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Human-Action-Recognition

Human-Action-Recognition is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for multi-class human action recognition. It uses the SiglipForImageClassification architecture to predict human activities from still images.

Classification Report:
 precision recall f1-score support

 calling 0.8525 0.7571 0.8020 840
 clapping 0.8679 0.7119 0.7822 840
 cycling 0.9662 0.9857 0.9758 840
 dancing 0.8302 0.8381 0.8341 840
 drinking 0.9093 0.8714 0.8900 840
 eating 0.9377 0.9131 0.9252 840
 fighting 0.9034 0.7905 0.8432 840
 hugging 0.9065 0.9000 0.9032 840
 laughing 0.7854 0.8583 0.8203 840
listening_to_music 0.8494 0.7988 0.8233 840
 running 0.8888 0.9321 0.9099 840
 sitting 0.5945 0.7226 0.6523 840
 sleeping 0.8593 0.8214 0.8399 840
 texting 0.8195 0.6702 0.7374 840
 using_laptop 0.6610 0.9190 0.7689 840

 accuracy 0.8327 12600
 macro avg 0.8421 0.8327 0.8339 12600
 weighted avg 0.8421 0.8327 0.8339 12600

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The model categorizes images into 15 action classes:

• 0: calling
• 1: clapping
• 2: cycling
• 3: dancing
• 4: drinking
• 5: eating
• 6: fighting
• 7: hugging
• 8: laughing
• 9: listening_to_music
• 10: running
• 11: sitting
• 12: sleeping
• 13: texting
• 14: using_laptop

---

Run with Transformers 🤗

!pip install -q transformers torch pillow gradio

import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/Human-Action-Recognition" # Change to your updated model path
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# ID to Label mapping
id2label = {
 0: "calling",
 1: "clapping",
 2: "cycling",
 3: "dancing",
 4: "drinking",
 5: "eating",
 6: "fighting",
 7: "hugging",
 8: "laughing",
 9: "listening_to_music",
 10: "running",
 11: "sitting",
 12: "sleeping",
 13: "texting",
 14: "using_laptop"
}

def classify_action(image):
 """Predicts the human action in the image."""
 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()

 predictions = {id2label[i]: round(probs[i], 3) for i in range(len(probs))}
 return predictions

# Gradio interface
iface = gr.Interface(
 fn=classify_action,
 inputs=gr.Image(type="numpy"),
 outputs=gr.Label(label="Action Prediction Scores"),
 title="Human Action Recognition",
 description="Upload an image to recognize the human action (e.g., dancing, calling, sitting, etc.)."
)

# Launch the app
if __name__ == "__main__":
 iface.launch()

---

Intended Use

The Human-Action-Recognition model is designed to detect and classify human actions from images. Example applications:

• Surveillance & Monitoring: Recognizing suspicious or specific activities in public spaces.
• Sports Analytics: Identifying player activities or movements.
• Social Media Insights: Understanding trends in user-posted visuals.
• Healthcare: Monitoring elderly or patients for activity patterns.
• Robotics & Automation: Enabling context-aware AI systems with visual understanding.

Base model

google/siglip2-base-patch16-224

(119)

Moderation, Balance, Classifiers • 12 items • Updated 1 day ago • 2