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---
license: cc-by-nc-4.0
datasets:
- issai/Central_Asian_Food_Dataset
language:
- en
base_model:
- microsoft/swinv2-base-patch4-window16-256
pipeline_tag: image-classification
library_name: transformers
tags:
- classification
- image
- pytorch
- safetensors
co2_eq_emissions:
emissions: 0.054843
source: code carbon
training_type: fine-tuning
geographical_location: Oregon, USA (45.5999, -121.1871)
hardware_used: 2x Tesla T4 GPUs, Intel Xeon CPU (4 cores), 31.35 GB RAM
---
# Central Asian Food Classification
## Model Information
- **Base Model**: [microsoft/swinv2-base-patch4-window16-256](https://huggingface.co/microsoft/swinv2-base-patch4-window16-256)
- **Dataset**: [issai/Central_Asian_Food_Dataset](https://huggingface.co/datasets/issai/Central_Asian_Food_Dataset)
- **Library**: `transformers`, `pytorch`
- **Pipeline**: Image Classification
- **License**: Creative Commons Attribution Non Commercial 4.0
## Model Description
- This model classifies images of Central Asian dishes into 42 different categories.
- The model is fine-tuned on the Central Asian Food Dataset using Swin Transformer v2 architecture.
- The training was conducted on 2 Tesla T4 GPUs in Oregon, USA.
## Labels (Classes)
```python
class_names = [
"achichuk", "airan-katyk", "asip", "bauyrsak", "beshbarmak-w-kazy",
"beshbarmak-wo-kazy", "chak-chak", "cheburek", "doner-lavash", "doner-nan",
"hvorost", "irimshik", "kattama-nan", "kazy-karta", "kurt", "kuyrdak",
"kymyz-kymyran", "lagman-fried", "lagman-w-soup", "lagman-wo-soup", "manty",
"naryn", "nauryz-kozhe", "orama", "plov", "samsa", "shashlyk-chicken",
"shashlyk-chicken-v", "shashlyk-kuskovoi", "shashlyk-kuskovoi-v",
"shashlyk-minced-meat", "sheep-head", "shelpek", "shorpa", "soup-plain",
"sushki", "suzbe", "taba-nan", "talkan-zhent", "tushpara-fried",
"tushpara-w-soup", "tushpara-wo-soup"
]
```
## Training
```
training_args = TrainingArguments(
output_dir="./swinv2_classification",
evaluation_strategy="epoch",
save_strategy="epoch",
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
num_train_epochs=5,
weight_decay=0.01,
logging_dir="./logs",
logging_steps=10
)
```
```
Epoch Training Loss Validation Loss
1 0.815700 0.741029
2 0.454500 0.641849
3 0.100500 0.680114
4 0.030000 0.704669
5 0.009000 0.661318
```
## Evaluation Metrics
The model achieved **87% accuracy** on the validation set. Below is the classification report with precision, recall, and F1-score for each class:
```
accuracy 0.87 2735
macro avg 0.86 0.85 0.85 2735
weighted avg 0.88 0.87 0.87 2735
```
## Environmental Impact
The estimated carbon emissions from training this model:
- **Emissions**: 0.054843 grams CO2
- **Source**: Code Carbon
- **Training Type**: Fine-tuning
- **Location**: Oregon, USA (45.5999, -121.1871)
- **Hardware Used**: 2x Tesla T4 GPUs, Intel Xeon CPU (4 cores), 31.35 GB RAM
## Usage
To use this model for inference:
```python
import requests
from io import BytesIO
from PIL import Image
from transformers import pipeline
# Load the model
pipe = pipeline("image-classification", model="Eraly-ml/centraasia-Swinv2", device=0)
# Image URL
image_url = "https://avatars.mds.yandex.net/get-altay/12813969/2a0000018e10a3da6a2a1d1d2c2745548220/XXXL"
# Download the image from the internet
response = requests.get(image_url)
image = Image.open(BytesIO(response.content))
# Model classes
class_names = [
"achichuk", "airan-katyk", "asip", "bauyrsak", "beshbarmak-w-kazy",
"beshbarmak-wo-kazy", "chak-chak", "cheburek", "doner-lavash", "doner-nan",
"hvorost", "irimshik", "kattama-nan", "kazy-karta", "kurt", "kuyrdak",
"kymyz-kymyran", "lagman-fried", "lagman-w-soup", "lagman-wo-soup", "manty",
"naryn", "nauryz-kozhe", "orama", "plov", "samsa", "shashlyk-chicken",
"shashlyk-chicken-v", "shashlyk-kuskovoi", "shashlyk-kuskovoi-v",
"shashlyk-minced-meat", "sheep-head", "shelpek", "shorpa", "soup-plain",
"sushki", "suzbe", "taba-nan", "talkan-zhent", "tushpara-fried",
"tushpara-w-soup", "tushpara-wo-soup"
]
# Make a prediction
predictions = pipe(image)
# Display results with correct labels
for pred in predictions:
label_id = int(pred["label"].replace("LABEL_", "")) # Extract the number
class_name = class_names[label_id] # Get the class name
score = pred["score"] # Probability
print(f"Class: {class_name}, probability: {score:.4f}")
```
## Citation
If you use this model, please cite:
```
@misc{CentralAsianFood,
author = {Eraly Gainulla},
title = {Central Asian Food Classification Model},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/Eraly-ml/centraasia-Swinv2}
}
``` |