Going Deeper with Convolutions

TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Image Classification	ImageNet	Inception V1	Top 1 Accuracy	69.8%	# 157
Image Classification	ImageNet	Inception V1	Top 5 Accuracy	89.9%	# 99
Image Classification	ImageNet	Inception V1	Number of params	5M	# 72

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We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014). The main hallmark of this architecture is the improved utilization of the computing resources inside the network... (read more)

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IMAGE CLASSIFICATION

OBJECT DETECTION

OBJECT RECOGNITION

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Ranked #157 on Image Classification on ImageNet

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TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK	BENCHMARK
Image Classification	ImageNet	Inception V1	Top 1 Accuracy	69.8%	# 157	Compare
			Top 5 Accuracy	89.9%	# 99	Compare
			Number of params	5M	# 72	Compare

METHOD	TYPE
Convolution	Convolutions
Average Pooling	Pooling Operations
Local Response Normalization	Normalization
Auxiliary Classifier	Miscellaneous Components
1x1 Convolution	Convolutions
ReLU	Activation Functions
Dropout	Regularization
Dense Connections	Feedforward Networks
Max Pooling	Pooling Operations
Softmax	Output Functions
Weight Decay	Regularization
Polyak Averaging	Stochastic Optimization
Random Horizontal Flip	Image Data Augmentation
Random Resized Crop	Image Data Augmentation
SGD with Momentum	Stochastic Optimization
Step Decay	Learning Rate Schedules
GoogLeNet	Convolutional Neural Networks
Inception Module	Image Model Blocks

METHOD

TYPE

Convolution