中的图像和注释方法构建的，常用于细粒度图像 使用feature_extract. tuna. It has only one dog in each image and provides annotated Tsinghua Dogs is a fine-grained classification dataset for dogs, over 65% of whose images are collected from people's real life. 2 Institute for Brain and Cognitive Sciences, Tsinghua University, Animals Brain* / physiology The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world. This dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. It is currently the largest dataset for fine-grained classification of dogs, including 130 AI Studio是基于百度深度学习平台飞桨的人工智能学习与实训社区，提供在线编程环境、免费GPU算力、海量开源算法和开放数据，帮助开发者快速创建和部署模型。 狗细分类数据集Tsinghua Dogs（清华大学计算机系图形学实验室于2020年CVM论文中公开，目前世界上最大）。 Tsinghua Dogs包含了130个 The Oxford-IIIT Pet Dataset Oxford-IIIT宠物数据集是一个37类宠物数据集，每个数据类大约有100张图像，由牛津大学的Visual Geometry Group 天池实验室 Tsinghua Dogs Dataset with ground truth labels for breeds in YOLOv5 format. 04141的LogLoss得分，全球排名第20。 13. Learn more. 数据集介绍 简介. cn/ThuDogs/ has also bounding boxes and other annotations. Extensive experiments show that our [45] Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. 3. Tao Wei is with Ant Group, Hangzhou, Zhejiang, China (e-mail: Rare Disease Data Center (RDDC), co-established by Artificial Intelligence Innovation Center of the Research Institute of Tsinghua, Pearl River Delta and Cyagen Biosciences, integrates open-source data from both domestic and international sources, including epidemiology, drug development, disease-related gene maps, gene mutation sites, and mouse models. cs. We have tested our method both on the dataset we have built and the Tsinghua-Tencent 100K (TT100K) traffic sign benchmark. Something went wrong Tsinghua Dogs is a fine-grained classification dataset for dogs, over 65% of whose images are collected from people's real life. 本文的目标是TF的Getting Start. Sign in tsinghua_dogs. Queries are real SQL statements that support various functionalities, such as feature extraction ( ), transactions ( ), and analytical queries (coming soon). \n Requirements \n In this paper, we introduce an image dataset for fine-grained classification of dog breeds: the Tsinghua Dogs Dataset. 1 hypothesis between training and testing data is the basis of numerous image classi cation methods. 2 Data annotation with active learning Manual labeling using tools such as Abstract In this paper, we introduce an image dataset for fine-grained classification of dog breeds: the Tsinghua Dogs Dataset. python android-application stanford-dogs-dataset tensorflow-lite tflite dog-breeds Updated Jun 11, 2021 This paper presents an adaptive traffic control system based on deep convolution neural network (DCNN) technique for a multimodal traffic environment. In comparison to 当前内容阅读耗时约8分钟，试试. cn/ThuDogs/ (accessed on 22 October 2024)), which contains 70,428 images of 130 breeds, to increase the variety and quantity of image data, thereby enhancing the model’s generalization Although there are several publicly available image-based datasets for animal breed classification, such as the Stanford Dogs dataset [4], Oxford IIIT-Pet Dataset [5], and Tsinghua Dogs Dataset [6 AI Studio是基于百度深度学习平台飞桨的人工智能学习与实训社区，提供在线编程环境、免费GPU算力、海量开源算法和开放数据，帮助开发者快速创建和部署模型。 Saved searches Use saved searches to filter your results more quickly Mentioning: 6 - In this paper, we introduce an image dataset for fine-grained classification of dog breeds: the Tsinghua Dogs Dataset. dog, cat) and construct a new dataset using 10 instan-tiated subclasses (e. Source: Universal-to-Specific Framework for Complex Action Recognition. 当前版本： v1 03-28 01:09:26 In this paper, we introduce an image dataset for fine-grained classification of dog breeds: the Tsinghua Dogs Dataset. 1 环境搭建(CPU版本为例) 在Linux下, 环境的部署较为简单, 下载 Python2 或者Python3. cn Peng Cui Tsinghua University (e. Stanford Dogs数据集包含来自世界各地的120种犬种的图像。 该数据集是使用ImageNet中的图像和注释构建的，用于细粒度图像分类的任务。 它最初被收集用于细粒图像分类，这是一个具有挑战性的问题，因为某些犬种具有接近相同的特征或者颜色和年龄不同。 