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--- |
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license: apache-2.0 |
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datasets: |
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- yunusserhat/TurkishFoods-25 |
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language: |
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- en |
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base_model: |
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- google/siglip2-base-patch16-224 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- TurkishFoods-25 |
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- SigLIP2 |
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- 93M |
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--- |
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# TurkishFoods-25 |
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> **TurkishFoods-25** is a computer vision model fine-tuned from `google/siglip2-base-patch16-224` for multi-class food image classification. It is trained to identify 25 traditional Turkish dishes using the `SiglipForImageClassification` architecture. |
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```py |
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Classification Report: |
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precision recall f1-score support |
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asure 0.9718 0.9503 0.9609 181 |
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baklava 0.9589 0.9292 0.9438 452 |
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biber_dolmasi 0.9505 0.9555 0.9530 382 |
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borek 0.8770 0.8842 0.8806 613 |
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cig_kofte 0.9051 0.9358 0.9202 265 |
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enginar 0.9116 0.8753 0.8931 377 |
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et_sote 0.7870 0.7688 0.7778 346 |
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gozleme 0.9220 0.9420 0.9319 414 |
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hamsi 0.9724 0.9763 0.9744 253 |
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hunkar_begendi 0.9583 0.9274 0.9426 248 |
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icli_kofte 0.9261 0.9353 0.9307 402 |
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ispanak 0.9567 0.9343 0.9454 213 |
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izmir_kofte 0.8763 0.9239 0.8995 368 |
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karniyarik 0.9538 0.8934 0.9226 347 |
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kebap 0.9154 0.8584 0.8860 706 |
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kisir 0.8919 0.9356 0.9132 388 |
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kuru_fasulye 0.8799 0.9820 0.9281 388 |
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lahmacun 0.9699 0.8703 0.9174 185 |
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lokum 0.9220 0.9369 0.9294 555 |
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manti 0.9569 0.9482 0.9525 328 |
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mucver 0.8743 0.9201 0.8966 363 |
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pirinc_pilavi 0.9110 0.9482 0.9292 367 |
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simit 0.9629 0.9284 0.9453 391 |
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taze_fasulye 0.8992 0.9253 0.9121 241 |
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yaprak_sarma 0.9742 0.9544 0.9642 395 |
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accuracy 0.9186 9168 |
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macro avg 0.9234 0.9216 0.9220 9168 |
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weighted avg 0.9194 0.9186 0.9186 9168 |
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``` |
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--- |
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## Label Space: 25 Classes |
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The model classifies food images into the following Turkish dishes: |
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```json |
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"id2label": { |
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"0": "asure", |
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"1": "baklava", |
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"2": "biber_dolmasi", |
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"3": "borek", |
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"4": "cig_kofte", |
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"5": "enginar", |
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"6": "et_sote", |
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"7": "gozleme", |
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"8": "hamsi", |
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"9": "hunkar_begendi", |
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"10": "icli_kofte", |
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"11": "ispanak", |
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"12": "izmir_kofte", |
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"13": "karniyarik", |
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"14": "kebap", |
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"15": "kisir", |
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"16": "kuru_fasulye", |
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"17": "lahmacun", |
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"18": "lokum", |
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"19": "manti", |
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"20": "mucver", |
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"21": "pirinc_pilavi", |
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"22": "simit", |
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"23": "taze_fasulye", |
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"24": "yaprak_sarma" |
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} |
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``` |
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--- |
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## Install Requirements |
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```bash |
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pip install -q transformers torch pillow gradio |
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``` |
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--- |
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## Inference Script |
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```python |
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import gradio as gr |
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from transformers import AutoImageProcessor, SiglipForImageClassification |
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from PIL import Image |
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import torch |
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model_name = "prithivMLmods/TurkishFoods-25" # Replace with your Hugging Face repo |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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id2label = { |
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"0": "asure", "1": "baklava", "2": "biber_dolmasi", "3": "borek", "4": "cig_kofte", |
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"5": "enginar", "6": "et_sote", "7": "gozleme", "8": "hamsi", "9": "hunkar_begendi", |
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"10": "icli_kofte", "11": "ispanak", "12": "izmir_kofte", "13": "karniyarik", "14": "kebap", |
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"15": "kisir", "16": "kuru_fasulye", "17": "lahmacun", "18": "lokum", "19": "manti", |
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"20": "mucver", "21": "pirinc_pilavi", "22": "simit", "23": "taze_fasulye", "24": "yaprak_sarma" |
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} |
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def predict_food(image): |
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image = Image.fromarray(image).convert("RGB") |
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inputs = processor(images=image, return_tensors="pt") |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() |
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return {id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))} |
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iface = gr.Interface( |
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fn=predict_food, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(num_top_classes=5, label="Top Turkish Foods"), |
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title="TurkishFoods-25 Classifier", |
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description="Upload a food image to identify one of 25 Turkish dishes." |
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) |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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--- |
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## Applications |
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* Turkish cuisine image datasets |
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* Food delivery or smart restaurant apps |
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* Culinary learning platforms |
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* Nutrition tracking via image-based recognition |
