metadata

license: mit
datasets:
  - issai/Central_Asian_Food_Dataset
language:
  - en
metrics:
  - accuracy
base_model:
  - microsoft/resnet-50
pipeline_tag: image-classification
tags:
  - classification
  - image
  - resnet
  - pytorch
  - safetensors

ResNet-50 Model for Central Asian Image Classification

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

Stage	Loss (train)	Accuracy (train)	Loss (val)	Accuracy (val)
Epoch 1	1.2345	85.00%	1.4567	82.00%
Epoch 2	1.0456	86.00%	1.2345	83.00%
...	...	...	...	...
Epoch 25	0.6789	90.00%	0.7890	87.00%

Model was trained on two T4 GPUs in a Kaggle notebook
Best validation accuracy: 87%

Testing

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

Test accuracy: 87%

Repository Structure

model.py — Code for training and testing the model
dataset/ — Folder containing the data (train, val, test)
trained_model/ — Saved model in SafeTensors format

Usage Instructions

Download the model:
- Go to the Hugging Face model link.
Load and use the model in your project:

from transformers import AutoModelForImageClassification

model = AutoModelForImageClassification.from_pretrained('path_to_model')

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