---
license: apache-2.0
base_model:
  - stabilityai/sdxl-turbo
  - stable-diffusion-v1-5/stable-diffusion-v1-5
  - stabilityai/stable-diffusion-xl-base-1.0
pipeline_tag: image-classification
library_name: diffusers
---

# Model Card for model_jan.safetensor

## Model Overview
`model_jan.safetensor` is an image classification model based on the **SDXL architecture (Base 1.0)**, specifically designed for detecting and classifying individuals wearing wristwatches. The model is optimized to recognize the presence of watches in images and can be used for a variety of applications, including image search, security surveillance, and retail product recognition.

## Model Details
- **Model Type**: SDXL Base 1.0
- **Model File**: `model_jan.safetensor`
- **Class Prompt**: Watch
- **Instance Prompt**: SGDW
- **Training Configuration**:
  - **Regularization Factor**: 2 × 6 = 12
  - **Training Repeat**: 4 × 3 = 12
  - **Epochs**: [Epoch count not provided]
  
## Intended Use
`model_jan.safetensor` is best used for the following tasks:
- **Image Classification**: Identifying whether individuals in an image are wearing a wristwatch.
- **Object Detection**: Detecting watches on individuals in various contexts, such as product recognition, fashion, and surveillance.

## Performance
The model has been trained on a specialized dataset containing images of people wearing wristwatches. The training process involved a combination of regularization and repeated cycles to enhance the model’s accuracy and generalization.

## How to Use
1. **Load the Model**: Load the model using frameworks that support the `safetensor` file format, such as Hugging Face Transformers, PyTorch, or TensorFlow.
2. **Input**: Provide images containing people, ideally wearing wristwatches, to obtain the classification or detection output.
3. **Output**: The model will output predictions based on whether the individuals in the image are wearing a wristwatch.

## Limitations
- The model performs best on images where people are wearing wristwatches. It may not be reliable on images that do not meet this criterion.
- Performance may vary based on the diversity of the input images.
- As a model based on SDXL, it requires considerable computational resources, so it’s advisable to run it on hardware optimized for deep learning tasks.

## Future Improvements
To increase the model’s robustness and accuracy:
- The model can be fine-tuned on more diverse datasets, including images of people wearing different types of wristwatches, to improve its generalization to a wider range of watch types and styles.
- Additional performance evaluation across various datasets could help refine its accuracy in real-world use cases.

## Citation
If you use this model in your work, please cite it as follows:


## License
This model is released under the **Apache 2.0 License**, and is free to use for both research and commercial purposes. Please refer to the specific license included with the model for further details.

## Contact
For any inquiries or issues with the model, feel free to contact the maintainer at: **asad.haider.rizvi64@gmail.com**