resnet-50 softmax Search Results


resnet50  (SoftMax Inc)
90
SoftMax Inc resnet50
Hyper-parameter settings for different transfer learning models.
Resnet50, supplied by SoftMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/resnet-50+softmax/resnet50/pmc09046104-25-0-5
Average 90 stars, based on 1 article reviews
resnet50 - by Bioz Stars, 2026-09
90/100 stars
  Buy from Supplier
classifier with resnet-50  (SoftMax Inc)
90
SoftMax Inc classifier with resnet-50
Hyper-parameter settings for different transfer learning models.
Classifier With Resnet 50, supplied by SoftMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/resnet-50+softmax/classifier+with+resnet+50/pm40596195-375-12-9
Average 90 stars, based on 1 article reviews
classifier with resnet-50 - by Bioz Stars, 2026-09
90/100 stars
  Buy from Supplier
se-resnet-50  (SoftMax Inc)
90
SoftMax Inc se-resnet-50
Hyper-parameter settings for different transfer learning models.
Se Resnet 50, supplied by SoftMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/resnet-50+softmax/se+resnet+50/pmc07249188-32-0-6
Average 90 stars, based on 1 article reviews
se-resnet-50 - by Bioz Stars, 2026-09
90/100 stars
  Buy from Supplier

Image Search Results


Hyper-parameter settings for different transfer learning models.

Journal: Computers & Electrical Engineering

Article Title: Internet of Medical Things for early prediction of COVID-19 using ensemble transfer learning ☆

doi: 10.1016/j.compeleceng.2022.108018

Figure Lengend Snippet: Hyper-parameter settings for different transfer learning models.

Article Snippet: ResNet50 (M2) , 100 , Softmax , 16 , Binary crossentropy , Adam , 0.001.

Techniques: Activation Assay

Result of the transfer learning models without using Dropout.

Journal: Computers & Electrical Engineering

Article Title: Internet of Medical Things for early prediction of COVID-19 using ensemble transfer learning ☆

doi: 10.1016/j.compeleceng.2022.108018

Figure Lengend Snippet: Result of the transfer learning models without using Dropout.

Article Snippet: ResNet50 (M2) , 100 , Softmax , 16 , Binary crossentropy , Adam , 0.001.

Techniques:

Result of the transfer learning models with Dropout and a FC connected layer at the end.

Journal: Computers & Electrical Engineering

Article Title: Internet of Medical Things for early prediction of COVID-19 using ensemble transfer learning ☆

doi: 10.1016/j.compeleceng.2022.108018

Figure Lengend Snippet: Result of the transfer learning models with Dropout and a FC connected layer at the end.

Article Snippet: ResNet50 (M2) , 100 , Softmax , 16 , Binary crossentropy , Adam , 0.001.

Techniques:

Result of the transfer learning models with Dropout and two FC layers with 128 and 64 neurons.

Journal: Computers & Electrical Engineering

Article Title: Internet of Medical Things for early prediction of COVID-19 using ensemble transfer learning ☆

doi: 10.1016/j.compeleceng.2022.108018

Figure Lengend Snippet: Result of the transfer learning models with Dropout and two FC layers with 128 and 64 neurons.

Article Snippet: ResNet50 (M2) , 100 , Softmax , 16 , Binary crossentropy , Adam , 0.001.

Techniques:

Result of the transfer learning models without using Dropout and three FC layers with 256, 128, and 64 neurons respectively.

Journal: Computers & Electrical Engineering

Article Title: Internet of Medical Things for early prediction of COVID-19 using ensemble transfer learning ☆

doi: 10.1016/j.compeleceng.2022.108018

Figure Lengend Snippet: Result of the transfer learning models without using Dropout and three FC layers with 256, 128, and 64 neurons respectively.

Article Snippet: ResNet50 (M2) , 100 , Softmax , 16 , Binary crossentropy , Adam , 0.001.

Techniques:

Result of the transfer learning models with Dropout and four FC layers with 512, 256, 128, and 64 neurons respectively and taking their ensemble.

Journal: Computers & Electrical Engineering

Article Title: Internet of Medical Things for early prediction of COVID-19 using ensemble transfer learning ☆

doi: 10.1016/j.compeleceng.2022.108018

Figure Lengend Snippet: Result of the transfer learning models with Dropout and four FC layers with 512, 256, 128, and 64 neurons respectively and taking their ensemble.

Article Snippet: ResNet50 (M2) , 100 , Softmax , 16 , Binary crossentropy , Adam , 0.001.

Techniques:

Average execution time taken by transfer learning models.

Journal: Computers & Electrical Engineering

Article Title: Internet of Medical Things for early prediction of COVID-19 using ensemble transfer learning ☆

doi: 10.1016/j.compeleceng.2022.108018

Figure Lengend Snippet: Average execution time taken by transfer learning models.

Article Snippet: ResNet50 (M2) , 100 , Softmax , 16 , Binary crossentropy , Adam , 0.001.

Techniques: