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TurkishFoods-25

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.

Classification Report:
 precision recall f1-score support

 asure 0.9718 0.9503 0.9609 181
 baklava 0.9589 0.9292 0.9438 452
 biber_dolmasi 0.9505 0.9555 0.9530 382
 borek 0.8770 0.8842 0.8806 613
 cig_kofte 0.9051 0.9358 0.9202 265
 enginar 0.9116 0.8753 0.8931 377
 et_sote 0.7870 0.7688 0.7778 346
 gozleme 0.9220 0.9420 0.9319 414
 hamsi 0.9724 0.9763 0.9744 253
hunkar_begendi 0.9583 0.9274 0.9426 248
 icli_kofte 0.9261 0.9353 0.9307 402
 ispanak 0.9567 0.9343 0.9454 213
 izmir_kofte 0.8763 0.9239 0.8995 368
 karniyarik 0.9538 0.8934 0.9226 347
 kebap 0.9154 0.8584 0.8860 706
 kisir 0.8919 0.9356 0.9132 388
 kuru_fasulye 0.8799 0.9820 0.9281 388
 lahmacun 0.9699 0.8703 0.9174 185
 lokum 0.9220 0.9369 0.9294 555
 manti 0.9569 0.9482 0.9525 328
 mucver 0.8743 0.9201 0.8966 363
 pirinc_pilavi 0.9110 0.9482 0.9292 367
 simit 0.9629 0.9284 0.9453 391
 taze_fasulye 0.8992 0.9253 0.9121 241
 yaprak_sarma 0.9742 0.9544 0.9642 395

 accuracy 0.9186 9168
 macro avg 0.9234 0.9216 0.9220 9168
 weighted avg 0.9194 0.9186 0.9186 9168

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Label Space: 25 Classes

The model classifies food images into the following Turkish dishes:

"id2label": {
 "0": "asure",
 "1": "baklava",
 "2": "biber_dolmasi",
 "3": "borek",
 "4": "cig_kofte",
 "5": "enginar",
 "6": "et_sote",
 "7": "gozleme",
 "8": "hamsi",
 "9": "hunkar_begendi",
 "10": "icli_kofte",
 "11": "ispanak",
 "12": "izmir_kofte",
 "13": "karniyarik",
 "14": "kebap",
 "15": "kisir",
 "16": "kuru_fasulye",
 "17": "lahmacun",
 "18": "lokum",
 "19": "manti",
 "20": "mucver",
 "21": "pirinc_pilavi",
 "22": "simit",
 "23": "taze_fasulye",
 "24": "yaprak_sarma"
}

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Install Requirements

pip install -q transformers torch pillow gradio

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Inference Script

import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch

model_name = "prithivMLmods/TurkishFoods-25" # Replace with your Hugging Face repo
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

id2label = {
 "0": "asure", "1": "baklava", "2": "biber_dolmasi", "3": "borek", "4": "cig_kofte",
 "5": "enginar", "6": "et_sote", "7": "gozleme", "8": "hamsi", "9": "hunkar_begendi",
 "10": "icli_kofte", "11": "ispanak", "12": "izmir_kofte", "13": "karniyarik", "14": "kebap",
 "15": "kisir", "16": "kuru_fasulye", "17": "lahmacun", "18": "lokum", "19": "manti",
 "20": "mucver", "21": "pirinc_pilavi", "22": "simit", "23": "taze_fasulye", "24": "yaprak_sarma"
}

def predict_food(image):
 image = Image.fromarray(image).convert("RGB")
 inputs = processor(images=image, return_tensors="pt")
 with torch.no_grad():
 logits = model(**inputs).logits
 probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
 return {id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))}

iface = gr.Interface(
 fn=predict_food,
 inputs=gr.Image(type="numpy"),
 outputs=gr.Label(num_top_classes=5, label="Top Turkish Foods"),
 title="TurkishFoods-25 Classifier",
 description="Upload a food image to identify one of 25 Turkish dishes."
)

if __name__ == "__main__":
 iface.launch()

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Applications

• Turkish cuisine image datasets
• Food delivery or smart restaurant apps
• Culinary learning platforms
• Nutrition tracking via image-based recognition

Base model

google/siglip2-base-patch16-224

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