Update README.md

README.md

@@ -1,3 +1,89 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: object-detection
+tags:
+- code
+---
+# David YOLOS Model
+
+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.
+
+## Model Details
+
+- **Model Architecture**: YOLOS (You Only Look One-level Object Structure)
+- **Base Model**: `hustvl/yolos-small`
+- **Training Framework**: PyTorch Lightning
+- **Dataset**: Balloon Dataset
+- **Number of Classes**: 1 (Balloon)
+
+## Installation and Usage
+
+### Installation
+
+You can install the necessary libraries using:
+
+```bash
+pip install transformers torch torchvision
+```
+
+# Usage
+You can load and use the model with the following code:
+
+```python
+from transformers import AutoModelForObjectDetection, AutoFeatureExtractor
+from PIL import Image
+import torch
+
+# Load model and feature extractor
+model_name = "your-username/my-custom-yolos-model"
+model = AutoModelForObjectDetection.from_pretrained(model_name)
+feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
+
+# Load an image
+image = Image.open("path/to/your/image.jpg")
+
+# Preprocess the image
+inputs = feature_extractor(images=image, return_tensors="pt")
+pixel_values = inputs['pixel_values']
+
+# Perform inference
+model.eval()
+with torch.no_grad():
+    outputs = model(pixel_values=pixel_values)
+
+# Visualize the results
+# (Insert visualization code here)
+```
+
+# Model Performance
+- Training Loss: 0.0614
+- Validation Loss: 0.1784
+- Training Dataset: Balloon Dataset (XXX images)
+- Validation Dataset: Balloon Dataset (XXX images)
+- Number of Epochs: 75
+
+
+# Citation
+If you use this model in your research, please cite:
+
+```bibtex
+Copy code
+@misc{my-custom-yolos-model,
+  author = {Your Name},
+  title = {YOLOS Fine-tuned on Balloon Dataset},
+  year = {2024},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/your-username/my-custom-yolos-model}},
+}
+```
+
+# License
+
+This model is licensed under the MIT License. Feel free to use, modify, and distribute it as you see fit.
+
+
+# Copy code
+
+You can copy and paste this Markdown into your README file on Hugging Face.