| medical-image-classification-on-nct-crc-he-100k | Efficientnet-b0 | #1 | Accuracy (%): 95.59F1-Score: 97.48Precision: 99.89… |
| fine-grained-image-classification-on-birdsnap | EfficientNet-B7 | #2 | Accuracy: 84.3% |
| fine-grained-image-classification-on-oxford-iiit-pet | EfficientNet-B7 | #4 | Accuracy: 95.4% |
| fine-grained-image-classification-on-food-101 | EfficientNet-B7 | #6 | Accuracy: 93.0 |
| image-classification-on-gashissdb | EfficientNet-b0 | #6 | Accuracy: 98.11Precision: 99.94F1-Score: 99.01 |
| image-classification-on-omnibenchmark | EfficientNetB4 | #13 | Average Top-1 Accuracy: 35.8 |
| fine-grained-image-classification-on-fgvc-aircraft | EfficientNet-B7 | #19 | Accuracy: 92.9 |
| domain-generalization-on-vizwiz-classification | EfficientNet-B5 | #21 | Accuracy - All Images: 42.8Accuracy - Corrupted Images: 37… |
| domain-generalization-on-vizwiz-classification | EfficientNet-B4 | #27 | Accuracy - All Images: 41.7Accuracy - Corrupted Images: 35.6… |
| domain-generalization-on-vizwiz-classification | EfficientNet-B3 | #33 | Accuracy - All Images: 40.7Accuracy - Corrupted Images: 34.2… |
| domain-generalization-on-vizwiz-classification | EfficientNet-B2 | #51 | Accuracy - All Images: 38.1Accuracy - Corrupted Images: 31.4… |
| domain-generalization-on-vizwiz-classification | EfficientNet-B1 | #58 | Accuracy - All Images: 36.7Accuracy - Corrupted Images: 30.9… |
| domain-generalization-on-vizwiz-classification | EfficientNet-B0 | #77 | Accuracy - All Images: 34.2Accuracy - Corrupted Images: 27.4… |
| image-classification-on-flowers-102 | EfficientNet-B7 | #22 | Accuracy: 98.8% |
| fine-grained-image-classification-on-stanford-cars | EfficientNet-B7 | #24 | Accuracy: 94.7% |
| image-classification-on-cifar-100 | EfficientNet-B7 | #27 | Percentage correct: 91.7PARAMS: 64M |
| image-classification-on-cifar-10 | EfficientNet-B7 | #34 | Percentage correct: 98.9 |
| image-classification-on-imagenet | EfficientNet-B7 | #343 | Top 1 Accuracy: 84.4%Number of params: 66MGFLOPs: 37 |
| image-classification-on-imagenet | EfficientNet-B6 | #389 | Top 1 Accuracy: 84%Number of params: 43MGFLOPs: 19 |
| image-classification-on-imagenet | EfficientNet-B5 | #467 | Top 1 Accuracy: 83.3%Number of params: 30MGFLOPs: 9.9 |
| image-classification-on-imagenet | EfficientNet-B4 | #551 | Top 1 Accuracy: 82.6%Number of params: 19MGFLOPs: 4.2 |
| image-classification-on-imagenet | EfficientNet-B3 | #701 | Top 1 Accuracy: 81.1%Number of params: 12M |
| image-classification-on-imagenet | EfficientNet-B2 | #773 | Top 1 Accuracy: 79.8%Number of params: 9.2MGFLOPs: 1 |
| image-classification-on-imagenet | EfficientNet-B1 | #840 | Top 1 Accuracy: 78.8%Number of params: 7.8MGFLOPs: 0.7 |
| image-classification-on-imagenet | EfficientNet-B0 | #958 | Top 1 Accuracy: 76.3%Number of params: 5.3MGFLOPs: 0.39 |