|
|
--- |
|
|
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: **[email protected]** |
|
|
|
