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