age

.gitignore

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+.venv*

app.py

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+import requests
+from PIL import Image
+from io import BytesIO
+import torch
+import streamlit as st
+
+from transformers import ViTFeatureExtractor, ViTForImageClassification
+
+st.title('나이를 예측')
+uploaded_file = st.file_uploader("나이 예측 용 파일 업로드")
+if uploaded_file is not None:
+    im = Image.open(uploaded_file)
+    # Get example image from official fairface repo + read it in as an image
+    # r = requests.get('https://github.com/dchen236/FairFace/blob/master/detected_faces/race_Asian_face0.jpg?raw=true')
+    # im = Image.open(BytesIO(r.content))
+
+    # Init model, transforms
+    model = ViTForImageClassification.from_pretrained('nateraw/vit-age-classifier')
+    transforms = ViTFeatureExtractor.from_pretrained('nateraw/vit-age-classifier')
+
+    # Transform our image and pass it through the model
+    inputs = transforms(im, return_tensors='pt')
+    output = model(**inputs)
+
+    # Predicted Class probabilities
+    proba = output.logits.softmax(1)
+
+    values, indices = torch.topk(proba, k=5)
+
+    result_dict = {model.config.id2label[i.item()]: v.item() for i, v in zip(indices.numpy()[0], values.detach().numpy()[0])}
+    first_result = list(result_dict.keys())[0]
+
+    print(f'predicted result:{result_dict}')
+    print(f'first_result: {first_result}')
+
+    st.write(f'예측나이 : {first_result}')

requirements.txt

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+transformers
+pillow
+streamlit
+torch
+torchvision
+torchaudio

sample.py

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+import requests
+from PIL import Image
+from io import BytesIO
+import torch
+
+from transformers import ViTFeatureExtractor, ViTForImageClassification
+
+# Get example image from official fairface repo + read it in as an image
+r = requests.get('https://github.com/dchen236/FairFace/blob/master/detected_faces/race_Asian_face0.jpg?raw=true')
+im = Image.open(BytesIO(r.content))
+
+# Init model, transforms
+model = ViTForImageClassification.from_pretrained('nateraw/vit-age-classifier')
+transforms = ViTFeatureExtractor.from_pretrained('nateraw/vit-age-classifier')
+
+# Transform our image and pass it through the model
+inputs = transforms(im, return_tensors='pt')
+output = model(**inputs)
+
+# Predicted Class probabilities
+proba = output.logits.softmax(1)
+
+values, indices = torch.topk(proba, k=5)
+
+result_dict = {model.config.id2label[i.item()]: v.item() for i, v in zip(indices.numpy()[0], values.detach().numpy()[0])}
+first_result = list(result_dict.keys())[0]
+
+print(f'predicted result:{result_dict}')
+print(f'first_result: {first_result}')