Add dinov3_vits16 CoreML FP32 model (448×448)

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+demo_image.png filter=lfs diff=lfs merge=lfs -text
+dinov3_feature_comparison.png filter=lfs diff=lfs merge=lfs -text

LICENSE.md

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+# DINOv3 License
+
+*Last Updated: August 19, 2025*
+
+**“Agreement”** means the terms and conditions for use, reproduction, distribution and modification of the DINO Materials set forth herein.
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README.md

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+---
+library_name: coreml
+tags:
+  - vision
+  - feature-extraction
+  - dinov3
+  - coreml
+  - apple-silicon
+  - fp32
+pipeline_tag: feature-extraction
+---
+
+# DINOv3 VITS16 CoreML FP32
+
+CoreML conversion of [facebook/dinov3-vits16-pretrain-lvd1689m](https://huggingface.co/facebook/dinov3-vits16-pretrain-lvd1689m) optimized for Apple Silicon.
+
+## Model Details
+
+- **Base Model**: facebook/dinov3-vits16-pretrain-lvd1689m
+- **Framework**: CoreML
+- **Precision**: FP32
+- **Input Size**: 448×448
+- **Model Size**: 82.9 MB
+
+## Usage
+
+### Python (CoreML)
+
+```python
+import coremltools as ct
+import numpy as np
+from PIL import Image
+
+# Load model
+model = ct.models.MLModel("dinov3_vits16_448x448_fp32.mlpackage")
+
+# Prepare image
+image = Image.open("image.jpg").resize((448, 448))
+
+# Extract features
+output = model.predict({"image": image})
+features = output["features"]  # Shape: [1, embed_dim, grid_size, grid_size]
+```
+
+### Swift/iOS
+
+```swift
+import CoreML
+
+// Load model
+guard let model = try? MLModel(contentsOf: modelURL) else {
+    fatalError("Failed to load model")
+}
+
+// Prepare image
+guard let image = UIImage(named: "image.jpg") else {
+    fatalError("Failed to load image")
+}
+
+// Extract features
+let input = try MLFeatureValue(image: image.cgImage!)
+let output = try model.prediction(from: [input])
+let features = output.featureValue(for: "features")?.multiArrayValue
+```
+
+## Performance
+
+Performance metrics on Apple Silicon:
+
+### CoreML Performance
+
+- **Throughput**: 32.15 FPS
+- **Latency**: 31.10 ± 0.53 ms
+- **Min Latency**: 30.57 ms
+- **Max Latency**: 32.79 ms
+
+### Speedup vs PyTorch
+
+- **PyTorch**: 19.33 FPS
+- **CoreML**: 32.15 FPS
+- **Speedup**: 1.66x faster ⚡
+
+### Feature Accuracy
+
+- **Cosine Similarity**: 0.9847 (vs PyTorch)
+- **Correlation**: 0.9847
+- **Quality**: ⭐⭐⭐ Very Good - Excellent similarity
+
+### Model Specifications
+
+- **Precision**: FP32
+- **Input Size**: 448×448
+- **Model Size**: 82.9 MB
+
+
+## License
+
+This model is released under the DINOv3 License. See [LICENSE.md](LICENSE.md) for details.
+
+## Citation
+
+```bibtex
+@article{dinov3,
+  title={DINOv3: A Versatile Vision Foundation Model},
+  author={Meta AI Research},
+  journal={arXiv preprint arXiv:2508.10104},
+  year={2025}
+}
+```
+
+**Reference**: [DINOv3 Paper](https://arxiv.org/pdf/2508.10104)
+
+Key contributions:
+- **Gram anchoring** strategy for high-quality dense feature maps
+- Self-supervised learning on 1.689B images
+- Superior performance on dense vision tasks
+- Versatile across tasks and domains without fine-tuning
+
+## Demo Images
+
+### Input Image
+<div align="center">
+  <img src="demo_image.png" alt="Demo Input Image" width="500"/>
+</div>
+*Sample input image for feature extraction demonstration*
+
+### Feature Visualization
+<div align="center">
+  <img src="dinov3_feature_comparison.png" alt="Feature Comparison Visualization" width="800"/>
+</div>
+
+The visualization shows:
+- **PCA projection** of high-dimensional features (RGB visualization)
+- **Feature channel activations** showing spatial patterns
+- **Gram matrix analysis** for object similarity detection
+- **Side-by-side comparison** with PyTorch reference implementation
+
+This comprehensive visualization demonstrates that CoreML conversion preserves the semantic structure and feature quality of the original DINOv3 model.
+
+## Powered By DINOv3
+
+🌟 **This model is powered by DINOv3** 🌟
+
+Converted by [Aegis AI](https://github.com/Aegis-AI/Aegis-AI) for optimized Apple Silicon deployment.
+
+## Related Models
+
+- Original PyTorch Model: [facebook/dinov3-vits16-pretrain-lvd1689m](https://huggingface.co/facebook/dinov3-vits16-pretrain-lvd1689m)
+- DINOv3 License: https://ai.meta.com/resources/models-and-libraries/dinov3-license/
+
+---
+*Last updated: 2025-10-31*

benchmark_results.json

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+    "min_time": 0.05101224998361431,
+    "max_time": 0.053992042026948184
+  },
+  "coreml": {
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+    "std_time": 0.0005346886997093497,
+    "min_time": 0.030570499977329746,
+    "max_time": 0.032787583011668175
+  },
+  "coreml_compute_units": {
+    "all": {
+      "fps": 32.15425560143712,
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+      "std_time": 0.0005346886997093497,
+      "min_time": 0.030570499977329746,
+      "max_time": 0.032787583011668175
+    },
+    "cpu_and_gpu": {
+      "fps": 32.20509249743861,
+      "avg_time": 0.031050989842042326,
+      "std_time": 0.0004550632317517775,
+      "min_time": 0.030179416004102677,
+      "max_time": 0.03201974998228252
+    },
+    "cpu_and_ne": {
+      "fps": 19.06985385844124,
+      "avg_time": 0.052438786758575585,
+      "std_time": 0.000377638568636697,
+      "min_time": 0.05188629199983552,
+      "max_time": 0.05374554201262072
+    },
+    "cpu_only": {
+      "fps": 19.221620435493886,
+      "avg_time": 0.052024750116979705,
+      "std_time": 0.0004059331112169348,
+      "min_time": 0.051470792008331046,
+      "max_time": 0.05362724998849444
+    }
+  },
+  "comparison": {
+    "cosine_similarity": 0.9847110509872437,
+    "l2_distance": 0.1632278561592102,
+    "correlation": 0.984681706507534
+  }
+}

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dinov3_vits16_448x448_fp32.mlpackage/Manifest.json

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