---
license: mit
base_model: microsoft/git-base
tags:
- generated_from_trainer
model-index:
- name: git-base-on-diffuision-dataset2
  results: []
language:
- en
library_name: transformers
pipeline_tag: image-to-text
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# git-base-on-diffuision-dataset2

This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/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