rakib730 / output-models

Vision Transformer (ViT) Fine-Tuned Model

Vision Transformer (ViT) Fine-Tuned Model

This repository contains a fine-tuned version of google/vit-large-patch16-224 , optimized for a custom image classification task.

📌 Model Overview

• Base model : google/vit-large-patch16-224
• Architecture : Vision Transformer (ViT)
• Patch size : 16×16
• Image resolution : 224×224
• Frameworks : PyTorch, Hugging Face Transformers

📊 Performance

Training loss and validation loss trends indicate good convergence with slight overfitting after ~30 epochs.

🔧 Training Configuration

📂 Training Results

🛠 Intended Uses

• Image classification on datasets with characteristics similar to the training dataset.
• Fine-tuning for domain-specific classification tasks.

⚠ Limitations

• Trained on a custom dataset — may not generalize well to unrelated domains without additional fine-tuning.
• No guarantees on fairness, bias, or ethical implications without dataset analysis.

🚀 How to Use

You can use this model in two main ways:

1️⃣ Using the High-Level pipeline API

from transformers import pipeline

pipe = pipeline("image-classification", model="rakib730/output-models")

# Classify an image from a URL
result = pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")
print(result)

2️⃣ Using the Processor and Model Directly**
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import requests
import torch

# Load processor and model
processor = AutoImageProcessor.from_pretrained("rakib730/output-models")
model = AutoModelForImageClassification.from_pretrained("rakib730/output-models")

# Load an image
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

# Preprocess
inputs = processor(images=image, return_tensors="pt")

# Inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class_id = logits.argmax(-1).item()

print("Predicted class:", model.config.id2label[predicted_class_id])