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Jael Gu
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# Evaluate with Similarity Search |
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## Introduction |
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Build a image 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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