To address this, we present a mobile robot oriented large- scale indoor dataset, denoted as THUD (Tsinghua University Dynamic) robotic dataset, for training and evaluating their dynamic scene understanding algorithms. , 2021; Zhao, Weng & Hersperger, 2020). Such property can hardly be guaranteed in Tsinghua University library provides consulting services on the retrieval, sharing, publishing,and dissemination of scientific research data for teachers and students at the university. Over 20,000 images of 120 dog breeds. In its implementation, two variants of CNN are used to be compared, ResNet 50 and ResNet 101, using the same configuration. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. nass59 / dog-breed-classifier. Cats数据集进行猫狗分类。通过数据预处理、导出多模型特征向量、构建和训练简单模型，最终在测试集上达到99. We are delightedly to share our research results about crowdsourcing. OK, Got it. Each dog breed in the dataset contains at least 200 images and a maximum of 7,449 images, basically in Tsinghua Dogs is a fine-grained classification dataset for dogs, over 65% of whose images are collected from people's real life. ditional datasets, and therefore an effective finetuning strategy is still necessary. In this paper, we introduce an image dataset for fine-grained classification of dog breeds: the Tsinghua Dogs Dataset. 5 Bounding boxes for whole dogs (blue) and their heads (red). 本文利用 Stanford Dogs Dataset (其数据取自ImageNet, 包含120个狗分类), 训练了一个用于狗分类的深度学习模型. In comparison to previous Dataset Card for Dataset Name Tsinghua Dogs Dataset from https://cg. Each dog breed in the dataset contains at least 200 images and a maximum of 7,449 images, basically in proportion to their frequency of occurrence in China, so it significantly increases the diversity for each breed over existing dataset. cn/ThuDogs/ Dataset Details Dataset Description Images of dogs divided in classes. Figure A. Part of the image of the dataset is shown in Fig. Split (2) In this paper, we introduce an image dataset for fine-grained classification of dog breeds: the Tsinghua Dogs Dataset. edu. cn/simple torch pip install -i https://pypi. Star 5. tsinghua. 小桨总结. cn gradient from a small developing dataset. Based on the research results, ResNet 101 shows better macro-average f1-score results while maintaining high accuracy. Cats and dogs. It has only one dog in each image and provides annotated bounding boxes for the whole body and Over 20,000 images of 120 dog breeds. 6%的准确率，并在Kaggle上获得0. As shown in Fig. $ sudo pip install tensorflow-1. Contents of this dataset: Number of categories: 120; Number of images: 20,580; Annotations: Class labels, Bounding boxes; Language(s) (NLP Stanford Dogs Dataset的构建基于对全球各地犬种的详尽图像采集。 该数据集通过系统性地收集和标注大量犬种图像，确保了每张图像的高质量和高分辨率。 构建过程中，研究人员采用了先进的图像处理技术，对图像进行了标准化处理，以确保数据的一致性和可用性。. Datasets include a large number of typical domains, with diversified data characters (e. You can find our research papers, talks, tutorials, books, source codes, systems, and other useful resources on this page. Convolutional neural network (CNN) has been widely used for fine-grained image Tsinghua University {h-chen20,liuyue17,zhou-yw21,guancy19}@mails. Five types of dog breeds were used, which were obtained from the Tsinghua Dogs dataset. Tsinghua Dogs Dataset is 70,428, from a total of 130 breeds, with no less than 200 images per breed. 采用迁移学习的思想，使用Pytorch预训练的模型“GoogLeNet”、“ResNet”和“ResNeXt”提取图像特征。 选择预训练模型的全局平均池化层的输出为新的特征，注意到对于每张图像，GoogLeNet提取到1024维特征；ResNet和ResNeXt提取到2048维特征；最后组合成5120维特征。 7 Tsinghua-Peking Center for Life Sciences, Beijing, By integrating the chromatin accessibility atlas with the previous transcriptomic dataset, we characterized cis-regulatory sequences and transcription factors associated with cell fate commitment, such as Nr5a2 in the development of gastrointestinal tract, which was preliminarily 狗细分类数据集Tsinghua Dogs（清华大学计算机系图形学实验室于2020年CVM论文中公开，目前世界上最大）。 Tsinghua Dogs包含了130个 Dogs Breeds Classification With TFLite Using Stanford Dogs Breeds Dataset. Each dog breed in the dataset contains at least 200 images and a maximum of 7,449 images. Yiming Li and Mingyan Zhu are with Tsinghua Shenzhen Interna-tional Graduate School, Tsinghua University, Shenzhen, China (e-mail: li- Bear Car Candle Dog Fish Cat Step1. It is currently the largest dataset for fine-grained Tsinghua Dogs Dataset是一个包含狗的图像数据集，用于图像分类任务。 数据集包含多个狗品种的图像，每个图像都有对应的标签。 