---
license: apache-2.0
language:
- en
base_model:
- google/bert_uncased_L-4_H-256_A-4
- WinKawaks/vit-small-patch16-224
pipeline_tag: image-to-text
library_name: transformers
tags:
- vit
- bert
- vision
- caption
- captioning
- image
---
An image captioning model, based on bert-mini and vit-small, weighing only 133mb!

Works very fast on CPU.

```python
from transformers import AutoTokenizer, AutoImageProcessor, VisionEncoderDecoderModel
import requests, torch, time
from PIL import Image

model_path = "cnmoro/mini-image-captioning"
device = torch.device("cpu")

# load the image captioning model and corresponding tokenizer and image processor
model = VisionEncoderDecoderModel.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
image_processor = AutoImageProcessor.from_pretrained(model_path)

# preprocess an image
url = "https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/New_york_times_square-terabass.jpg/800px-New_york_times_square-terabass.jpg"
image = Image.open(requests.get(url, stream=True).raw)
pixel_values = image_processor(image, return_tensors="pt").pixel_values

start = time.time()

# generate caption - suggested settings
generated_ids = model.generate(
    pixel_values,
    temperature=0.7,
    top_p=0.8,
    top_k=50,
    num_beams=3 # you can use 1 for even faster inference with a small drop in quality
)
generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

end = time.time()

print(generated_text)
# a large group of people walking through a busy city.

print(f"Time taken: {end - start} seconds")
# Time taken: 0.19002342224121094 seconds
# on CPU !
```