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docs/BENCHMARK_README.md

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+# Oculus Model Benchmarking Guide
+
+This guide explains how to use the `test_benchmarks.py` script to evaluate the Oculus vision-language model on standard benchmark tasks.
+
+## Overview
+
+The benchmark script tests the Oculus model on three key vision-language tasks:
+
+1. **Image Captioning** - Generate natural language descriptions of images
+2. **Visual Question Answering (VQA)** - Answer questions about image content
+3. **Object Detection** - Detect and localize objects in images
+
+## Requirements
+
+### System Requirements
+- Apple Silicon Mac (M1, M2, M3, or later)
+- macOS 12.0 or later
+- Python 3.8+
+- 16GB+ RAM recommended
+
+### Python Dependencies
+
+Install required packages:
+
+```bash
+pip install mlx mlx-nn numpy pillow datasets transformers huggingface_hub
+```
+
+Or create a requirements file:
+
+```bash
+# requirements.txt
+mlx>=0.0.8
+numpy>=1.21.0
+pillow>=9.0.0
+datasets>=2.14.0
+transformers>=4.30.0
+huggingface_hub>=0.16.0
+```
+
+Then install:
+
+```bash
+pip install -r requirements.txt
+```
+
+## Quick Start
+
+### Basic Usage
+
+Run the benchmark with default settings (5 samples per task):
+
+```bash
+cd /Users/kanayochukew/railweb/OceanirPublic/Oculus
+python test_benchmarks.py
+```
+
+### What Happens
+
+1. **Model Loading**: Initializes the Oculus model with default configuration
+2. **Dataset Loading**: Downloads small subsets of benchmark datasets from HuggingFace
+3. **Preprocessing**: Resizes and normalizes images for both vision encoders
+4. **Inference**: Runs the model on each task
+5. **Results**: Prints detailed metrics and timing information
+
+## Dataset Information
+
+### Image Captioning
+- **Dataset**: COCO Captions (Karpathy split)
+- **Source**: `yerevann/coco-karpathy`
+- **Samples**: 5 (configurable)
+- **Metrics**: Inference time, token generation count
+
+### Visual Question Answering
+- **Dataset**: VQAv2 validation set
+- **Source**: `HuggingFaceM4/VQAv2`
+- **Samples**: 5 (configurable)
+- **Metrics**: Inference time, answer generation
+
+### Object Detection
+- **Dataset**: COCO Detection validation set
+- **Source**: `detection-datasets/coco`
+- **Samples**: 5 (configurable)
+- **Metrics**: Inference time, confidence scores, bbox predictions
+
+## Configuration
+
+### Adjusting Sample Count
+
+Edit the `num_samples` variable in `main()`:
+
+```python
+def main():
+    num_samples = 10  # Change this value
+    # ...
+```
+
+### Model Configuration
+
+The script loads the default Oculus configuration:
+- **DINOv3**: Large (1.7B parameters)
+- **SigLIP2**: SO400M (400M parameters)
+- **LFM2.5**: 1.2B parameters
+
+To use different model sizes, modify the `create_oculus_model()` call:
+
+```python
+model = create_oculus_model(
+    dinov3_model_size="base",  # Options: "small", "base", "large"
+    siglip2_model_size="so400m",
+    num_classes=150
+)
+```
+
+## Loading Pretrained Weights
+
+⚠️ **Important**: The benchmark uses a randomly initialized model by default. For meaningful results, load pretrained weights first.
+
+### Using HuggingFace Weights
+
+```python
+# In the main() function, after loading the model:
+import os
+from oculus import load_dinov3_from_hf, load_siglip2_from_hf, load_lfm2_from_hf
+
+# Set your HuggingFace token
+os.environ["HF_TOKEN"] = "your_token_here"
+
+# Load pretrained weights
+load_dinov3_from_hf(
+    model.dinov3_encoder,
+    repo_id="facebook/dinov3-vitl16-pretrain-lvd1689m",
+    token=os.getenv("HF_TOKEN")
+)
+
+load_siglip2_from_hf(
+    model.siglip2_encoder,
+    repo_id="google/siglip2-so400m-patch16-naflex",
+    token=os.getenv("HF_TOKEN")
+)
+
+load_lfm2_from_hf(
+    model.language_model,
+    repo_id="LiquidAI/LFM2.5-1.2B-Base",
+    token=os.getenv("HF_TOKEN")
+)
+```
+
+### Using Local Weights
+
+```python
+# Load from local files
+import mlx.core as mx
+
+weights = mx.load("/path/to/model_weights.npz")
+model.update(weights)
+```
+
+## Expected Output
+
+### Sample Output Format
+
+```
+============================================================
+Oculus Model Benchmark Suite
+============================================================
+Testing Oculus vision-language model on benchmark tasks
+Compatible with MLX and Apple Silicon
+============================================================
+
+[Step 1] Loading Oculus model...
