metadata

tags:
  - image-classification
  - pytorch
  - vision
  - vit
  - transformers
license: apache-2.0
library_name: transformers
pipeline_tag: image-classification
model-index:
  - name: Image Quality Regression Model
    results: []

Image Quality Regression Model

This model is trained on the dataset yigagilbert/image_quality_dataset and performs regression tasks to predict image quality scores.

Model Details

Dataset: yigagilbert/image_quality_dataset
Target Column: quality_score
Test Split: 20% test data
Training Epochs: 3
Learning Rate: 5e-5
Max Value in Dataset: 54.02

This model fine-tunes the google/vit-base-patch16-224 Vision Transformer using PyTorch and Hugging Face's 🤗 Transformers library. It predicts a numerical score based on the quality of the input image.

Image Regression Model

This repository contains a model for image regression tasks, where the goal is to predict a numerical value from an input image. The model fine-tunes the google/vit-base-patch16-224 Vision Transformer using PyTorch and 🤗 Hugging Face tools. You can train the model, upload it to the 🤗 Model Hub, and perform inference using a simple API.

Installation

Install the required packages by running:

pip install -r requirements.txt

Usage

Import Functions

from ImageRegression import train_model, upload_model, predict

Train Model

Train the model using the train_model() function. Below are the key parameters:

dataset_id: Hugging Face dataset identifier or path to your local dataset.
value_column_name: Column in the dataset containing the target regression values.
test_split: Proportion of data to use for testing (e.g., 0.2 for 20% test data).
output_dir: Directory where model checkpoints will be saved.
num_train_epochs: Number of training epochs.
learning_rate: Learning rate for the optimizer.

train_model(dataset_id='yigagilbert/image_quality_dataset',
            value_column_name='quality_score',
            test_split=0.2,
            output_dir='./model_output',
            num_train_epochs=10,
            learning_rate=1e-4)

Training progress will be logged, and checkpoints will be saved in output_dir. These checkpoints can be used for model inference and uploaded to the 🤗 Hub.

Upload Model to Hugging Face Hub

To upload your trained model to the 🤗 Hub, use the upload_model() function:

model_id: The name of the model repository on the 🤗 Hub.
token: Authentication token (create one here).
checkpoint_dir: Directory where the trained model checkpoints are located.

upload_model(model_id='yigagilbert/image-qaulity-model',
             token='your_HF_token',
             checkpoint_dir='./model_output/checkpoint-940')

Once uploaded, the model can be used for inference directly from the Hub.

Model Inference (Prediction)

You can perform inference using the predict() function.

repo_id: The repository identifier of the uploaded model.
image_path: Path to the image file you want to run predictions on.

predict(repo_id='yigagilbert/image-qaulity-model',
        image_path='path_to_image.jpg')

The first time you run inference, the model will be downloaded from the Hugging Face Hub. Subsequent inferences will run faster as the model is cached locally.