Centraasia ResNet 50

🚀 ResNet-50 Model for Central Asian Image Classification

This pre - trained ResNet - 50 model is fine - tuned on the Central Asian Food Dataset, designed for multi - class image classification.

✨ Features

• Utilizes a pre - trained ResNet - 50 architecture.
• Fine - tuned on the Central Asian Food Dataset for image classification.
• Trained with specific hyperparameters for optimal performance.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForImageClassification
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file

repo_id = "Eraly-ml/centraasia-ResNet-50"
filename = "model.safetensors"

# Load model
model_path = hf_hub_download(repo_id=repo_id, filename=filename)
model = AutoModelForImageClassification.from_pretrained(repo_id)
model.load_state_dict(load_file(model_path))

📚 Documentation

Model Description

This is a pre - trained ResNet - 50 model fine - tuned on the Central Asian Food Dataset. The model is used for image classification across multiple classes. The data was split into training, validation, and test sets. The model was trained using gradient descent with an SGD optimizer and CrossEntropyLoss as the loss function.

Training Parameters

• Epochs: 25
• Batch Size: 32
• Learning Rate: 0.001
• Optimizer: SGD with momentum of 0.9
• Loss Function: CrossEntropyLoss

Results

Training and Validation, F1

Model was trained on two T4 GPUs in a Kaggle notebook trained 36m 7s
Best validation accuracy: 86,54%

                      precision    recall  f1-score   support

            achichuk       0.91      0.98      0.94        41
         airan-katyk       0.84      0.93      0.89        46
                asip       0.78      0.57      0.66        37
            bauyrsak       0.90      0.90      0.90        62
   beshbarmak-w-kazy       0.71      0.84      0.77        44
  beshbarmak-wo-kazy       0.86      0.69      0.76        61
           chak-chak       0.94      0.94      0.94        93
            cheburek       0.92      0.88      0.90        94
        doner-lavash       0.77      1.00      0.87        20
           doner-nan       0.86      0.82      0.84        22
             hvorost       0.98      0.86      0.91       141
            irimshik       0.96      0.94      0.95       175
         kattama-nan       0.84      0.88      0.86        66
          kazy-karta       0.72      0.78      0.75        46
                kurt       0.86      0.97      0.91        61
             kuyrdak       0.92      0.93      0.92        58
       kymyz-kymyran       0.93      0.82      0.87        49
        lagman-fried       0.86      0.95      0.90        38
       lagman-w-soup       0.90      0.80      0.85        75
      lagman-wo-soup       0.58      0.86      0.69        22
               manty       0.91      0.95      0.93        63
               naryn       0.97      0.99      0.98        84
        nauryz-kozhe       0.88      0.96      0.92        52
               orama       0.68      0.84      0.75        38
                plov       0.95      0.98      0.97       101
               samsa       0.91      0.93      0.92       106
    shashlyk-chicken       0.68      0.65      0.66        62
  shashlyk-chicken-v       0.74      0.76      0.75        33
   shashlyk-kuskovoi       0.75      0.75      0.75        71
 shashlyk-kuskovoi-v       0.53      0.79      0.64        29
shashlyk-minced-meat       0.74      0.69      0.72        42
          sheep-head       0.75      0.94      0.83        16
             shelpek       0.77      0.86      0.81        64
              shorpa       0.95      0.88      0.91        80
          soup-plain       0.96      0.94      0.95        71
              sushki       0.83      1.00      0.91        43
               suzbe       0.89      0.82      0.86        62
            taba-nan       0.92      0.80      0.86       136
        talkan-zhent       0.86      0.80      0.83        90
      tushpara-fried       0.79      0.74      0.76        46
     tushpara-w-soup       0.94      0.94      0.94        67
    tushpara-wo-soup       0.92      0.87      0.89        91

            accuracy                           0.87      2698
           macro avg       0.84      0.86      0.85      2698
        weighted avg       0.88      0.87      0.87      2698

Testing

After training, the model was tested on the test set:

• Test accuracy: 87%

Repository Structure

• main.py — Code for training and testing the model
• model/ — Saved model in SafeTensors format

🔧 Technical Details

The model is based on the ResNet - 50 architecture from Microsoft. It is fine - tuned on the Central Asian Food Dataset. The data was split into training, validation, and test sets. The model was trained using gradient descent with an SGD optimizer and CrossEntropyLoss as the loss function. The training was carried out on two T4 GPUs in a Kaggle notebook for 36m 7s.

📄 License

The model is licensed under the CC - BY - NC - 4.0 license.

💡 Usage Tip

You can contact the author on Telegram: @eralyf