---
license: cc-by-nc-4.0
language:
- en
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- vision
- multimodal
- vision-language
- reasoning
- detection
- segmentation
- ocr
- vqa
- captioning
base_model:
- facebook/dinov2-large
- google/siglip-base-patch16-224
- Salesforce/blip-image-captioning-base
---

# Oculus 0.2

**A unified vision-language model with multi-modal reasoning capabilities.**

Oculus 0.2 is a hybrid-reasoning vision-language model that combines:
- **DINOv3** for semantic visual understanding
- **SigLIP2** for vision-language alignment
- **Trained Projector** for vision-to-language mapping
- **Optional Reasoning** via thinking traces

## 🚀 What's New in Oculus 0.2

| Feature | Description |
|---------|-------------|
| **🧠 Reasoning via Thinking Traces** | Short, structured reasoning traces improve multi-step decisions and ambiguous spatial tasks |
| **🔍 Focus System (Zoom & Crop)** | Automatically focus on smaller regions for fine-grained perception |
| **📦 Multiple Output Modes** | Text, Point, Box, and Polygon outputs for different tasks |
| **📝 Improved Captioning** | Better descriptions with context awareness |
| **❓ Enhanced VQA** | More accurate answers to visual questions |

## Output Modes

| Mode | Description | Use Case |
|------|-------------|----------|
| **📝 Text** | Natural language output | Captioning, VQA, descriptions |
| **📍 Point** | (x, y) coordinates + labels | Object counting, localization |
| **📦 Box** | Bounding boxes + labels | Object detection |
| **🔷 Polygon** | Segmentation masks | Semantic/instance segmentation |

## Quick Start

```python
from oculus_unified_model import OculusForConditionalGeneration
from PIL import Image

# Load model
model = OculusForConditionalGeneration.from_pretrained("OceanirAI/oculus-0.2")

# Load image
image = Image.open("your_image.jpg")

# Caption mode
output = model.generate(image, mode="text", prompt="Describe this image")
print(output.text)

# VQA mode
output = model.generate(image, mode="text", prompt="What color is the car?")
print(output.text)

# With reasoning traces
output = model.generate(image, mode="text", prompt="Count the people", think=True)
print(f"Thinking: {output.thinking_trace}")
print(f"Answer: {output.text}")

# Detection mode (bounding boxes)
output = model.generate(image, mode="box", prompt="Find all vehicles")
for box, label, conf in zip(output.boxes, output.labels, output.confidences):
    print(f"  {label}: {box} (conf={conf:.2f})")

# Point mode (counting)
output = model.generate(image, mode="point", prompt="Count the birds")
print(f"Found {len(output.points)} points")

# Segmentation mode
output = model.generate(image, mode="polygon", prompt="Segment the road")
print(f"Mask shape: {output.mask.shape}")
```

## Reasoning Mode

Enable thinking traces for complex reasoning tasks:

```python
output = model.generate(
    image,
    mode="text",
    prompt="How many people are sitting vs standing?",
    think=True  # Enable reasoning
)

print(f"💭 Thinking: {output.thinking_trace}")
print(f"📝 Answer: {output.text}")
```

## Focus System

The Focus system enables zoom-and-crop for fine-grained perception:

```python
output = model.generate(
    image,
    mode="text", 
    prompt="What does the small text say?",
    focus=True  # Enable focus/zoom
)
```

## Architecture

```
Image → DINOv3 ────┐
                   ├→ Fusion → Projector → 64 tokens × 1536D ───┐
Image → SigLIP2 ──┘                                             │
                                                                 ↓
                                              ┌─────────────────────────────────┐
                                              │                                 │
                                              ↓                                 ↓
                                         LM Head                         Task Heads
                                              │                                 │
                                              ↓                                 ↓
                                    Text/Caption/VQA              Point/Box/Polygon
```

## Model Details

| Component | Size | Description |
|-----------|------|-------------|
| DINOv3 Encoder | 1.0B | Semantic visual features |
| SigLIP2 Encoder | 400M | Vision-language aligned features |
| Projector | 160M | Vision-to-language bridge |
| Detection Head | 12M | Bounding box prediction |
| Point Head | 8M | Point localization |
| Segmentation Head | 24M | Mask prediction |
| **Total** | **~1.6B** | Full model |

## Training

The model components were trained in stages:
1. **Projector**: Trained on COCO Captions (5k paired images) for 3 epochs.
2. **Detection Heads**: Trained on COCO Detection for 5+ epochs using GIoU and Focal Loss.

## Benchmarks & Evaluation

We use a comprehensive benchmark suite `eval_benchmarks.py` covering:
- **COCO Detection**: mAP evaluation
- **Car Part Damage**: Specialized evaluation on HuggingFace `moondream/car_part_damage` dataset
- **Counting**: Accuracy on Pixmo-style counting tasks
- **VQA**: Open-ended question answering accuracy

To run benchmarks:
```bash
python eval_benchmarks.py --model checkpoints/oculus_detection_v2/final
```

## 🔌 Python API Usage

To use Oculus in your own applications, simply import the `OculusPredictor`:

```python
from oculus_inference import OculusPredictor

# Initialize (automatically loads best checkpoint)
model = OculusPredictor()

# 1. Object Detection
results = model.detect("image.jpg")
print(f"Found {len(results['boxes'])} objects")

# 2. Visual Question Answering (Reasoning)
answer = model.ask("image.jpg", "What is the person holding?")
print(f"Answer: {answer}")

# 3. Captioning
caption = model.caption("image.jpg")
print(f"Caption: {caption}")
```

## Requirements

```bash
pip install transformers torch pillow numpy
```

For Apple Silicon:
```bash
pip install mlx
```

## Citation

```bibtex
@misc{oculus2025,
  title={Oculus: Unified Vision-Language Model with Multi-Modal Reasoning},
  author={OceanirAI},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/OceanirAI/oculus-0.2}
}
```

## License

CC-BY-NC-4.0

## Contact

- **Organization**: OceanirAI
- **GitHub**: [github.com/Oceanir](https://github.com/Oceanir)