git-base-on-diffuision-dataset2

This model is a fine-tuned version of microsoft/git-base on hieudinhpro/diffuision-dataset2 dataset.

Model description

GIT (short for GenerativeImage2Text) model, base-sized version.
It was introduced in the paper GIT: A Generative Image-to-text Transformer for Vision and Language

Model train for task : Sketch Scene image to text

How to use mdoel

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("microsoft/git-base")
model = AutoModelForCausalLM.from_pretrained("hieudinhpro/git-base-on-diffuision-dataset2")

# load image
from PIL import Image

image = Image.open('/content/image_3.jpg')

# pre image
inputs = processor(images=image, return_tensors="pt")
pixel_values = inputs.pixel_values

# predict 
generated_ids = model.generate(pixel_values=pixel_values, max_length=50)

# decode to text
generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_caption)

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 2
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 1

Framework versions

• Transformers 4.34.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.5
• Tokenizers 0.14.0