Networks Accuracy trained
| Paper | Dataset | Network | Bit(A/W/G) | Paper report | My Accuracy | Comment |
|---|---|---|---|---|---|---|
| - | imagenet | resnet18 | 32/32/32 | - | 70.090 | bacs-0 |
| - | imagenet | resnet18 | t/ t/32 | - | 62.138 | sgd-1, all quant |
| - | imagenet | resnet18 | t/ t/32 | - | 63.700 | sgd-1, force-fp |
| - | imagenet | resnet18 | t/ t/32 | - | 58.066 | sgd-3, all quant 0.250 |
| - | imagenet | resnet18 | t/ t/32 | - | 61.986 | sgd-3, all quant 0.500 |
| - | imagenet | resnet18 | t/ t/32 | - | 62.760 | sgd-3, all quant 0.618 |
| - | imagenet | resnet18 | t/ t/32 | - | 62.9?? | sgd-1, all quant 0.625 |
| - | imagenet | resnet18 | t/ t/32 | - | 61.986 | sgd-3, all quant 0.750 |
| - | imagenet | resnet18 | t/ t/32 | - | 62.176 | sgd-3, all quant 0.625, wt adaptive: mean-var |
| - | imagenet | resnet18 | t/ t/32 | - | 62.968 | sgd-1, all quant 0.625, wt adaptive: mean-var |
| - | <-> | - | - | - | - | - |
| - | imagenet | bireal18 | 32/32/32 | - | 68.892 | cbsa-1 |
| - | imagenet | bireal18 | t/ t/32 | - | 63.146 | sgd-3, force-fp |
| - | imagenet | bireal18 | t/ t/32 | - | 61.816 | sgd-3, all quant |
| - | <-> | - | - | - | - | - |
| - | imagenet | resnet34 | 32/32/32 | - | 73.328 | bacs-1 |
| - | imagenet | resnet34 | t/ t/32 | - | 67.442 | sgd-1, all quant |
| - | imagenet | resnet34 | t/ t/32 | - | 68.990 | sgd-1, force-fp |
| - | imagenet | resnet34 | t/ t/32 | - | 68.020 | sgd-1, all quant 0.625 |
| - | <-> | - | - | - | - | - |
| - | imagenet | bireal34 | 32/32/32 | - | 67.610 | cbsa-1, custom-step |
| - | imagenet | bireal34 | 32/32/32 | - | 69.390 | cbsa-1, sgdr |
| - | imagenet | bireal34 | t/ t/32 | - | 66.058 | sgd-3, force-fp |
| - | imagenet | bireal34 | t/ t/32 | - | xx.xxx | sgd-3, all quant |
| - | <-> | - | - | - | - | - |
| - | imagenet | resnet50 | 32/32/32 | - | xx.xxx | bacs-1 |
| - | imagenet | resnet50 | t/ t/32 | - | xx.xxx | sgd-1, all quant |
| - | imagenet | resnet50 | t/ t/32 | - | 71.1?? | sgd-1, all quant 0.625 |
| - | <-> | - | - | - | - | - |
| - | imagenet | det-rs18 | t/ t/32 | - | 62.576 | sgd-3, all quant 0.7 |
| - | imagenet | det-rs18 | t/ t/32 | - | 62.720 | sgd-3, all quant 0.7, wt adaptive: mean-var |
| - | imagenet | det-rs18 | t/ t/32 | - | 62.746 | sgd-3, all quant 0.625, wt adaptive: mean-var |
| - | imagenet | det-rs34 | t/ t/32 | - | 67.970 | sgd-3, all quant 0.625, wt adaptive: mean-var |
Tips:
update the threshold according to
‘Ternary neural networks with fine-grained quantization’ and ‘TWN’?