Aevanis/Aevanis-Vision-Core

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.

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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.

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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

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.