caltech 256 object category dataset

  • Stanford Vision Lab Prof. Fei-Fei Li

     · 3D Object Category Dataset. dataset citation Savarese et al. ICCV 2007 downloads Caltech 101 Object Categories. dataset citations Fei-Fei et al. CVPR Workshop 2004. Fei-Fei et al. PAMI 2006 (w/ annotations) more information can be found here. 13 Natural Scene Categories.

  • CALTECH 101(101

     · Caltech-256 Object Category Dataset (2007) Cited 544 times. 67.6% Additional Info Griffin's SPM Improved Spatial Pyramid Matching for Image Classification (ACCV 2010) Cited 3 times. 67.36% ± 0.17% Variable Sparsity Kernel Learning (JMLR 2011) Cited 23

  • WebVision Database Visual Learning and Understanding from

     · For the Caltech-256 dataset, we run ten rounds of random sampling and train a multi-class SVM classifier using the training set at each round. The mean classification accuracy on the test set over ten rounds is reported. Caltech-256 object category dataset. Technical report, California Institute of Technology, 2007. [14] K.

  • G. Griffin, A. Holub and P. Perona, “Caltech-256 Object

    G. Griffin, A. Holub and P. Perona, “Caltech-256 Object Category Dataset,” Technical Report 7694, California Institute of Technology, Pasadena, 2007. has been cited by the following article

  • Caltech-256 () label_

     · Caltech-256 Dataset Caltech-101 Dataset , a);b) 31 80; c);d)。

  • Visual Recognition Challenge (Caltech 256 and PASCAL

     · ICCV'07 Workshop, Monday 15th October 2007. There are two datasets that are becoming standard for measuring visual recognition performance in vision papers the Caltech dataset, and the PASCAL Visual Object Classes Challenge datasets.For 2007 both have released new versions that are more challenging, for example with more classes.

  • torchvision.datasets.caltech — Torchvision 0.10.0

     · Args root (string) Root directory of dataset where directory ``caltech101`` exists or will be saved to if download is set to True. target_type (string or list, optional) Type of target to use, ``category`` or ``annotation``. Can also be a list to output a tuple with all specified target types. ``category`` represents the target class, and

  • Caltech 256 Image Dataset Kaggle

     · The Caltech 256 is considered an improvement to its predecessor, the Caltech 101 dataset, with new features such as larger category sizes, new and larger clutter categories, and overall increased difficulty. This is a great dataset to train models for visual recognition How can we recognize frogs, cell phones, sail boats and many other categories

  • 【】

     · Caltech-101 9146 101 40-800 300x200 Learning generative visual models from few training examples An incremental bayesian approach tested on 101 object categories Caltech-256 30607 256 >80 300x200 Caltech-256 object category dataset 9963 20

  • The PASCAL Visual Object Classes (VOC) Challenge

     · The “Caltech 256” Dataset (Griffin et al. 2007) corrected some of the deficiencies of Caltech 101—there is more vari-ability in size and localisation, and obvious artifacts have been removed. The number of classes is increased (from 101 to 256) and the aim is still to investigate multi-category ob-

  • GTDLBenchGitHub Pages

     · CALTECH256 CALTECH256. The CALTECH256 dataset. Dataset Statistics. Color RGB Sample Size Camprison with Caltech-101 The Number of Samples per Category for Caltech-256

  • torchvision.datasets.caltech — Torchvision 0.10.0

     · Args root (string) Root directory of dataset where directory ``caltech101`` exists or will be saved to if download is set to True. target_type (string or list, optional) Type of target to use, ``category`` or ``annotation``. Can also be a list to output a tuple with all specified target types. ``category`` represents the target class, and

  • Table of results for Caltech 101 dataset

     · Caltech-256 Object Category Dataset (2007) Cited 544 times. 67.6% Additional Info Griffin's SPM Improved Spatial Pyramid Matching for Image Classification (ACCV 2010) Cited 3 times. 67.36% ± 0.17% Variable Sparsity Kernel Learning (JMLR 2011) Cited 23

  • GitHubnickbiso/Keras-Caltech-256 257-way Image

    Caltech-256. Caltech-256 is a challenging set of 257 (including the last category of clutter) object categories containing a total of only 30607 images. Furthermore this dataset is imbalanced as seen in the plot below. In this exercise I utilized different Neural Network architectures and

  • _Caltech 256(256

    Caltech 256. Abstract. We introduce a challenging set of 256 object categories containing a total of 30607 images. The original Caltech-101 was collected by choosing a set of object categories, downloading examples from Google Images and then manually screening out all images that did not fit the category. Caltech-256 is collected in a similar

  • Caltech-256 Object Category DatasetCORE Reader

    Caltech-256 Object Category DatasetCORE Reader. We are not allowed to display external PDFs yet. You will be redirected to the full text document in the repository in a few seconds, if not click here.

