metadata
license: apache-2.0
tags:
- image-captioning
languages:
- en
pipeline_tag: image-to-text
datasets:
- michelecafagna26/hl-narratives
language:
- en
metrics:
- sacrebleu
- rouge
library_name: transformers
GIT-base fine-tuned for Narrative Image Captioning
GIT base trained on the HL Narratives for high-level narrative descriptions generation
Model fine-tuning 🏋️
- Trained for a 3 epochs
- lr: 5e−5
- Adam optimizer
- half-precision (fp16)
Test set metrics 🧾
| Cider | SacreBLEU | Rouge-L|
|--------|------------|--------|
| 75.78 | 11.11 | 27.61 |
Model in Action 🚀
import requests
from PIL import Image
from transformers import AutoProcessor, AutoModelForCausalLM
processor = AutoProcessor.from_pretrained("git-base-captioning-ft-hl-narratives")
model = AutoModelForCausalLM.from_pretrained("git-base-captioning-ft-hl-narratives").to("cuda")
img_url = '/static-proxy?url=https%3A%2F%2Fdatasets-server.huggingface.co%2Fassets%2Fmichelecafagna26%2Fhl%2F--%2Fdefault%2Ftrain%2F0%2Fimage%2Fimage.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
inputs = processor(raw_image, return_tensors="pt").to("cuda")
pixel_values = inputs.pixel_values
generated_ids = model.generate(pixel_values=pixel_values, max_length=50,
do_sample=True,
top_k=120,
top_p=0.9,
early_stopping=True,
num_return_sequences=1)
processor.batch_decode(generated_ids, skip_special_tokens=True)
>>> "she is posing for a photo on the beach, she wants to post on her social media."
BibTex and citation info