Views
No views yet
| id | name | 한글 |
|---|---|---|
| 0 | epidural | 경막외출혈 |
| 1 | intraparenchymal | 뇌실질내출혈 |
| 2 | intraventricular | 뇌실내출혈 |
| 3 | subarachnoid | 지주막하출혈 |
| 4 | subdural | 경막하출혈 |
| 5 | any | 두개내출혈 |
tf_efficientnet_b4_ns_jft_in1k_fold0.ptconvnext_small_fb_in22k_ft_in1k_fold0.ptich_resnet18.ptmodel_state_dict (또는 ResNet18의 model) 키를 포함한 torch.save dict입니다.1from pathlib import Path
2import torch
3import timm
4from huggingface_hub import hf_hub_download
5
6REPO = "kimsungil/brain-ich-ensemble"
7NUM_CLASSES = 6
8
9def load_ckpt(filename, model_name, device):
10 path = hf_hub_download(REPO, filename)
11 blob = torch.load(path, map_location=device, weights_only=False)
12 sd = blob.get("model_state_dict") or blob.get("model") or blob
13 kwargs = dict(pretrained=False, num_classes=NUM_CLASSES)
14 if "resnet" not in model_name.lower():
15 kwargs.update(drop_rate=0.2, drop_path_rate=0.1)
16 model = timm.create_model(model_name, **kwargs)
17 model.load_state_dict(sd, strict=False)
18 return model.to(device).eval()
19
20device = torch.device("cpu")
21models = [
22 load_ckpt("tf_efficientnet_b4_ns_jft_in1k_fold0.pt", "tf_efficientnet_b4.ns_jft_in1k", device),
23 load_ckpt("convnext_small_fb_in22k_ft_in1k_fold0.pt", "convnext_small.fb_in22k_ft_in1k", device),
24 load_ckpt("ich_resnet18.pt", "resnet18", device),
25]