File size: 3,278 Bytes
33f1b9c a4d4fe3 57bb9bf a4d4fe3 2ca4a1d 57bb9bf d180a82 57bb9bf 2ca4a1d a4d4fe3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
---
license: apache-2.0
datasets:
- prithivMLmods/Human-vs-NonHuman
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- Human
- Non-Human
- Detection
- SigLIP2
- Vision-Encoder
---
# **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.
```py
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🤗**
```python
!pip install -q transformers torch pillow gradio
```
```python
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. |