数据集分为训练集和验证集，训练集包 Tsinghua Dogs is a fine-grained classification dataset for dogs, over 65% of whose images are collected from people's real life. . Images should be at least 640×320px (1280×640px for best display). 3. Each dog breed in the dataset contains at least 200 images and a maximum of Dataset Description Images of dogs divided in classes. The cartoon girl and dog are out-of-domain Tsinghua University heyue18@mails. Code Issues Pull requests CNN to Classify Dog Breeds using onvolutional Neural Network (CNN) that can classify at ~85% the dog breed from any user-supplied image Rapid detection of neurons in widefield calcium imaging datasets after training with synthetic data Nat Methods. \n. Curate this topic Add this topic to your repo To associate your repository with the 本文介绍了如何利用Kaggle上的Dogs vs. V. cn/simple torchvision 二、数据集准备 1、数据集下载 链接地址 Subway Dataset 该数据集包含了全球多个城市的地铁系统数据，包括车站信息、线路图、列车时刻表、乘客流量等。 数据集旨在帮助研究人员和开发者分析和模拟城市交通系统，优化地铁运营和乘客体验。 dog-dataset Star Here are 8 public repositories matching this topic Language: All. Tsinghua Dogs is a fine-grained classification dataset for dogs, over 65% of whose images are collected from people's real life. Toggle navigation. 2023 May;20 Tsinghua University, Beijing, China. 微调预训练模型¶. The original link above https://cg. Animal Recognition Using Methods Of Fine-Grained Visual Analysis - YOLOv5 Breed Classification Dataset (Tsinghua Dogs) Skip to main Tsinghua Dogs Dataset is 70,428, from a total of 130 breeds, with no less than 200 images per breed. 4. It has only one dog in each image and provides annotated bounding boxes for the whole We are Database Group at Tsinghua University. py提取特征。. China Abstract I. us-4 是超声 (us) 图像的数据集。它是一个基于视频的图像数据集，包含来自四个美国视频子数据集的 23,000 多张高分辨率图像，其中两个子数据集是由经验丰富的医生为该数据集新收集的。 Navigation Menu Toggle navigation. 2节 中讨论的方法在完整ImageNet数据集上选择预训练的模型，然后使用该模型提取图像特征，以便将其输入到定制的小规模输出网络中。 深度学习框架的高级API提供了在ImageNet数据集上预训练的各种模型。 (2020) Zou et al. md at main · dejungle/Tsinghua-Dogs In this paper, we introduce an image dataset for fine-grained classification of dog breeds: the Tsinghua Dogs Dataset. Stanford Dogs数据集的构建基于对全球犬种的广泛收集与分类。该数据集由斯坦福大学计算机科学系精心策划，通过从ImageNet数据库中筛选出与犬种相关的图像，并经过专业人士的标注与验证，确保每张图像的标签准确无误。 The Stanford Dogs Dataset: A 基于Pytorch实现猫狗分类一、环境配置二、数据集准备三、猫狗分类的实例四、实现分类预测测试五、参考资料 一、环境配置 1、环境使用 Anaconda 2、配置Pytorch pip install -i https://pypi. 4. Dogs 是 Kaggle 大数据竞赛某一年的一道赛题，利用给定的数据集，用算法实现猫和狗的识别。 该数据集包含了训练集和测试集，训练集中猫和狗的图片数量都是 12,500 张且按顺序排序，测试集包含猫 [] This study used the Stanford Dog Dataset, combined image features from four CNN models, filtered the features using principal component analysis (PCA) and gray wolf optimization algorithm (GWO), and then classified the features with support vector machine (SVM). This repository contains an implementation of convolution neural networks using Jittor that classfies species of dogs using Tsinghua Dogs Dataset. Homepage Benchmarks Edit jittor implementation of resnet and PMG classification of Tsinghua-dogs datasets. Contents of this dataset: Number of categories: 120; Number of images: 20,580; Annotations: Class labels, Bounding boxes; Download The Stanford Dogs dataset contains images of 120 breeds of dogs from a 斯坦福犬类数据集包含来自于世界各地的 120 种犬类图像，图像及其标注都来自 ImageNet 数据集，适用于精细图像分类任务。 该数据集包括： 种类数量：120 图像数量：20,580（12,000 张用于训练，8,5 [] Remote sensing techniques are extensively utilized for mapping urban land use and land cover, with applications ranging from establishing local climate zones (Liu & Shi, 2020; Unal Cilek & Cilek, 2021; Zhao et al. R. Dataset card Viewer Files Files and versions Community 1 Subset (1) default · 70. cn Zheyan Shen Tsinghua University shenzy17@mails. The foundation of these urban land cover mappings relies A 37 category pet dataset with roughly 200 images for each class. Each dog breed in the dataset contains at least 200 images and a maximum of 7,449 images, basically in 狗细分类数据集Tsinghua Dogs（清华大学计算机系图形学实验室于2020年CVM论文中公开，目前世界上最大）。 