| | --- |
| | 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: |
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|
| | ## 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** |
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