  • WebVision Dataset 1.0ETH Z

     · For the Caltech-256 dataset, 30 images per category are used as the training set and the rest as the testing set. For PASCAL-VOC 2007 dataset, we combine the official train and validation splits as the training set, and use the test split as the test set. For all CNN models, we use the 4096-d output from the "fc7" layer as the feature

  • CV Datasets on the web

     · Caltech 256 Pictures of objects belonging to 256 categories ETHZ Shape Classes A dataset for testing object class detection algorithms. It contains 255 test images and features five diverse shape-based classes (apple logos, bottles, giraffes, mugs, and swans). Flower classification data sets 17 Flower Category Dataset Animals with attributes

  • Caltech256 Image DatasetAcademic Torrents

    Caltech256 Image DatasetAcademic Torrents. 256_ObjectCategories.tar. 1.18GB. Type Dataset. Tags Abstract ==Overview 256 Object Categories Clutter At least 80 images per category 30608 images instead of 9144. ==Caltech-101 Drawbacks Smallest category size is 31 images Too easy? left-right aligned Rotation artifacts Soon will saturate

  • Caltech 256 Image Dataset Kaggle

     · Caltech 256 Image Dataset Over 30,000 images in 256 object categories

  • SeaShips A Large-Scale Precisely Annotated Dataset for

     · The Caltech-256 dataset [22] has 4 ship lated work about ship dataset and object detection algorithms are described in Section II. The acquisition and annotation pro- determination of the position and category directly by a single network. As a result, one can quickly detect multi-targets in

  • Learning subcategory relevances for category recognition

     · On the challenging Caltech-256 dataset, the proposed approach significantly outperforms the best categorizations reported. This result is significant in that it not only demonstrates the advantages of exploiting subcategory taxonomy for recognition, but also suggests that a feature space spanned by part properties, instead of direct object

  • Visual Geometry Group Home PageUniversity of Oxford

    For example the ketch and schooner category will be both classified as a sail boat. Then at lower level we learn the specific feature weights to distinguish amongst these more related categories. Object ambiguities for Caltech 256. Ambiguous annotations for Caltech 256. Resources. Downloads from this site PHOG code (page) PHOW code (zip file)

  • Caltech256 Image DatasetTechnicalAcademic Torrents

    ==Overview 256 Object Categories Clutter At least 80 images per category 30608 images instead of 9144

  • GitHubnickbiso/Keras-Caltech-256 257-way Image

    Caltech-256 is a challenging set of 257 (including the last category of clutter) object categories containing a total of only 30607 images. Furthermore this dataset is imbalanced as seen in the plot below. In this exercise I utilized different Neural Network architectures and compare their performance.

  • Guide to Visual Recognition Datasets for Deep Learning

     · CALTECH 256. Released in 2006 by Greg Griffin, Alex Holub, and Perona Pietro, Caltech256 is an improvement to Caltech101 such as the number of object categories is more than double and the minimum number of samples per category was increased from 31 to 80. The background clutter class is also larger than earlier.

  • Caltech-256 Object Category DatasetCORE

    We introduce a challenging set of 256 object categories containing a total of 30607 images. The original Caltech-101 [1] was collected by choosing a set of object categories, downloading examples from Google Images and then manually screening out all images that did not fit the category.

  • Caltech-256Mathematical softwareswMATH

    Caltech-256 Object Category Dataset. We introduce a challenging set of 256 object categories containing a total of 30607 images. The original Caltech-101 [1] was collected by choosing a set of object categories, downloading examples from Google Images and then manually screening out all images that did not fit the category. Caltech-256 is collected in a similar manner with several improvements

  • Caltech-256 Object Category Dataset Semantic Scholar

    We introduce a challenging set of 256 object categories containing a total of 30607 images. The original Caltech-101 was collected by choosing a set of object categories, downloading examples from Google Images and then manually screening out all images that did not fit the category.

  • Caltech256 Image DatasetAcademic Torrents

    Caltech256 Image Dataset - Academic Torrents 256_ObjectCategories.tar 1.18GB

  • Caltech-256 Dataset Papers With Code

    Caltech-256 is an object recognition dataset containing 30,607 real-world images, of different sizes, spanning 257 classes (256 object classes and an additional clutter class). Each class is represented by at least 80 images. The dataset is a superset of the Caltech-101 dataset. Source Exploiting Non-Linear Redundancy for Neural Model Compression