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--- |
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language: en |
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datasets: |
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- abdulmananraja/real-life-violence-situations |
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tags: |
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- image-classification |
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- vision |
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- harassment-detection |
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license: apache-2.0 |
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--- |
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# RKSHT Harassment Detection Model |
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## Model Description |
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This is a custom Vision Transformer (ViT) model fine-tuned for detecting instances of harassment in public and workplace environments. The model is built on [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) and trained on a dataset tailored for harassment detection, classifying images into 'harassment' or 'non-harassment' categories. |
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## Intended Use |
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This model is designed for use in applications requiring harassment detection through visual data, including: |
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- Workplace and public safety monitoring |
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- Real-time CCTV surveillance |
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- Automated alert systems |
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## Model accuracy |
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The RKSHT model has been fine-tuned with high accuracy for distinguishing harassment behavior. |
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## How to Use |
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Here’s an example of how to use the RKSHT Harassment Detection model for image classification: |
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```python |
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import torch |
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from transformers import ViTForImageClassification, ViTFeatureExtractor |
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from PIL import Image |
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# Load the model and feature extractor |
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model = ViTForImageClassification.from_pretrained('Binarybardakshat/RKSHT') |
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feature_extractor = ViTFeatureExtractor.from_pretrained('Binarybardakshat/RKSHT') |
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# Load an image |
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image = Image.open('image.jpg') |
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# Preprocess the image |
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inputs = feature_extractor(images=image, return_tensors="pt") |
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# Perform inference |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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predicted_class_idx = logits.argmax(-1).item() |
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# Print the predicted class |
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print("Predicted class:", model.config.id2label[predicted_class_idx]) |
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