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README.md

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 license: apache-2.0
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+# Model Card: Fine-Tuned Vision Transformer (ViT) for NSFW Image Classification
+
+## Model Description
+
+The **Fine-Tuned Vision Transformer (ViT)** is a variant of the transformer encoder architecture, similar to BERT, that has been adapted for image classification tasks. This specific model, named "google/vit-base-patch16-224-in21k," is pre-trained on a substantial collection of images in a supervised manner, leveraging the ImageNet-21k dataset. The images in the pre-training dataset are resized to a resolution of 224x224 pixels, making it suitable for a wide range of image recognition tasks.
+
+During the pre-training phase, the model underwent training for fewer than 20 epochs with a batch size of 16. This training process involved learning valuable visual features from the ImageNet-21k dataset to create a robust foundation for subsequent fine-tuning on specific tasks.
+
+## Intended Uses & Limitations
+
+### Intended Uses
+- **NSFW Image Classification**: The primary intended use of this model is for the classification of NSFW (Not Safe for Work) images. It has been fine-tuned for this purpose, making it suitable for filtering explicit or inappropriate content in various applications.
+
+### How to use
+Here is how to use this model to classifiy an image based on 1 of 2 classes (normal,nsfw):
+
+```markdown
+
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+classifier = pipeline("image-classification", model="RealFalconsAI/nsfw_image_detection")
+classifier(image)
+
+```
+
+<hr>
+
+``` markdown
+
+# Load model directly
+from transformers import AutoModelForImageClassification, ViTImageProcessor
+
+model = AutoModelForImageClassification.from_pretrained("RealFalconsAI/nsfw_image_detection")
+processor = ViTImageProcessor.from_pretrained('RealFalconsAI/nsfw_image_detection')
+with torch.no_grad():
+    inputs = processor(images=<image>, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits
+
+predicted_label = logits.argmax(-1).item()
+model.config.id2label[predicted_label]
+
+```
+
+<hr>
+
+### Limitations
+- **Specialized Task Fine-Tuning**: While the model is adept at NSFW image classification, its performance may vary when applied to other tasks. Users interested in employing this model for different tasks should explore fine-tuned versions available in the model hub for optimal results.
+
+## Training Data
+
+The model's training data includes a proprietary dataset comprising approximately 80,000 images. This dataset encompasses a significant amount of variability and consists of two distinct classes: "normal" and "nsfw." The training process on this data aimed to equip the model with the ability to distinguish between safe and explicit content effectively.
+
+**Note:** It's essential to use this model responsibly and ethically, adhering to content guidelines and applicable regulations when implementing it in real-world applications, particularly those involving potentially sensitive content.
+
+For more details on model fine-tuning and usage, please refer to the model's documentation and the model hub.
+
+## References
+
+- [Hugging Face Model Hub](https://huggingface.co/models)
+- [Vision Transformer (ViT) Paper](https://arxiv.org/abs/2010.11929)
+- [ImageNet-21k Dataset](http://www.image-net.org/)
+
+**Disclaimer:** The model's performance may be influenced by the quality and representativeness of the data it was fine-tuned on. Users are encouraged to assess the model's suitability for their specific applications and datasets.