Asadrizvi64/Axel_Watch

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: [email protected]