update README

README.md

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 ---
 license: mit
+language:
+- en
+- pt
+metrics:
+- accuracy
+- bertscore
+pipeline_tag: image-classification
+tags:
+- code
 ---
+
+# Galaxy10 SDSS Dataset CNN TF Model
+
+This model contains a Convolutional Neural Network (CNN) ensemble model trained on the Galaxy10 SDSS Dataset available in the astroNN library.
+
+## Dataset
+The Galaxy10 SDSS Dataset is a collection of astronomical images categorized into 10 classes of galaxies. 
+
+To access the dataset:
+- Visit the [astroNN Galaxy10 SDSS Documentation](https://astronn.readthedocs.io/en/stable/galaxy10sdss.html)
+
+## Models Used
+The ensemble model comprises the following pre-trained CNN architectures:
+- AlexNet
+- DenseNet121
+- ResNet50
+- EfficientNet-V2-M
+
+## Ensemble Learning Technique
+The ensemble model employs Mean Voting to aggregate predictions from individual models. This technique combines the output probabilities or predictions from each model and calculates the mean for final classification.
+
+## Results
+After training and evaluation on the Galaxy10 SDSS Dataset, using the test dataset, the ensemble model achieved the following metrics:
+- Accuracy: 0.8653
+- Precision: 0.8597
+- Recall: 0.8602
+- F1-score: 0.8653
+- Loss: 0.8292
+
+
+## Training
+The models were trained using PyTorch, leveraging transfer learning for some architectures and training from scratch for others. The ensemble learning technique was applied post-training using the mean voting strategy.
+
+## Requirements
+- PyTorch v2.1.0
+- CUDA v11.8 (cu118)
+
+## Acknowledgments
+- Credits to the astroNN team for providing the Galaxy10 SDSS Dataset and related documentation.
+- The pre-trained model architectures used are courtesy of Pytorch.
+
+Feel free to reach out for any queries, issues, or improvements related to this model repository.