Statements (54)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Database_Management_System
|
| gptkbp:bfsLayer |
5
|
| gptkbp:bfsParent |
gptkb:Res_Net-101
|
| gptkbp:challenges |
data quality
overfitting class imbalance computational efficiency model generalization |
| gptkbp:collection_size |
10,000 images
|
| gptkbp:contains |
100,000 images
200 classes |
| gptkbp:created_by |
gptkb:Stanford_University
|
| gptkbp:developed_by |
gptkb:Image_Net
|
| gptkbp:format |
gptkb:JPEG
|
| gptkbp:has_content |
http://tiny-imagenet.herokuapp.com/
|
| https://www.w3.org/2000/01/rdf-schema#label |
Tiny Image Net
|
| gptkbp:is_available_on |
gptkb:Open_ML
gptkb:archive gptkb:Kaggle |
| gptkbp:is_divided_into |
training, validation, test
|
| gptkbp:is_evaluated_by |
top-5 accuracy
top-1 accuracy |
| gptkbp:is_part_of |
gptkb:Image_Net_family
|
| gptkbp:is_popular_in |
deep learning community
|
| gptkbp:is_similar_to |
gptkb:CIFAR-10
gptkb:CIFAR-100 |
| gptkbp:is_used_for |
image classification
|
| gptkbp:is_used_in |
gptkb:academic_research
educational purposes competitions benchmarking algorithms |
| gptkbp:label |
gptkb:military_officer
|
| gptkbp:notable_for |
small size
hyperparameter tuning model evaluation ease of use visualization techniques feature extraction performance benchmarking research applications data exploration algorithm testing educational value diverse classes |
| gptkbp:performance |
machine learning models
|
| gptkbp:provides_information_on |
used
|
| gptkbp:release_year |
gptkb:2015
|
| gptkbp:resolution |
64x64 pixels
|
| gptkbp:size |
64x64 pixels
|
| gptkbp:supports |
transfer learning
convolutional neural networks data science research computer vision tasks |
| gptkbp:training |
100,000 images
|