---
license: apache-2.0
tags:
- vision
- edge-ai
- robotics
- foundation-model
- computer-vision
- realtime
pipeline_tag: image-feature-extraction
library_name: pytorch
---

# Aevanis Vision Core (Universal Visual Eye - Pro Scale)
High-Capacity 24.35M Parameter Foundation Visual Backbone for Rich Scene Understanding and Edge Robotics.

Developed by **Aevanis AI Lab**, Av-Vision-Core scales the hybrid ConvNeXt-Transformer architecture to 12 layers and 384 embedding dimensions, delivering substantially higher representational capacity while retaining blistering 400+ FPS server throughput and 100+ FPS edge execution.

---

## Architectural Highlights
* **24.35M Parameters** (12 Transformer Blocks, 384 Embed Dim, 6 Attention Heads).
* **420 to 550 FPS** on Server GPUs (Tesla T4 / RTX 4090).
* **75 to 110 FPS** on NVIDIA Jetson Orin Nano.
* **Deep Non-Linear Spatial Manifold**: Zero-shot semantic grouping, dense depth recovery, and tactile/kinematic grounding.

---

## Hardware Latency & Throughput Benchmark

| Hardware Target | Batch Size | Latency (ms) | Inference Rate (FPS / Hz) |
| :--- | :---: | :---: | :---: |
| **NVIDIA RTX 4090** | 1 | **1.8 ms** | **550 FPS** |
| **Tesla T4 (fp16)** | 1 | **2.4 ms** | **420 FPS** |
| **Jetson Orin Nano** | 1 | **9.5 ms** | **105 FPS** |
| **Desktop CPU (i7 Native FP32)** | 1 | **58.0 ms** | **17 FPS** |
| **Desktop CPU (INT8 Quantized)** | 1 | **28.0 ms** | **35 FPS** |
| **Raspberry Pi 5** | 1 | **125.0 ms** | **8 FPS** |

*Note: For low-power microcomputers like Raspberry Pi 5, Av-Vision-Nano is the recommended deployment backbone.*

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## Pre-Training Dataset
Trained for 20 deep epochs on 166,440 continuous frames across 67 dynamic video sequences (48.2 GB temporal footage). Pre-trained with 75% patch masking and Normalized Sobel Edge Gradient Loss.

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## Quickstart

```python
import torch
from model import AevanisVisionBackbone

model = AevanisVisionBackbone.from_pretrained("pytorch_model.bin", model_scale="core")
img = torch.randn(1, 3, 224, 224)

with torch.no_grad():
    features = model(img)

# Shape: [1, 196, 384]
spatial_tokens = features["spatial_tokens"]
```

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## License
Apache-2.0.