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---
license: apache-2.0
base_model: microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft
model-index:
- name: THW
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: None
type: None
config: None
split: None
args: None
metrics:
- name: None
type: None
value: None
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Normal1919/THW
This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft) on the private dataset.
# How to use
```python
import torch
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from transformers import AutoModelForImageClassification
from matplotlib import pyplot as plt
model_name = "Normal1919/THW"
model = AutoModelForImageClassification.from_pretrained(model_name)
model.eval()
# model = torch.compile(model)
image_transform = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.697, 0.633, 0.635], std=[0.3135, 0.320, 0.315])
])
with torch.no_grad():
image_raw = torchvision.io.read_image("test_img/c9f00dbb7e8fe20538fcc71b1dc0fbb913029959.png")
if image_raw.size()[0] == 1:
image_raw = torch.cat([image_raw]*3, 0)
if image_raw.size()[0] == 4:
image_raw = image_raw[:3]
edit_image_tensor: torch.Tensor = image_transform(image_raw)
edit_image_tensor = edit_image_tensor.unsqueeze(0)
outputs = model(pixel_values=edit_image_tensor)
logits = F.sigmoid(outputs.logits)[0]
ind = logits.argmax().item()
print(model.config.id2label[ind])
cha_names = [model.config.id2label[i] for i in range(146)]
cha_probs = logits.numpy()
names_probs = list(zip(cha_names, cha_probs))
names_probs = sorted(names_probs, key=lambda x: x[1], reverse=True)
print(names_probs)
top_k = 10
names_show = []
probs_show = []
for i in range(top_k):
names_show.append(names_probs[i][0])
probs_show.append(names_probs[i][1])
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.figure(figsize=(12, 8))
plt.bar(names_show, probs_show)
plt.show()
``` |