Pytorch Implementation of "ISFPN: A medical detection model for ischemic stroke based on Feature Pyramid Network and Swin Transformer
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OS: Windows 10
Python: python3.7 with torch==1.2.0, torchvision==0.4.0

You could get the trained models reported above at 
https://github.com/Thanostitan1022/MyModel_2

usage: train.py [-h] --datasetname DATASETNAME]
                [--checkpointspath CHECKPOINTSPATH]
optional arguments:
  -h, --help            show this help message and exit
  --datasetname DATASETNAME
                        dataset for training.
  --checkpointspath CHECKPOINTSPATH
                        checkpoints you want to use.
                        
parser.add_argument("--start_epoch", type=int, default=0, help="epoch to start training from last time")
parser.add_argument("--epoch", type=int, default=0, help="epoch to start training from")
parser.add_argument("--n_epochs", type=int, default=80, help="number of epochs of training")
parser.add_argument("--dataset_name", type=str, default="", help="name of the dataset")
parser.add_argument("--batch_size", type=int, default=32, help="size of the batches")
parser.add_argument("--lr", type=float, default=0.0003, help="adam: learning rate")
parser.add_argument("--b1", type=float, default=0.5,
                    help="adam: decay of first order momentum of gradient")
parser.add_argument("--b2", type=float, default=0.999,
                    help="adam: decay of first order momentum of gradient")
parser.add_argument("--decay_epoch", type=int, default=3, help="epoch from which to start lr decay")
parser.add_argument("--n_cpu", type=int, default=8, help="number of cpu threads to use during batch generation")
parser.add_argument("--img_height", type=int, default=64, help="size of image height")
parser.add_argument("--img_width", type=int, default=64, help="size of image width")
parser.add_argument("--channels", type=int, default=1, help="number of image channels")
parser.add_argument("--sample_interval", type=int, default=100, help="interval between saving generator outputs")
parser.add_argument("--checkpoint_interval", type=int, default=5, help="interval between saving model checkpoints")
parser.add_argument("--n_residual_blocks", type=int, default=9, help="number of residual blocks in model")
parser.add_argument("--lambda_cyc", type=float, default=10.0, help="cycle loss weight")
parser.add_argument("--lambda_id", type=float, default=5.0, help="identity loss weight")
parser.add_argument('--cuda', type=bool, default=True, help='use GPU computation')
parser.add_argument('--model', type=str, default='', help='model checkpoint file')                        


cmd example:
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python train.py --datasetname ISLES

usage: test.py [-h] --datasetname DATASETNAME [--annfilepath ANNFILEPATH]
               [--datasettype DATASETTYPE]
               --checkpointspath CHECKPOINTSPATH [--nmsthresh NMSTHRESH]
optional arguments:
  -h, --help            show this help message and exit
  --datasetname DATASETNAME
                        dataset for testing.
  --annfilepath ANNFILEPATH
                        used to specify annfilepath.
  --datasettype DATASETTYPE
                        used to specify datasettype.
  --checkpointspath CHECKPOINTSPATH
                        checkpoints you want to use.
  --nmsthresh NMSTHRESH
                        thresh used in nms.
cmd example:
CUDA_VISIBLE_DEVICES=0 python test.py --checkpointspath fpn_res50_trainbackup/mymodel_20.pth --datasetname ISLES

import torch

# Model
model = torch.load("save", "mymodel")

# Images
img = "Dataset/images/test_iamges/0001.jpg"

# Inference
results = model(img)

# Results
results.print()