imagenet top 1

MEAL v2 Achieving ImageNet Top-1 80 with ResNet-50

 · As a result ResNet-50 s ImageNet-1K performance (224 224 single crop) surpassed TOP-1 80 without changing the network structure. The title of the paper is also "ResNet-50 performance 80 pass without trick". The code as well as pre-trained models of ResNet-50 MobileNet V3 and EfficientNet-B0 are all publicly available so they look

7 Popular Image Classification Models in ImageNet

 · Popular Deep Learning Models of ImageNet Challenge (ILSVRC) Competition History. In this section we ll go through the deep learning models that won in the Imagenet Challenge ILSVRC competition history. We ll also see what all advantages they provide and where they need to improve. 1.

ImageNet

ImageNet is an image database organized according to the WordNet hierarchy (currently only the nouns) in which each node of the hierarchy is depicted by hundreds and thousands of images. The project has been instrumental in advancing computer vision and deep learning research. The data is available for free to researchers for non-commercial use.

ImageNettop-190

 · ImageNettop-190 . Quoc Le ImageNet top-1 85 . AutoML Quoc Le

MEAL V2 Boosting Vanilla ResNet-50 to 80 Top-1

 · On ImageNet our method obtains 80.67 top-1 accuracy using a single crop-size of 224X224 on the vanilla ResNet-50 outperforming the previous state-of-the-arts by a remarkable margin under the same network structure. Our result can be regarded as a new strong baseline on ResNet-50 using knowledge distillation.

ImageNetdevopedia

 · EfficientNet claims to have achieved top-5 classification accuracy of 97.1 and top-1 accuracy of 84.4 for ImageNet dethroning it s predecessor GPipe (December 2018) by a meagre 0.1 in both top-1 and top-5 accuracies.

ImageNet Classification

Top-1 Accuracy 57.0 Top-5 Accuracy 80.3 Forward Timing 3.1 ms/img CPU Forward Timing 0.29 s/img cfg file weight file (238 ) Darknet Reference Model. This model is designed to be small but powerful. It attains the same top-1 and top-5 performance as AlexNet but with 1/10th the parameters.

ImageNet top-1top-5 QA

ImageNet top-1top-5 Krizhevsky ImageNet CNN 7 5 57CNN

sotabench Image Classification on ImageNet Benchmark

Image Classification on ImageNet. Leaderboard TOP 1 ACCURACY TOP 5 ACCURACY SPEED PAPER ε-REPRODUCES PAPER 1 FixEfficientNet_L2. rwightman / pytorch-image-models. 88.5 98.7 11.1

ImageNettop-190

 · 2016 ImageNet top-1 85 Quoc Le 90.2 ImageNet top-1

ImageNetCNNPilgrimHui

 ·  / val top-1 val top-5 test top-5 2012 AlexNet 38.1 16.4 16.42 5 CNNs 2012 AlexNet 36.7 15.4 15.32 7CNNs 2011 2013 OverFeat 14.18 7 fast models 2013 OverFeat 13.6

VOLO

 ·  ImageNet87.1 top-1 CNNSOTA NFnet 86.5 Transformer CNN outlook

human level performance on ImageNet top-1 or top-5

 · Active 2 years 5 months ago. Viewed 3k times. 3. Anyone have pointers to where the human level performance on ImageNet comes from I found a reference to 5.1 accuracy (top-1 or top

Adversarial Examples Improve Image Recognition

 · ImageNet Top-1 Accuracy ( ) Vanilla Training Madry s Adversarial Training Madry s Adversarial Training Fine-tuning Figure 2. Two take-home messages from the experiments on Ima-geNet (1) training exclusively on adversarial examples results in performance degradation and (2) simply training with adversarial

ImageNet A Large-Scale Hierarchical Image Database

 · Figure 1 A snapshot of two root-to-leaf branches of ImageNet the top row is from the mammal subtree the bottom row is from the vehicle subtree. For each synset 9 randomly sampled images are presented. Figure 2 Scale of ImageNet. Red curve Histogram of number of images per synset. About 20 of the synsets have very few

2104.10858v2 Token Labeling Training a 85.4 Top-1

 · Taking a vision transformer with 26M learnable parameters as an example we can achieve an 84.4 Top-1 accuracy on ImageNet. When the model size is scaled up to 56M/150M the result can be further increased to 85.4 /86.2 without extra data. We hope this study could provide researchers with useful techniques to train powerful vision transformers.

