# Image captioning

[[open-in-colab]]

Image captioning is the task of predicting a caption for a given image. Common real world applications of it include

aiding visually impaired people that can help them navigate through different situations. Therefore, image captioning

helps to improve content accessibility for people by describing images to them.

This guide will show you how to:

* Fine-tune an image captioning model.

* Use the fine-tuned model for inference. 

Before you begin, make sure you have all the necessary libraries installed:

```bash

pip install transformers datasets evaluate -q

pip install jiwer -q

We encourage you to log in to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to log in:

thon

from huggingface_hub import notebook_login

notebook_login()

## Load the Pokémon BLIP captions dataset

Use the 🤗 Dataset library to load a dataset that consists of {image-caption} pairs. To create your own image captioning dataset

in PyTorch, you can follow [this notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/GIT/Fine_tune_GIT_on_an_image_captioning_dataset.ipynb). 

thon

from datasets import load_dataset

ds = load_dataset("lambdalabs/pokemon-blip-captions")

ds

```bash

DatasetDict({

    train: Dataset({

        features: ['image', 'text'],

        num_rows: 833

    })

})

The dataset has two features, `image` and `text`.

Many image captioning datasets contain multiple captions per image. In those cases, a common strategy is to randomly sample a caption amongst the available ones during training. 

Split the dataset’s train split into a train and test set with the [~datasets.Dataset.train_test_split] method:

thon

ds = ds["train"].train_test_split(test_size=0.1)

train_ds = ds["train"]

test_ds = ds["test"]

Let's visualize a couple of samples from the training set. 

thon

from textwrap import wrap

import matplotlib.pyplot as plt

import numpy as np

def plot_images(images, captions):

    plt.figure(figsize=(20, 20))

    for i in range(len(images)):

        ax = plt.subplot(1, len(images), i + 1)

        caption = captions[i]

        caption = "\n".join(wrap(caption, 12))

        plt.title(caption)

        plt.imshow(images[i])

        plt.axis("off")

sample_images_to_visualize = [np.array(train_ds[i]["image"]) for i in range(5)]

sample_captions = [train_ds[i]["text"] for i in range(5)]

plot_images(sample_images_to_visualize, sample_captions)

    

## Preprocess the dataset

Since the dataset has two modalities (image and text), the pre-processing pipeline will preprocess images and the captions.

To do so, load the processor class associated with the model you are about to fine-tune. 

thon

from transformers import AutoProcessor

checkpoint = "microsoft/git-base"

processor = AutoProcessor.from_pretrained(checkpoint)

The processor will internally pre-process the image (which includes resizing, and pixel scaling) and tokenize the caption. 

thon

def transforms(example_batch):

    images = [x for x in example_batch["image"]]

    captions = [x for x in example_batch["text"]]

    inputs = processor(images=images, text=captions, padding="max_length")

    inputs.update({"labels": inputs["input_ids"]})

    return inputs

train_ds.set_transform(transforms)

test_ds.set_transform(transforms)

With the dataset ready, you can now set up the model for fine-tuning. 

## Load a base model

Load the ["microsoft/git-base"](https://huggingface.co/microsoft/git-base) into a [`AutoModelForCausalLM`](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForCausalLM) object.

thon

from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(checkpoint)

## Evaluate

Image captioning models are typically evaluated with the [Rouge Score](https://huggingface.co/spaces/evaluate-metric/rouge) or [Word Error Rate](https://huggingface.co/spaces/evaluate-metric/wer). For this guide, you will use the Word Error Rate (WER). 

We use the 🤗 Evaluate library to do so. For potential limitations and other gotchas of the WER, refer to [this guide](https://huggingface.co/spaces/evaluate-metric/wer). 

thon

from evaluate import load

import torch

wer = load("wer")

def compute_metrics(eval_pred):

    logits, labels = eval_pred

    predicted = logits.argmax(-1)

    decoded_labels = processor.batch_decode(labels, skip_special_tokens=True)

    decoded_predictions = processor.batch_decode(predicted, skip_special_tokens=True)

    wer_score = wer.compute(predictions=decoded_predictions, references=decoded_labels)

    return {"wer_score": wer_score}

## Train!

Now, you are ready to start fine-tuning the model. You will use the 🤗 [`Trainer`] for this. 

First, define the training arguments using [`TrainingArguments`].

thon

from transformers import TrainingArguments, Trainer

model_name = checkpoint.split("/")[1]

training_args = TrainingArguments(

    output_dir=f"{model_name}-pokemon",

    learning_rate=5e-5,

    num_train_epochs=50,

    fp16=True,

    per_device_train_batch_size=32,

    per_device_eval_batch_size=32,

    gradient_accumulation_steps=2,

    save_total_limit=3,

    evaluation_strategy="steps",

    eval_steps=50,

    save_strategy="steps",

    save_steps=50,

    logging_steps=50,

    remove_unused_columns=False,

    push_to_hub=True,

    label_names=["labels"],

    load_best_model_at_end=True,

)

Then pass them along with the datasets and the model to 🤗 Trainer. 

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trainer = Trainer(

    model=model,

    args=training_args,

    train_dataset=train_ds,

    eval_dataset=test_ds,

    compute_metrics=compute_metrics,

)

To start training, simply call [`~Trainer.train`] on the [`Trainer`] object.

thon 

trainer.train()

You should see the training loss drop smoothly as training progresses.

Once training is completed, share your model to the Hub with the [`~Trainer.push_to_hub`] method so everyone can use your model:

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trainer.push_to_hub()

## Inference

Take a sample image from `test_ds` to test the model.

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from PIL import Image

import requests

url = "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"

image = Image.open(requests.get(url, stream=True).raw)

image

    

Prepare image for the model.

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device = "cuda" if torch.cuda.is_available() else "cpu"

inputs = processor(images=image, return_tensors="pt").to(device)

pixel_values = inputs.pixel_values

Call [`generate`] and decode the predictions. 

thon

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

generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

print(generated_caption)

```bash

a drawing of a pink and blue pokemon

Looks like the fine-tuned model generated a pretty good caption!