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README.md

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+---
+license: apache-2.0
+base_model: Salesforce/blip-image-captioning-base
+datasets:
+- flickr8k
+language:
+- en
+- th
+pipeline_tag: image-to-text
+tags:
+- image-captioning
+- blip
+- vision-encoder-decoder
+- pytorch
+- fine-tuned
+---
+# 🖼️ BLIP Fine-Tuned on Flickr8k (Image Captioning)
+This repository contains a **Fine-Tuned BLIP (Bootstrapping Language-Image Pre-training)** model for **Image Captioning**, trained on the **Flickr8k** dataset.
+- **Base Model:** [`Salesforce/blip-image-captioning-base`](https://huggingface.co/Salesforce/blip-image-captioning-base)
+- **Developer:** Pokzy
+- **Interactive Web Demo:** [Hugging Face Space Demo](https://huggingface.co/spaces/Pokzy/image-captioning-blip)
+---
+## 📌 Model Description
+This model takes an image as input and generates descriptive English captions. It has been fine-tuned on the 8,000 daily images and human-annotated captions of the **Flickr8k** dataset to improve caption accuracy for everyday outdoor scenes, people, animals, and sports activities.
+In the associated web application, generated captions are also translated into **Thai (ภาษาไทย)** using the NLLB-200 translation model (`facebook/nllb-200-distilled-600M`).
+---
+## 🚀 How to Use
+You can easily load and use this fine-tuned model using Hugging Face's `transformers` library:
+```python
+import torch
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+# 1. Load Preprocessor & Fine-Tuned Model
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+model = BlipForConditionalGeneration.from_pretrained("Pokzy/flickr8k-finetuned")
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+model.eval()
+# 2. Load & Preprocess Image
+image_path = "example.jpg"
+image = Image.open(image_path).convert("RGB")
+inputs = processor(images=image, return_tensors="pt").to(device)
+# 3. Generate Caption
+with torch.no_grad():
+    output_ids = model.generate(
+        **inputs,
+        do_sample=True,
+        temperature=0.7,
+        top_p=0.9,
+        max_length=50
+    )
+caption = processor.decode(output_ids[0], skip_special_tokens=True)
+print("Generated Caption:", caption)