Ais Anomaly Vit v2

AIS Anomaly ViT v2

ais_anomaly

Model Overview

AIS Anomaly ViT v2 is a Vision Transformer (ViT) model fine-tuned for maritime anomaly detection using visual representations derived from Automatic Identification System (AIS) vessel tracks.

The model classifies vessel trajectory images into:

• Normal Maritime Behavior
• Anomalous Maritime Behavior

The objective is to support maritime domain awareness, vessel monitoring, maritime security operations, and geospatial intelligence workflows by automatically identifying suspicious vessel movement patterns from AIS-derived visualizations.

The model was trained using the AIS Anomaly v1-30 dataset.

Model Details

Property	Value
Architecture	Vision Transformer (ViT)
Base Model	google/vit-base-patch16-224
Task	Image Classification
Domain	Maritime Analytics
Input	AIS Track Images
Output	Binary Classification
Classes	Normal, Anomaly
Framework	Transformers
Training Dataset	Neo111x/ais-anomaly-v1-30

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Intended Use

Primary Use Cases

• Maritime anomaly detection
• Vessel behavior monitoring
• Maritime traffic surveillance
• Illegal fishing detection support
• Dark vessel activity analysis
• Maritime OSINT and GEOINT workflows
• Maritime security research
• AIS trajectory intelligence

Example Applications

• Coast guard monitoring systems
• Maritime traffic management
• Port security operations
• Maritime threat intelligence platforms
• Geospatial AI research
• AIS analytics dashboards

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Dataset

AIS Anomaly v1-30

The model was trained on:

Dataset: Neo111x/ais-anomaly-v1-30

The dataset contains AIS trajectory visualizations generated from vessel movement data.

Each image represents a vessel track encoded as a visual pattern suitable for computer vision models.

Classes include:

• Normal vessel movement
• Anomalous vessel movement

Potential anomaly categories represented in the data may include:

• Unusual route deviations
• Suspicious loitering
• AIS gaps or interruptions
• Unexpected course changes
• Irregular movement behavior

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Training Procedure

Preprocessing

AIS trajectories are transformed into image representations before training.

Typical preprocessing steps include:

1. AIS trajectory extraction
2. Geographic normalization
3. Route visualization generation
4. Image resizing to ViT input dimensions
5. Dataset balancing and augmentation

Training Configuration

Parameter	Value
Model Type	Vision Transformer
Image Size	224 × 224
Classification Type	Binary
Optimizer	AdamW
Framework	PyTorch + Transformers

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Evaluation

The model was evaluated on a held-out validation split derived from the AIS Anomaly v1-30 dataset.

Evaluation metrics may include:

• Accuracy
• Precision
• Recall
• F1 Score
• Confusion Matrix Analysis

Users are encouraged to perform independent evaluation on operational maritime datasets before deployment in production environments.

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Usage

Install Dependencies

pip install transformers torch pillow

Load the model

from transformers import AutoImageProcessor, AutoModelForImageClassification

model_name = "Neo111x/ais-anomaly-vit-v2"

processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)

Inference example

from PIL import Image
import torch
from transformers import AutoImageProcessor, AutoModelForImageClassification

model_name = "Neo111x/ais-anomaly-vit-v2"

processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)

image = Image.open("trajectory.png").convert("RGB")

inputs = processor(images=image, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

predicted_class = outputs.logits.argmax(-1).item()

print("Predicted class:", predicted_class)

Citation

@misc{cherif2026aisanomalyvitv2,
  title={AIS Anomaly ViT v2},
  author={Bilel Cherif},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/Neo111x/ais-anomaly-vit-v2}
}