MagCrop-TMRA-v2

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]}")