File size: 4,698 Bytes
dc8eeaf 85d7c88 06ee5b6 ca1a6a5 66eea3e 06ee5b6 66eea3e ca1a6a5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 | ---
license: apache-2.0
datasets:
- yunusserhat/TurkishFoods-25
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- TurkishFoods-25
- SigLIP2
- 93M
---
# 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.
```py
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
```
---
## Label Space: 25 Classes
The model classifies food images into the following Turkish dishes:
```json
"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"
}
```
---
## Install Requirements
```bash
pip install -q transformers torch pillow gradio
```
---
## Inference Script
```python
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()
```
---
## Applications
* Turkish cuisine image datasets
* Food delivery or smart restaurant apps
* Culinary learning platforms
* Nutrition tracking via image-based recognition |