tonyassi/sales-pred-3

Title

Image Regression Model

This model was trained with Image Regression Model Trainer. It takes an image as input and outputs a float value.

from ImageRegression import predict
predict(repo_id='-',image_path='image.jpg')

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Dataset

Dataset:
Value Column:
Train Test Split:

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Training

Base Model: google/vit-base-patch16-224
Epochs:
Learning Rate:

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Usage

Download

git clone https://github.com/TonyAssi/ImageRegression.git
cd ImageRegression

Installation

pip install -r requirements.txt

Import

from ImageRegression import train_model, upload_model, predict

Inference (Prediction)

• repo_id 🤗 repo id of the model
• image_path path to image

predict(repo_id='-',
        image_path='image.jpg')

The first time this function is called it'll download the safetensor model. Subsequent function calls will run faster.

Train Model

• dataset_id 🤗 dataset id
• value_column_name column name of prediction values in dataset
• test_split test split of the train/test split
• output_dir the directory where the checkpoints will be saved
• num_train_epochs training epochs
• learning_rate learning rate

train_model(dataset_id='-',
            value_column_name='-',
            test_split=-,
            output_dir='./results',
            num_train_epochs=-,
            learning_rate=1e-)

The trainer will save the checkpoints in the output_dir location. The model.safetensors are the trained weights you'll use for inference (predicton).

Upload Model

This function will upload your model to the 🤗 Hub.

• model_id the name of the model id
• token go here to create a new 🤗 token
• checkpoint_dir checkpoint folder that will be uploaded

upload_model(model_id='-',
             token='YOUR_HF_TOKEN',
             checkpoint_dir='./results/checkpoint-940')