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# Evaluate with Similarity Search
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## Introduction
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Build a classification system based on similarity search across embeddings.
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The core ideas in `run.py`:
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1. create a new Milvus collection each time
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2. extract embeddings using a pretrained model with model name specified by `--model`
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3. specify inference method with `--format` in value of `pytorch` or `onnx`
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4. insert & search embeddings with Milvus collection without index
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5. measure performance with accuracy at top 1, 5, 10
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1. vote for the prediction from topk search results (most frequent one)
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2. compare final prediction with ground truth
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3. calculate percent of correct predictions over all queries
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## Example Usage
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```bash
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python evaluate.py --model MODEL_NAME --format pytorch
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python evaluate.py --model MODEL_NAME --format onnx
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```
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