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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: object-detection |
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tags: |
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- code |
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--- |
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# David YOLOS Model |
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This repository contains a custom YOLOS model fine-tuned on the [Balloon Dataset](https://github.com/matterport/Mask_RCNN/tree/master/samples/balloon) for object detection tasks. The model was trained using the PyTorch Lightning framework and is available for inference and further fine-tuning. |
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## Model Details |
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- **Model Architecture**: YOLOS (You Only Look One-level Object Structure) |
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- **Base Model**: `hustvl/yolos-small` |
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- **Training Framework**: PyTorch Lightning |
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- **Dataset**: Balloon Dataset |
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- **Number of Classes**: 1 (Balloon) |
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## Installation and Usage |
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### Installation |
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You can install the necessary libraries using: |
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```bash |
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pip install transformers torch torchvision |
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``` |
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# Usage |
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You can load and use the model with the following code: |
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```python |
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from transformers import AutoModelForObjectDetection, AutoFeatureExtractor |
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from PIL import Image |
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import torch |
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# Load model and feature extractor |
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model_name = "your-username/my-custom-yolos-model" |
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model = AutoModelForObjectDetection.from_pretrained(model_name) |
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feature_extractor = AutoFeatureExtractor.from_pretrained(model_name) |
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# Load an image |
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image = Image.open("path/to/your/image.jpg") |
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# Preprocess the image |
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inputs = feature_extractor(images=image, return_tensors="pt") |
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pixel_values = inputs['pixel_values'] |
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# Perform inference |
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model.eval() |
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with torch.no_grad(): |
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outputs = model(pixel_values=pixel_values) |
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# Visualize the results |
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# (Insert visualization code here) |
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``` |
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# Model Performance |
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- Training Loss: 0.0614 |
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- Validation Loss: 0.1784 |
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- Training Dataset: Balloon Dataset (XXX images) |
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- Validation Dataset: Balloon Dataset (XXX images) |
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- Number of Epochs: 75 |
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# Citation |
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If you use this model in your research, please cite: |
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```bibtex |
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Copy code |
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@misc{my-custom-yolos-model, |
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author = {Your Name}, |
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title = {YOLOS Fine-tuned on Balloon Dataset}, |
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year = {2024}, |
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publisher = {Hugging Face}, |
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howpublished = {\url{https://huggingface.co./your-username/my-custom-yolos-model}}, |
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} |
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``` |
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# License |
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This model is licensed under the MIT License. Feel free to use, modify, and distribute it as you see fit. |
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# Copy code |
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You can copy and paste this Markdown into your README file on Hugging Face. |
