update README

README.md

@@ -4,4 +4,67 @@ base_model:
 - timm/swin_base_patch4_window7_224.ms_in22k_ft_in1k
 pipeline_tag: image-classification
 library_name: timm
----
+---
+
+# PowerPoint slide classifier
+
+This is a classifier to classify 5 types of PowerPoint slide layouts. Finetuned from `timm/swin_base_patch4_window7_224.ms_in22k_ft_in1k` and trained on 10k powerpoint slide images.
+
+## Usage
+
+### Install timm and dependencies
+
+```bash
+pip install timm==1.0.15 torch==2.7.0 torchvision==0.22.0
+```
+
+### Inference
+
+Use the following code to classify images from a folder.
+
+```python
+import os
+import timm
+import torch
+from PIL import Image
+from torchvision import transforms
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+image_folder = 'path_to_images'
+
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]
+    )
+])
+
+model = timm.create_model('swin_base_patch4_window7_224', pretrained=False, num_classes=5)
+model.load_state_dict(torch.load('pytorch_model.bin'))
+model.to(device)
+model.eval()
+
+image_files = [f for f in os.listdir(image_folder) if f.lower().endswith('.png')]
+
+idx_to_class = {
+    0: 'content',
+    1: 'end',
+    2: 'start',
+    3: 'subt',
+    4: 'subtl'
+}
+
+with torch.no_grad():
+    for image_name in image_files:
+        image_path = os.path.join(image_folder, image_name)
+        image = Image.open(image_path).convert('RGB')
+        input_tensor = transform(image).unsqueeze(0).to(device)
+
+        output = model(input_tensor)
+        predicted_class = torch.argmax(output, dim=1).item()
+        predicted_label = idx_to_class[predicted_class]
+
+        print(f"{image_name} --> {predicted_label}")
+```