syt2 / CNN

pytorch implementation of several CNNs for image classification

CNNs for image classification

Train CNNs for image classification from scratch.

I post several pretrained weights below.

Requirement

• pytorch 1.4.0+
• torchvision
• tensorboard 1.14+
• numpy
• pyyaml
• tqdm
• pillow

Dataset

• CIFAR-10
• CIFAR-100
• ImageNet 2012

Usage

• Add configuration file under configs folder as follows

  • cuda: "all"  # if not specified, use cpu. or specified as "0", "0,1"
    model:
        arch: resnet
        depth: 50
    data:
        dataset: imagenet
        train_dir: /PATH/TO/ILSVRC/train
        val_dir: /PATH/TO/ILSVRC/val
        workers: 16
    training:
        runid: xxxx  # recommended specified during validation and testing
        epochs: 100
        batch_size: 256
        loss:
            name: 'label_smooth'
            smoothing: 0.1
        optimizer:
            name: 'sgd'
            lr: 0.1
            weight_decay: 0.0001
            momentum: 0.9
        lr_schedule:
            name: 'multi_step'
            milestones: [30,60,90]
            gamma: 0.1
        save_interval: 1
        resume: save_model.pkl
        best_model: best_model.pkl
    

• run train.py, validate.py or test.py as follows

  • python train.py --config configs/aaaa.yml

Pretrained Model on ImageNet 2012

Architecture	Top-1 error	Params	FLOPs	Pretrained weights
ResNet18 (My Imp.)	29.72	11.69M	1.82G	Google Drive Baidu Netdisk
ResNet18 (paper)	30.43	-	-	-
ResNet50 (My Imp.)	23.30	25.56M	4.11G	Google Drive Baidu Netdisk
ResNet50 (paper)	24.7	-	-	-
ResNet101 (My Imp.)	22.18	44.55M	7.84G	Google Drive
ResNet101 (paper)	22.44	-	-	-
ResNeXt50 (My Imp.)	22.35	25.03M	4.26G	Google Drive Baidu Netdisk
ResNeXt50 (paper)	22.2	-	-	-
SE ResNet50 (My Imp.)	22.64	28.09M	4.12G	Google Drive Baidu Netdisk
SE ResNet50 (paper)	23.29	-	-	-
CBAM ResNet50 (My Imp.)	22.40	28.07M	4.13G	Google Drive
CBAM ResNet50 (paper)	22.66	-	-	-
SKNet50 (My Imp.)	21.26	27.49M	4.50G	Google Drive Baidu Netdisk
SKNet50 (paper)	20.79	-	-	-
MobileNet V2 0.5x (My Imp.)	35.62	1.97M	138.46M	Google Drive
MobileNet V2 0.5x (paper)	35.6	-	-	-
MobileNet V2 1x (My Imp.)	28.09	3.50M	315.41M	Google Drive Baidu Netdisk
MobileNet V2 1x (paper)	28.0	-	-	-
MobileNet V3 large (My Imp.)	26.79	5.48M	230.05M	Google Drive
MobileNet V3 large (paper)	24.8	-	-	-

The hyperparameters and settings during my training for ResNet, ResNeXt, SENet, SKNet are the same as the paper, except I use label smooth loss.

And for MobileNet V2 and MobileNet V3, I follow the setup in this project, and use label smooth loss too.

Pretrained weights usage

1. place the downloaded pretrained model in runs/aaaa/xxxx folder under this project, where aaaa is the name of configuration file and xxxx is runid in configuration file.
2. run validate.py or test.py as above.