Update README.md

README.md

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 tags:
-- model_hub_mixin
-- pytorch_model_hub_mixin
+- pytorch
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-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+# CNN Leukemia Classifier
+
+## Model Description
+
+This Convolutional Neural Network (CNN) model is designed for the classification of leukemia images into one of four classes. It was developed for the Quito AI Day event by [Sebastian Sarasti](https://www.linkedin.com/in/sebastiansarasti/). The model leverages a series of convolutional layers followed by fully connected layers to process and classify images effectively.
+
+## Model Architecture
+
+The model consists of the following layers:
+
+- Convolutional Layer: 3 input channels, 128 output channels, 3x3 kernel size, stride 1, padding 1
+- ReLU Activation
+- Max Pooling Layer: 2x2 kernel size
+- Dropout Layer: 0.3 dropout rate
+
+- Convolutional Layer: 128 input channels, 64 output channels, 3x3 kernel size, stride 1, padding 1
+- ReLU Activation
+- Max Pooling Layer: 2x2 kernel size
+- Dropout Layer: 0.3 dropout rate
+
+- Convolutional Layer: 64 input channels, 32 output channels, 3x3 kernel size, stride 1, padding 1
+- ReLU Activation
+- Max Pooling Layer: 2x2 kernel size
+- Dropout Layer: 0.3 dropout rate
+
+- Convolutional Layer: 32 input channels, 8 output channels, 3x3 kernel size, stride 1, padding 1
+- ReLU Activation
+- Max Pooling Layer: 2x2 kernel size
+- Dropout Layer: 0.3 dropout rate
+
+- Flatten Layer
+
+- Fully Connected Layer: 1568 input features, 512 output features
+- ReLU Activation
+- Dropout Layer: 0.5 dropout rate
+
+- Fully Connected Layer: 512 input features, 4 output features
+
+## Dataset
+
+The model was trained on the [Leukemia dataset from Kaggle](https://www.kaggle.com/datasets/mehradaria/leukemia), which consists of images labeled into different leukemia types.
+
+## Usage
+
+To use this model, you can load it from the Hugging Face Hub as follows:
+
+```python
+from transformers import AutoModel
+
+model = AutoModel.from_pretrained("path/to/your/model")