feat: initial commit

.gitignore

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+__pycache__

app.py

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+import torch
+from torchvision import transforms
+import gradio as gr
+import timm
+
+# Read the categories
+with open("labels.txt", "r") as f:
+    categories = [s.strip() for s in f.readlines()]
+
+model_ft = timm.create_model('vit_base_patch16_224_in21k', pretrained=True, num_classes=len(categories))
+model_path = 'best_cpu.pt'
+model_ft.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
+model_ft.eval()
+
+
+# Download an example image from the pytorch website
+# torch.hub.download_url_to_file("https://iiif.dl.itc.u-tokyo.ac.jp/iiif/genji/TIFF/A00_6587/01/01_0001.tif/full/,400/0/default.jpg", "examples/other.jpg")
+# torch.hub.download_url_to_file("https://iiif.dl.itc.u-tokyo.ac.jp/iiif/genji/TIFF/A00_6587/01/01_0002.tif/full/,400/0/default.jpg", "examples/front.jpg")
+# torch.hub.download_url_to_file("https://iiif.dl.itc.u-tokyo.ac.jp/iiif/genji/TIFF/A00_6587/01/01_0003.tif/full/,400/0/default.jpg", "examples/page.jpg")
+# torch.hub.download_url_to_file("https://iiif.dl.itc.u-tokyo.ac.jp/iiif/genji/TIFF/A00_6587/01/01_0009.tif/full/,400/0/default.jpg", "examples/page2.jpg")
+# torch.hub.download_url_to_file("https://iiif.dl.itc.u-tokyo.ac.jp/iiif/genji/TIFF/A00_6587/01/01_0032.tif/full/,400/0/default.jpg", "examples/back.jpg")
+
+
+def inference(input_image):
+    preprocess = transforms.Compose([
+        transforms.Resize(224),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    ])
+    input_tensor = preprocess(input_image)
+    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
+
+    # move the input and model to GPU for speed if available
+    if torch.cuda.is_available():
+        input_batch = input_batch.to('cuda')
+        model_ft.to('cuda')
+
+    with torch.no_grad():
+        output = model_ft(input_batch)
+    # The output has unnormalized scores. To get probabilities, you can run a softmax on it.
+    probabilities = torch.nn.functional.softmax(output[0], dim=0)
+
+    
+    # Show top categories per image
+    top5_prob, top5_catid = torch.topk(probabilities, len(categories))
+    result = {}
+    for i in range(top5_prob.size(0)):
+        result[categories[top5_catid[i]]] = top5_prob[i].item()
+    return result
+
+inputs = gr.inputs.Image(type='pil')
+outputs = gr.outputs.Label(type="confidences",num_top_classes=len(categories))
+
+title = "表紙・裏表紙・その他のページの分類"
+description = "Vision Transformerを用いた表紙・裏表紙・その他のページの分類モデルです。"
+
+article = "<p style='text-align: center'>次のデータセットを使用しました。<a href='' target='_blank'>あああ</a></p>"
+
+examples = [
+    ['examples/other.jpg'],
+    ['examples/front.jpg'],
+    ["examples/page.jpg"],
+    ["examples/page2.jpg"],
+    ["examples/back.jpg"]
+]
+
+gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch()

best_cpu.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f3ab2e5acd80138398b765a0ab0f68cdda324e25fb311af590c6c21fe788c3b3
+size 343257169

labels.txt

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+back
+front
+page