7 Popular Image Classification Models in ImageNet

 · Popular Deep Learning Models of ImageNet Challenge (ILSVRC) Competition History. In this section we ll go through the deep learning models that won in the Imagenet Challenge ILSVRC competition history. We ll also see what all advantages they provide and where they need to improve. 1.

2104.10858v2 Token Labeling Training a 85.4 Top-1

 · Taking a vision transformer with 26M learnable parameters as an example we can achieve an 84.4 Top-1 accuracy on ImageNet. When the model size is scaled up to 56M/150M the result can be further increased to 85.4 /86.2 without extra data. We hope this study could provide researchers with useful techniques to train powerful vision transformers.

classificationImageNet what is top-1 and top-5 error

 · The Top-1 class is "mouse". The top-2 classes are mouse dog . If the correct class was "dog" it would be counted as "correct" for the Top-2 accuracy but as wrong for the Top-1 accuracy.

ImageNetCNNPilgrimHui

 ·  / val top-1 val top-5 test top-5 2012 AlexNet 38.1 16.4 16.42 5 CNNs 2012 AlexNet 36.7 15.4 15.32 7CNNs 2011 2013 OverFeat 14.18 7 fast models 2013 OverFeat 13.6

ImageNettop-190

 · ImageNettop-190 . Quoc Le ImageNet top-1 85 . AutoML Quoc Le

ImageNetdevopedia

 · EfficientNet claims to have achieved top-5 classification accuracy of 97.1 and top-1 accuracy of 84.4 for ImageNet dethroning it s predecessor GPipe (December 2018) by a meagre 0.1 in both top-1 and top-5 accuracies.

ImageNet87.1

 · VOLO ImageNet 87.1 top-1 Transformer CNN

MEAL V2 Boosting Vanilla ResNet-50 to 80 Top-1

 · Using this approach the authors were able to train the vanilla ResNet-50 architecture on ImageNet with no modifications external training data or tricks like AutoAugment mixup label smoothing etc to achieve top-1 accuracy with 224 224 input images of 80.67 which is by far the best result to date with this architecture.

ImageNet A Large-Scale Hierarchical Image Database

 · Figure 1 A snapshot of two root-to-leaf branches of ImageNet the top row is from the mammal subtree the bottom row is from the vehicle subtree. For each synset 9 randomly sampled images are presented. Figure 2 Scale of ImageNet. Red curve Histogram of number of images per synset. About 20 of the synsets have very few

MEAL V2 Boosting Vanilla ResNet-50 to 80 Top-1

 · Using this approach the authors were able to train the vanilla ResNet-50 architecture on ImageNet with no modifications external training data or tricks like AutoAugment mixup label smoothing etc to achieve top-1 accuracy with 224 224 input images of 80.67 which is by far the best result to date with this architecture.

VOLO

ImageNet 87.1 top-1 CNNSOTA NFnet 86.5

ImageNettop-190

 · ImageNettop-190 . Quoc Le ImageNet top-1 85 . AutoML Quoc Le

ImageNettop-190

2016 ImageNet top-1 85 Quoc Le 90.2 ImageNet top-1

ImageNet

ImageNet is an image database organized according to the WordNet hierarchy (currently only the nouns) in which each node of the hierarchy is depicted by hundreds and thousands of images. The project has been instrumental in advancing computer vision and deep learning research. The data is available for free to researchers for non-commercial use.

ImageNettop-190

 · ImageNettop-190 . Quoc Le ImageNet top-1 85 . AutoML Quoc Le

Top-1Top-5_a7_aaaaa

 · Top-1 AccuracyTop-5 Accuracy ImageNet1000 1000 Top-1 Accuracy Top-5 Accuracy

trick ResNet-50ImageNet

224 224 ImageNetResNet-5080.67 · 1 · 28

ImageNet A Large-Scale Hierarchical Image Database

 · Figure 1 A snapshot of two root-to-leaf branches of ImageNet the top row is from the mammal subtree the bottom row is from the vehicle subtree. For each synset 9 randomly sampled images are presented. Figure 2 Scale of ImageNet. Red curve Histogram of number of images per synset. About 20 of the synsets have very few