how to use

README.md

@@ -23,4 +23,61 @@ should probably proofread and complete it, then remove this comment. -->
 
 # swinv2-large-patch4-window12to24-192to384-22kto1k-ft-microbes-merged
 
-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.
+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 = "THW_02"
+
+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()
+```