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.
- Downloads last month
- 11
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "blackhole33/Image2text"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "blackhole33/Image2text", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'