tony__assi-ig-prediction

IG Prediction

This model was trained with IGPrediction. It predicts how many likes an image will get.

from IGPredict import predict_ig
predict_ig(repo_id='tonyassi/tony__assi-ig-prediction',image_path='image.jpg')

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Dataset

Dataset: tonyassi/tony__assi-ig-ds5
Value Column: 'likes'
Train Test Split: 0.2

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Training

Base Model: google/vit-base-patch16-224
Epochs: 20
Learning Rate: 0.0001

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Usage

Download

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

Installation

pip install -r requirements.txt

Import

from IGPredict import ig_download, upload_dataset, train_ig_model, upload_ig_model, predict_ig

Download Instagram Images

• username Instagram username
• num_images maximum number of images to download

ig_download(username='instagarm_username', num_images=100)

Instagram images will be downloaded to './images' folder, each one named like so "index-likes.jpg". E.g. "3-17.jpg" is the third image and has 17 likes.

Upload Dataset

• dataset_name name of dataset to be uploaded
• token go here to create a new 🤗 token

upload_dataset(dataset_name='tonyassi/tony__assi-ig-ds5', token='YOUR_HF_TOKEN')

Go to your 🤗 profile to find your uploaded dataset, it should look similar to tonyassi/tony__assi-ig-ds.

Train Model

• dataset_id 🤗 dataset id
• test_split test split of the train/test split
• num_train_epochs training epochs
• learning_rate learning rate

train_ig_model(dataset_id='tonyassi/tony__assi-ig-ds5',
               test_split=0.2,
               num_train_epochs=20,
               learning_rate=0.0001)

The trainer will save the checkpoints in the 'results' folder. 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_ig_model(model_id='tony__assi-ig-prediction',
             token='YOUR_HF_TOKEN',
             checkpoint_dir='./results/checkpoint-940')

Inference (Prediction)

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

predict_ig(repo_id='tonyassi/tony__assi-ig-prediction',
        image_path='image.jpg')

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