fine-tune-image-caption-pokemon

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#dataset used: polinaeterna/pokemon-blip-captions

#code

from transformers import AutoProcessor, AutoModelForCausalLM
import torch
from PIL import Image
import requests

#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.

from transformers import AutoProcessor

checkpoint = "microsoft/git-base"
processor = AutoProcessor.from_pretrained(checkpoint)

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

model_name = "kr-manish/git-base-pokemon"  # Replace with your actual username and model name
model = AutoModelForCausalLM.from_pretrained(model_name).to(device)

url =  "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"  # Replace with the URL of your image
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(images=image, return_tensors="pt").to(device)

generated_ids = model.generate(pixel_values=inputs.pixel_values, max_length=50)
generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_caption)

#a pink and purple pokemon character with big eyes

git-base-pokemon

This model is a fine-tuned version of microsoft/git-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.5797
• Wer Score: 8.9592

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 3e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.38.2
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2