Update README.md

README.md

@@ -1,3 +1,56 @@
----
-license: mit
----
+---
+language: en
+datasets:
+- abdulmananraja/real-life-violence-situations
+tags:
+- image-classification
+- vision
+- harassment-detection
+license: apache-2.0
+---
+
+# RKSHT Harassment Detection Model
+
+## Model Description
+
+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.
+
+## Intended Use
+
+This model is designed for use in applications requiring harassment detection through visual data, including:
+
+- Workplace and public safety monitoring
+- Real-time CCTV surveillance
+- Automated alert systems
+
+## Model accuracy
+
+The RKSHT model has been fine-tuned with high accuracy for distinguishing harassment behavior.
+
+## How to Use
+
+Here’s an example of how to use the RKSHT Harassment Detection model for image classification:
+
+```python
+import torch
+from transformers import ViTForImageClassification, ViTFeatureExtractor
+from PIL import Image
+
+# Load the model and feature extractor
+model = ViTForImageClassification.from_pretrained('Binarybardakshat/RKSHT')
+feature_extractor = ViTFeatureExtractor.from_pretrained('Binarybardakshat/RKSHT')
+
+# Load an image
+image = Image.open('image.jpg')
+
+# Preprocess the image
+inputs = feature_extractor(images=image, return_tensors="pt")
+
+# Perform inference
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class_idx = logits.argmax(-1).item()
+
+# Print the predicted class
+print("Predicted class:", model.config.id2label[predicted_class_idx])