---
license: apache-2.0
pipeline_tag: image-classification
---
# 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
import torch
from PIL import Image
from transformers import AutoModelForImageClassification, ViTImageProcessor

img = Image.open("<path_to_image_file>")
model = AutoModelForImageClassification.from_pretrained("RealFalconsAI/nsfw_image_detection")
processor = ViTImageProcessor.from_pretrained('RealFalconsAI/nsfw_image_detection')
with torch.no_grad():
    inputs = processor(images=img, 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.