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| Release | VQAv2 | GQA | TextVQA | DocVQA | TallyQA (simple/full) | POPE (rand/pop/adv) |
|---|---|---|---|---|---|---|
| 2024-08-26 (latest) | 80.3 | 64.3 | 65.2 | 70.5 | 82.6 / 77.6 | 89.6 / 88.8 / 87.2 |
| 2024-07-23 | 79.4 | 64.9 | 60.2 | 61.9 | 82.0 / 76.8 | 91.3 / 89.7 / 86.9 |
| 2024-05-20 | 79.4 | 63.1 | 57.2 | 30.5 | 82.1 / 76.6 | 91.5 / 89.6 / 86.2 |
| 2024-05-08 | 79.0 | 62.7 | 53.1 | 30.5 | 81.6 / 76.1 | 90.6 / 88.3 / 85.0 |
| 2024-04-02 | 77.7 | 61.7 | 49.7 | 24.3 | 80.1 / 74.2 | - |
| 2024-03-13 | 76.8 | 60.6 | 46.4 | 22.2 | 79.6 / 73.3 | - |
| 2024-03-06 | 75.4 | 59.8 | 43.1 | 20.9 | 79.5 / 73.2 | - |
| 2024-03-04 | 74.2 | 58.5 | 36.4 | - | - | - |
pip install transformers einops1from transformers import AutoModelForCausalLM, AutoTokenizer
2from PIL import Image
3
4model_id = "vikhyatk/moondream2"
5revision = "2024-08-26"
6model = AutoModelForCausalLM.from_pretrained(
7 model_id, trust_remote_code=True, revision=revision
8)
9tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
10
11image = Image.open('<IMAGE_PATH>')
12enc_image = model.encode_image(image)
13print(model.answer_question(enc_image, "Describe this image.", tokenizer))