MagCrop-TMRA-v2

MagCrop-TMRA-v2

Model Description

TMRA (Task-Multi-Resolution Aggregator) is a BERT-based classifier that automatically determines the granularity level required for Remote Sensing Visual Question Answering tasks. The model classifies input queries into three categories: Image-level, Region-level, or Pixel-level precision requirements.

Model Details

• Base Model: bert-base-uncased
• Task: Multi-class Text Classification (3 classes)
• Classes:
  • image: Global scene understanding queries
  • region: Localized object detection queries
  • pixel: Fine-grained segmentation queries
• Training Data: Synthetic RS-VQA query dataset

Intended Use

TMRA is designed as a preprocessing component for the OmniCrop pipeline, enabling adaptive multi-granular processing of remote sensing imagery based on query semantics.

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("beingamanforever/MagCrop-TMRA-v2")
model = AutoModelForSequenceClassification.from_pretrained("beingamanforever/MagCrop-TMRA-v2")

# Classify query
query = "Count the number of white cars in the parking lot"
inputs = tokenizer(query, return_tensors="pt", padding=True, truncation=True)

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

granularity_map = {0: "image", 1: "region", 2: "pixel"}
print(f"Predicted Granularity: {granularity_map[prediction]}")

Example Predictions

Limitations

• Trained on synthetic RS-VQA queries; performance may vary on real-world datasets
• Optimized for English language queries
• May require fine-tuning for domain-specific terminology