create app

app.py

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+import torch
+import torchvision.transforms as T
+from timm import create_model
+from safetensors.torch import load_model
+import numpy as np
+from pathlib import Path
+import gradio as gr
+
+examples = Path('./examples').glob('*')
+examples = list(map(str,examples))
+
+valid_tfms = T.Compose([
+    T.Resize((224,224)),
+    T.ToTensor(),
+    T.Normalize(
+        mean = (0.5,0.5,0.5),
+         std = (0.5,0.5,0.5)
+    )
+])
+
+
+model_path = 'model/swin_s3_base_224-pascal/model.safetensors'
+model = create_model(
+        'swin_s3_base_224',
+        pretrained = False,
+        num_classes = 20
+)
+load_model(model,model_path)
+model.eval()
+
+class_names = [
+    "Aeroplane","Bicycle","Bird","Boat","Bottle",
+    "Bus","Car","Cat","Chair","Cow","Diningtable",
+    "Dog","Horse","Motorbike","Person",
+    "Potted plant","Sheep","Sofa","Train","Tv/monitor"
+]
+
+label2id = {c:idx for idx,c in enumerate(class_names)}
+id2label = {idx:c for idx,c in enumerate(class_names)}
+
+
+def predict(im):
+    im = valid_tfms(im).unsqueeze(0)
+    with torch.no_grad():
+        logits = model(im)
+    
+    confidences = logits.sigmoid().flatten()
+    predictions = confidences > 0.5
+    predictions = predictions.float().numpy()
+    pred_labels = np.where(predictions==1)[0]
+    confidences = confidences[pred_labels].numpy()
+    pred_labels = [id2label[label] for label in pred_labels]
+    outputs = {l:c for l,c in zip(pred_labels, confidences)}
+    return outputs
+
+gr.Interface(fn=predict,
+             inputs=gr.Image(type="pil"),
+             outputs=gr.Label(label='the image contains:'),
+             examples=examples).queue().launch()

model/swin_s3_base_224-pascal/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:57c40f375cba8df0eae8186e3be85d6ad1fcb3e02d307fb263962872e990e66a
+size 281538560

requirements.txt

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+torch
+torchvision
+safetensors
+timm
+gradio