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README.md

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+# Banknote Embedder
+
+Fine-tuned visual embedding model for Nepali banknotes.
+
+It maps an input banknote image to a 512-d normalized embedding vector.  
+You can compare two notes using cosine similarity:
+- higher score -> more similar
+- lower score -> less similar
+
+## Model Files
+
+- `banknote_embedder_finetuned.pth`: training state_dict
+- `banknote_embedder_fp16.safetensors`: compressed inference weights (recommended)
+- `banknote_embedder_int8_dynamic.pth`: dynamic int8 export for supported CPU backends
+
+## Quantize / Export
+
+Generate compressed artifacts from your fine-tuned weights:
+
+```bash
+python quantize_model.py --input-model banknote_embedder_finetuned.pth --skip-int8
+```
+
+Outputs:
+- `banknote_embedder_fp16.safetensors`
+
+Optional (if your CPU backend supports it):
+
+```bash
+python quantize_model.py --input-model banknote_embedder_finetuned.pth
+```
+
+Additional output:
+- `banknote_embedder_int8_dynamic.pth`
+
+## Quick Test
+
+Run similarity test:
+
+```bash
+python test.py --weights banknote_embedder_fp16.safetensors --img1 notes/20.jpg --img2 notes/20.jpg --img3 notes/22.jpg
+```
+
+Expected behavior:
+- `img1 vs img2` should be high (often close to `1.0` for identical image)
+- `img1 vs img3` should be lower
+
+## Usage Notes
+
+- Input preprocessing uses ImageNet normalization and `224x224` resize.
+- Embeddings are L2-normalized in the model forward pass.
+- `xFormers is not available` warnings are expected and non-fatal.