# How to reproduce the results:

## MobileNetV2

### Baselines

DBQ-1T with first and last layers quantized

```bash
python main.py -b 100 -g 1 -ie 5 -m mobilenetv2 -d cifar100 -ft "mobilenetv2_pretrained.ckpt" -nb 2 -if -nbf 2 -il -nbl 2 -rp 0
```

DBQ-2T with first and last layers quantized

```bash
python main.py -b 100 -g 1 -ie 5 -m mobilenetv2 -d cifar100 -ft "mobilenetv2_pretrained.ckpt" -nb 2 -if -nbf 2 -il -nbl 2 -rp 0
```

DBQ-3T with first and last layers quantized

```bash
python main.py -b 100 -g 1 -ie 5 -m mobilenetv2 -d cifar100 -ft "mobilenetv2_pretrained.ckpt" -nb 3 -if -nbf 3 -il -nbl 3 -rp 0
```

DBQ-4T with first and last layers quantized

```bash
python main.py -b 100 -g 1 -ie 5 -m mobilenetv2 -d cifar100 -ft "mobilenetv2_pretrained.ckpt" -nb 4 -if -nbf 4 -il -nbl 4 -rp 0
```

### Our Method

Shrink 300% from DBQ-4T with first and last layers quantized

```bash
python main.py -b 100 -g 1 -ie 5 -m mobilenetv2 -d cifar100 -ft "mobilenetv2_pretrained.ckpt" -nb 4 -if -nbf 4 -il -nbl 4 -rp 3.0
```

The pretrained FP model can be downloaded here: [mobilenetv2_cifar100_pretrained.ckpt - Google Drive](https://drive.google.com/file/d/1cf5US2IKE-9PiVihcmZmsgfmMaMBqP0W/view?usp=sharing).
