|
|
--- |
|
|
license: mit |
|
|
--- |
|
|
|
|
|
# Model discription and Inference |
|
|
|
|
|
**Image to Text** modeli bu asosan pre-trained qilingan model ustiga fine-tuned qilindi juda kam dataset bilan. |
|
|
|
|
|
epoch soni : 50 ta |
|
|
|
|
|
loss: 0.03.... |
|
|
|
|
|
train_time: o'rtacha 45 minute. |
|
|
|
|
|
# test |
|
|
|
|
|
**Juda ham kam dataset bilan fine-tuned qilingani uchun , ko'rsatilgan dataset imagelaridan foydalanish tafsiya qilaman.** |
|
|
|
|
|
*Dataset* image and uning dscription holatidan bo'ladi. |
|
|
|
|
|
misol uchun : |
|
|
|
|
|
``` |
|
|
from datasets import load_dataset |
|
|
dataset = load_dataset("ybelkada/football-dataset", split="train") |
|
|
|
|
|
``` |
|
|
|
|
|
|
|
|
### Usage model |
|
|
|
|
|
``` |
|
|
from transformers import AutoProcessor, BlipForConditionalGeneration |
|
|
|
|
|
processor = AutoProcessor.from_pretrained("ai-nightcoder/Image2text") |
|
|
model = BlipForConditionalGeneration.from_pretrained("ai-nightcoder/Image2text") |
|
|
|
|
|
``` |
|
|
# image olamiz |
|
|
|
|
|
``` |
|
|
example = dataset[0] |
|
|
image = example["image"] |
|
|
image |
|
|
|
|
|
``` |
|
|
|
|
|
|
|
|
#### generate qismi. |
|
|
|
|
|
``` |
|
|
inputs = processor(images=image, return_tensors="pt").to(device) |
|
|
pixel_values = inputs.pixel_values |
|
|
|
|
|
generated_ids = model.generate(pixel_values=pixel_values, max_length=50) |
|
|
generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] |
|
|
print(generated_caption) |
|
|
|
|
|
``` |
|
|
|
|
|
**Yuqorida ko'rsatgan tartibda modeldan foydalanishni tavsiya qilaman.** |
