Human-vs-NonHuman-Detection
Human-vs-NonHuman-Detection is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to classify images as either human or non-human using the SiglipForImageClassification architecture.
Classification Report:
precision recall f1-score support
Human 𖨆 0.9939 0.9735 0.9836 6646
Non Human メ 0.9807 0.9956 0.9881 8989
accuracy 0.9862 15635
macro avg 0.9873 0.9845 0.9858 15635
weighted avg 0.9863 0.9862 0.9862 15635
The model categorizes images into two classes:
- Class 0: "Human 𖨆"
- Class 1: "Non Human メ"
Run with Transformers🤗
!pip install -q transformers torch pillow gradio
import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/Human-vs-NonHuman-Detection"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
def human_detection(image):
"""Predicts whether the image contains a human or non-human entity."""
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
labels = {
"0": "Human 𖨆",
"1": "Non Human メ"
}
predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Create Gradio interface
iface = gr.Interface(
fn=human_detection,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Prediction Scores"),
title="Human vs Non-Human Detection",
description="Upload an image to classify whether it contains a human or non-human entity."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
Intended Use:
The Human-vs-NonHuman-Detection model is designed to distinguish between human and non-human entities. Potential use cases include:
- Surveillance & Security: Enhancing monitoring systems to detect human presence.
- Autonomous Systems: Helping robots and AI systems identify humans.
- Image Filtering: Automatically categorizing human vs. non-human images.
- Smart Access Control: Identifying human presence for secure authentication.