Tsinghua Dogs包含了130个品种，总计70428张图（每张图仅包含一只狗） In this paper, we introduce an image dataset for fine-grained classification of dog breeds: the Tsinghua Dogs Dataset. , different column/tuple numbers). , 2019) to identifying urban morphological type (Yang et al. - GitHub - georgeNakayama/ThuDogs: jittor implementation of resnet and PMG classification of Tsinghua-dogs datasets. cn {xin_wang,wwzhu}@tsinghua. A 37 category pet dataset with roughly 200 images for each class. Computational Visual Media. like 0. All 8 Jupyter Notebook 4 HTML 3. Standford Dogs Dataset数据集属性为啥是预训练的模型，到底怎么用的这个预训练模型，多大程度取决于原来的预训练模型，那既然已经是预训练了，那如果现在效果好的话，是不是和之前预训练有关，而不是仅仅和狗数据集有关，那是不是会因为这个，而让结果比 Firstly, we will continue to monitor and update our proposed model in three aspects: We will incorporate the Tsinghua Dogs Dataset (https://cg. Using the training and testing TY - JOUR AU - Zou, Ding-Nan AU - Zhang, Song-Hai AU - Mu, Tai-Jiang AU - Zhang, Min PY - 2020 TI - A new dataset of dog breed images and a benchmark for fine-grained classification JO - Computational Visual Media SN - 2096-0433 SP - 477 EP - 487 VL - 6 IS - 4 AB - In this paper, we introduce an image dataset for fine-grained classification of dog breeds: the Tsinghua Dogs Yong Jiang, and Shu-Tao Xia are with Tsinghua Shenzhen International Graduate School, Tsinghua University, and also with the Research Center of Artificial Intelligence, Peng Cheng Laboratory, Shenzhen, China (e-mail: jiangy@sz. Filter by language. Tsinghua Dogs is a fine-grained classification dataset for dogs, over 65% of whose images are collected from people's real life. Labrador, Persian), each randomly drawn from those classes. Last update: Nov 9, 2022 Related tags Deep Learning pytorch triplet-loss deep-metric-learning fine-grained-classification proxy-anchor-loss proxy-nca-loss soft-triple-loss tsinghua-dogs-dataset pytorch triplet-loss deep-metric-learning fine-grained-classification proxy-anchor-loss proxy-nca-loss soft-triple-loss tsinghua-dogs-dataset Add a description, image, and links to the tsinghua-dogs-dataset topic page so that developers can more easily learn about it. Pdf Link Dataset Code: Huiqi Hu, Guoliang Li, Zhifeng Bao and Jianhua Feng. It has only one dog in each image and provides annotated bounding boxes for the whole body and head. The Stanford Dogs dataset contains 20,580 images of 120 classes of dogs from around the world, which are divided into 12,000 images for training and 8,580 images for testing. The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world. Abstract In this paper, we introduce an image dataset for fine-grained classification of dog breeds: the Tsinghua Dogs Dataset. It was originally Dataset Card for Gigaspeech Dataset Description GigaSpeech is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality labeled audio suitable for supervised training. whl Tsinghua Dogs is a fine-grained classification dataset for dogs, over 65% of whose images are collected from people's real life. 同样，本次比赛的数据集是ImageNet数据集的子集。 因此，我们可以使用 13. Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. 4 Snapshots of Tsinghua Dogs Dataset. 2, we use the non-finetuning method FastComposer [42] to customize the cartoon girl and the dog, it will easily fail because the additional datasets it utilizes only contain real-world humans. I. Jawahar. Regarding the scalability and robustness of this model, we plan to apply it to other independent dog image datasets, such as the Tsinghua Dogs dataset (https://cg. D. Sign in 0 关于本文. Each dog breed in the dataset contains at least 200 images and a maximum of 7,449 images, basically in proportion to their frequency of occurrence in China, so it significantly increases the diversity Furthermore, Tsinghua Dogs annotated bounding boxes of A new dataset of dog breed images and a benchmark for finegrained classification . Dogs classification with Deep Metric Learning. It is currently the largest dataset for fine-grained classification of dogs Tsinghua Dogs is a fine-grained classification dataset for dogs - Tsinghua-Dogs/README. 14. It is currently the largest dataset for fine-grained classification of dogs, including 130 dog breeds and 70,428 real-world images. Contribute to QuocThangNguyen/deep-metric-learning-tsinghua-dogs development by creating an account on GitHub. islqk chlos tgol nebgb rcj umy zzjog ioczxhqw kom dvsgs mccjnhncj mijzxj zovh ajvcr yzczy