---
language: en
license: mit
tags:
  - text-classification
  - nlp
  - transformers
  - bert
  - routing
  - vision-task-classifier
model_name: ICM
base_model: bert-base-uncased
pipeline_tag: text-classification
datasets:
  - synthetic
tasks:
  - text-classification
library_name: transformers
---

# Task Classification Model (ICM)

## Model Description

A BERT-based sequence classification model that routes computer vision questions to appropriate specialized modules. Classifies questions into 4 task categories: VQA, Captioning, Grounding, and Geometry.

- **Repository:** beingamanforever/ICM
- **Base Model:** bert-base-uncased
- **Task:** 4-way Sequence Classification

## Labels

| ID | Label | Description |
|---|---|---|
| 0 | vqa | Visual Question Answering ("What color is the car?") |
| 1 | captioning | Image Description ("Describe the sunset.") |
| 2 | grounding | Object Localization ("Find the person in the image.") |
| 3 | geometry | Spatial/Metric Queries ("Calculate the area of the red box.") |

## Architecture

BERT-Base encoder + 3-layer MLP classifier on [CLS] token:

- Layer 1: Linear(768 → 256) + ReLU + Dropout(0.1)
- Layer 2: Linear(256 → 128) + ReLU + Dropout(0.1)  
- Layer 3: Linear(128 → 4)

## Training

| Hyperparameter | Value |
|---|---|
| Samples | 1,600 (400 per class) |
| Epochs | 5 |
| Learning Rate | 2e-5 |
| Batch Size | 32 |
| Optimizer | AdamW |
| Loss | Cross Entropy |

**Data:** Synthetic questions from balanced JSON files (vqa_qs.json, captioning_qs.json, grounding_qs.json, geometry_qs.json)

## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "beingamanforever/ICM"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

questions = [
    "What is the distance between the two trees?",
    "Describe what the child is wearing.",
    "Is the traffic light green?",
    "Box the location of the blue umbrella."
]

inputs = tokenizer(questions, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
    logits = model(**inputs).logits
    predictions = torch.argmax(logits, dim=-1)

for q, pred in zip(questions, predictions):
    print(f"{q} → {model.config.id2label[pred.item()]}")
```

## Limitations

- **Synthetic Training Data:** May not generalize to complex real-world queries
- **Text-Only:** Processes questions without image context
- **Domain Scope:** Optimized for vision task routing, not general NLP classification

## Intended Use

- Automatic query routing in multimodal AI pipelines
- VQA dataset analysis and taxonomy studies
- Educational demonstrations of vision task classification