Image to text using blip2 gives incorrect answer

[rxshah]

Here is code snippet slightly modified from blip2 site:

first prompt “Question: How many cats are there? Answer:” –> gives correct answer Two

However, second prompt “Question: How many dogs are there? Answer:” –> gives incorrect answer - Two should be Zero or None.

Is this because the accuracy of the trained model is not 100% we should get incorrect answers? OR AM I doing something incorrectly?

Here is the complete code:

from PIL import Image
import requests
from transformers import Blip2Processor, Blip2ForConditionalGeneration
import torch

device = “cuda” if torch.cuda.is_available() else “cpu”

processor = Blip2Processor.from_pretrained(“Salesforce/blip2-opt-2.7b”)
model = Blip2ForConditionalGeneration.from_pretrained(
“Salesforce/blip2-opt-2.7b”, torch_dtype=torch.float16
)
model.to(device)

url = “http://images.cocodataset.org/val2017/000000039769.jpg”
image = Image.open(requests.get(url, stream=True).raw)

prompt = “Question: How many cats are there? Answer:”
inputs = processor(images=image, text=prompt, return_tensors=“pt”).to(
device, torch.float16
)

outputs = model.generate(**inputs)

text = processor.tokenizer.batch_decode(outputs, skip_special_tokens=True)
print(text)

Gives correct answer: [‘Question: How many cats are there? Answer: Two\n’]

However, when I change prompt to

prompt2 = "Question: How many dogs are there? Answer: "

inputs2 = processor(images=image, text=prompt2, return_tensors=“pt”).to(
device, torch.float16
)

outputs2 = model.generate(**inputs2)

text2 = processor.tokenizer.batch_decode(outputs2, skip_special_tokens=True)
print(text2)

[‘Question: How many dogs are there? Answer: Two\n’]

[John6666]

OR AM I doing something incorrectly?

There’s no problem with the code; it seems to be a known issue with the model / architecture. You might want to try using some fine-tuned version.

[rxshah]

Thanks!!

Tried the examples you pointed to. The number of dogs still gave Two. However, following the examples further got following results:

55.3% that image 0 is 'a photo of a cat'
44.7% that image 0 is 'a photo of a dog'

Perhaps this explains why the model cannot distinguish between cats, dogs or anything else?

[John6666]

Yeah. For example, CLIP can perfectly classify dogs and cats, but BLIP seems utterly unsuitable for classification…

[rxshah]

Thanks for the clear explanation!!