Res Net-50

URI: https://gptkb.org/entity/Res_Net-50

GPTKB entity

Statements (64)

Predicate	Object
gptkbp:instance_of	gptkb:television_channel
gptkbp:bfsLayer	4
gptkbp:bfsParent	gptkb:Res_Net
gptkbp:applies_to	Image Classification
gptkbp:architectural_style	gptkb:Deep_Learning
gptkbp:coat_of_arms	50
gptkbp:connects	gptkb:theorem
gptkbp:developed_by	gptkb:Microsoft_Research
gptkbp:engine	gptkb:Autonomous_Vehicles Facial Recognition Medical Imaging Agricultural Monitoring Retail Analytics
gptkbp:established	Re LU
https://www.w3.org/2000/01/rdf-schema#label	Res Net-50
gptkbp:input_output	224x224
gptkbp:introduced	gptkb:2015
gptkbp:is_analyzed_in	gptkb:Neural_Architecture_Search Adversarial Attacks Transfer Learning Techniques
gptkbp:is_evaluated_by	gptkb:CIFAR-10 gptkb:SVHN gptkb:CIFAR-100
gptkbp:is_implemented_in	gptkb:Graphics_Processing_Unit gptkb:Py_Torch
gptkbp:is_known_for	gptkb:Deep_Residual_Learning High Accuracy Efficient Training
gptkbp:is_open_source	gptkb:theorem
gptkbp:is_part_of	gptkb:Keras_Applications gptkb:ONNX_Model_Zoo gptkb:Res_Net_Family Open CVDNN Module
gptkbp:is_popular_in	gptkb:viewpoint
gptkbp:is_used_for	Object Detection Image Segmentation
gptkbp:is_used_in	gptkb:streaming_service
gptkbp:localization	gptkb:theorem
gptkbp:performance	gptkb:Image_Net_Challenge Feature Extraction Data Augmentation Techniques Real-time Image Processing Model Compression Techniques Visual Recognition Tasks Ensemble Learning Techniques Generative Adversarial Networks (GA Ns) Contrastive Learning Techniques Deep Learning Competitions Domain Adaptation Techniques Few-shot Learning Techniques Fine-tuning Models Hyperparameter Optimization Techniques Model Quantization Techniques Multi-task Learning Techniques Neural Network Pruning Techniques Self-supervised Learning Techniques Semi-supervised Learning Techniques Zero-shot Learning Techniques
gptkbp:predecessor	gptkb:Res_Net-34
gptkbp:resulted_in	Top-5 accuracy of 92.3% on Image Net
gptkbp:successor	gptkb:Res_Net-101
gptkbp:training	gptkb:Image_Net_Dataset
gptkbp:uses	Average Pooling Residual Learning