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Update README.md

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@@ -115,14 +115,14 @@ Returns top-5 most similar artists with confidence scores:
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  ### Installation / Установка
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  ```bash
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- pip install onnxruntime onnx pillow numpy
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  # or for GPU / или для GPU:
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- pip install onnxruntime-gpu onnx pillow numpy
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  ```
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  ### Inference / Инференс
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- ``python
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  import onnxruntime as ort
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  import onnx
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  import numpy as np
@@ -154,11 +154,9 @@ class AuthorID:
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  elif prop.key == "input_size":
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  self.input_size = int(prop.value)
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- # Create inference session
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  providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
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  self.session = ort.InferenceSession(onnx_path, providers=providers)
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- # ImageNet normalization
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  self.mean = np.array([0.485, 0.456, 0.406], dtype=np.float32).reshape(1, 3, 1, 1)
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  self.std = np.array([0.229, 0.224, 0.225], dtype=np.float32).reshape(1, 3, 1, 1)
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  ### Installation / Установка
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  ```bash
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+ pip install onnxruntime onnx pillow numpy huggingface_hub
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  # or for GPU / или для GPU:
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+ pip install onnxruntime-gpu onnx pillow numpy huggingface_hub
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  ```
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  ### Inference / Инференс
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+ ```python
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  import onnxruntime as ort
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  import onnx
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  import numpy as np
 
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  elif prop.key == "input_size":
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  self.input_size = int(prop.value)
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  providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
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  self.session = ort.InferenceSession(onnx_path, providers=providers)
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  self.mean = np.array([0.485, 0.456, 0.406], dtype=np.float32).reshape(1, 3, 1, 1)
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  self.std = np.array([0.229, 0.224, 0.225], dtype=np.float32).reshape(1, 3, 1, 1)
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