README.md

metadata

tags:
  - image-classification
  - document-classification
  - vision
library_name: transformers
pipeline_tag: image-classification
license: mit

Document Classification Model

Overview

This model is trained for document classification using vision transformers (DiT).

Model Details

• Architecture: Vision Transformer (DiT)
• Tasks: Document Classification
• Training Framework: 🤗 Transformers
• Base Model: microsoft/dit-large
• Training Dataset Size: 32786

Training Parameters

• Batch Size: 256
• Learning Rate: 0.001
• Number of Epochs: 90
• Mixed Precision: BF16
• Gradient Accumulation Steps: 2
• Weight Decay: 0.01
• Learning Rate Schedule: cosine_with_restarts
• Warmup Ratio: 0.1

Training and Evaluation Metrics

Training Metrics

• Loss: 0.1915
• Grad Norm: 1.3002
• Learning Rate: 0.0009
• Epoch: 26.4186
• Step: 1704.0000

Evaluation Metrics

• Loss: 0.9457
• Accuracy: 0.7757
• Weighted F1: 0.7689
• Micro F1: 0.7757
• Macro F1: 0.7518
• Weighted Recall: 0.7757
• Micro Recall: 0.7757
• Macro Recall: 0.7603
• Weighted Precision: 0.8023
• Micro Precision: 0.7757
• Macro Precision: 0.7941
• Runtime: 8.4106
• Samples Per Second: 433.1450
• Steps Per Second: 3.4480

Usage

from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image

# Load model and processor
processor = AutoImageProcessor.from_pretrained("jnmrr/ds3-img-classification")
model = AutoModelForImageClassification.from_pretrained("jnmrr/ds3-img-classification")

# Process an image
image = Image.open("document.png")
inputs = processor(image, return_tensors="pt")

# Make prediction
outputs = model(**inputs)
predicted_label = outputs.logits.argmax(-1).item()