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---
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.** |