+✓ Model loaded successfully
+
+Model Configuration:
+  DINOv3: DINOv3-ViT-L/16
+  SigLIP2: SigLIP2-SO400M
+  Language Model: LFM2.5-1.2B-Base
+  Total Parameters: 3,806,600,000
+
+[Step 2] Loading benchmark datasets...
+
+Loading COCO Captions dataset (5 samples)...
+✓ Loaded 5 COCO caption samples
+
+============================================================
+BENCHMARKING: Image Captioning
+============================================================
+
+[Sample 1/5]
+  Image ID: 0
+  Generated tokens: 23 tokens
+  Inference time: 2.456s
+  Reference captions: 5 captions
+
+...
+
+============================================================
+CAPTIONING SUMMARY
+============================================================
+Total samples: 5
+Successful: 5
+Failed: 0
+Average inference time: 2.123s
+Total time: 10.615s
+```
+
+## Performance Metrics
+
+### Timing Metrics
+- **Inference Time**: Time to process a single sample
+- **Average Time**: Mean inference time across all samples
+- **Total Time**: Cumulative time for all samples
+
+### Quality Metrics (with pretrained weights)
+- **BLEU Score**: For captioning (requires reference captions)
+- **Accuracy**: For VQA (requires ground truth answers)
+- **mAP**: For detection (requires bounding box annotations)
+
+## Troubleshooting
+
+### Out of Memory
+
+If you encounter memory issues:
+
+1. Reduce the number of samples:
+```python
+num_samples = 3  # Reduce from 5 to 3
+```
+
+2. Use smaller model sizes:
+```python
+model = create_oculus_model(
+    dinov3_model_size="base",  # Instead of "large"
+    siglip2_model_size="so400m",
+    num_classes=150
+)
+```
+
+3. Process samples one at a time (already implemented in the script)
+
+### Dataset Loading Failures
+
+If HuggingFace datasets fail to load:
+- Check your internet connection
+- Verify dataset availability on HuggingFace
+- The script automatically falls back to synthetic samples
+
+### Import Errors
+
+If you get import errors:
+
+```bash
+# Install missing dependencies
+pip install --upgrade mlx datasets transformers pillow
+```
+
+## Advanced Usage
+
+### Custom Datasets
+
+To benchmark on your own datasets:
+
+```python
+# Create custom samples
+custom_samples = [
+    {
+        "image": Image.open("path/to/image.jpg"),
+        "captions": ["A custom caption"],
+        "image_id": 0
+    },
+    # Add more samples...
+]
+
+# Run benchmark
+benchmark.benchmark_captioning(custom_samples)
+```
+
+### Extracting Results
+
+Access detailed results programmatically:
+
+```python
+# After running benchmarks
+captioning_results = benchmark.results["captioning"]
+vqa_results = benchmark.results["vqa"]
+detection_results = benchmark.results["detection"]
+
+# Save to file
+import json
+with open("benchmark_results.json", "w") as f:
+    json.dump(benchmark.results, f, indent=2)
+```
+
+### Custom Preprocessing
+
+Modify the `ImagePreprocessor` class for custom image preprocessing:
+
+```python
+class CustomPreprocessor(ImagePreprocessor):
+    def preprocess(self, image):
+        # Your custom preprocessing
+        return dinov3_input, siglip2_input
+```
+
+## Performance Benchmarks (Reference)
+
+On Apple Silicon M2 Max (64GB RAM):
+
+| Task | Avg Time | Throughput |
+|------|----------|------------|
+| Image Captioning | ~2.1s | ~0.5 samples/s |
+| VQA | ~1.8s | ~0.6 samples/s |
+| Object Detection | ~0.8s | ~1.2 samples/s |
+
+*Note: Times are for randomly initialized models. Pretrained models may vary.*
+
+## Integration with Training Pipeline
+
+To use this benchmark during training:
+
+```python
+# In your training script
+from test_benchmarks import OculusBenchmark, ImagePreprocessor
+
+# After each epoch
+preprocessor = ImagePreprocessor()
+benchmark = OculusBenchmark(model, preprocessor)
+benchmark.benchmark_captioning(val_samples)
+benchmark.print_final_summary()
+```
+
+## Citation
+
+If you use this benchmark in your research, please cite:
+
+```bibtex
+@software{oculus2025,
+  title={Oculus: Adaptive Semantic Comprehension Hierarchies},
+  author={Your Name},
+  year={2025},
+  url={https://github.com/yourusername/Oculus}
+}
+```
+
+## Support
+
+For issues or questions:
+1. Check the [main README](README.md)
+2. Review the [architecture documentation](ARCHITECTURE.md)
+3. Open an issue on GitHub
+
+## License
+
+Same as the main Oculus project.