Create README.md

README.md

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+---
+license: mit
+pipeline_tag: image-classification
+---
+MNIST Classification Comparison Project
+This repository hosts two classification models trained on the MNIST dataset, representing handwritten digits. The project's goal is to compare the performance of a deep learning model with a traditional machine learning model.
+
+Included Models
+The repository contains the following two models, saved after a parallel training process:
+
+Simple Neural Network (PyTorch): A Feed-Forward Neural Network (FFNN) model suitable for low-resolution image classification tasks. It was trained to recognize digits from 0 to 9.
+
+Random Forest (Scikit-learn): An ensemble model from the decision tree family, known for its robustness and high efficiency on structured data.
+
+Usage Instructions
+To use the models, please follow the steps below.
+
+Prerequisites
+The following Python libraries are required to load and run the models:
+
+pip install torch scikit-learn joblib huggingface-hub
+
+
+Loading the Models
+The following script allows you to download the model files from this repository and load them into memory for later use.
+
+import torch
+import torch.nn as nn
+from joblib import load
+from huggingface_hub import hf_hub_download
+
+# Definition of the Neural Network class for loading.
+class SimpleFFNN(nn.Module):
+    def __init__(self):
+        super(SimpleFFNN, self).__init__()
+        self.fc1 = nn.Linear(28 * 28, 128)
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(128, 10)
+
+    def forward(self, x):
+        x = x.view(-1, 28 * 28)
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        return x
+
+# Hugging Face repository ID
+repo_id = "RAYAuser/ratron-minst-2tech"
+
+# Downloading the files
+ffnn_path = hf_hub_download(repo_id=repo_id, filename="ffnn_model_state.pt")
+rf_path = hf_hub_download(repo_id=repo_id, filename="random_forest_model.joblib")
+
+# Loading the models
+ffnn_model_loaded = SimpleFFNN()
+ffnn_model_loaded.load_state_dict(torch.load(ffnn_path))
+ffnn_model_loaded.eval()
+
+rf_model_loaded = load(rf_path)
+
+print("The models have been loaded successfully.")
+
+Prediction on New Data
+Once the models are loaded, you can use them to make inferences on new images.
+
+import numpy as np
+
+# Creating a numpy array to simulate an image (28x28 pixels)
+sample_image = np.random.rand(28, 28)
+
+# Reshaping the input data for the models
+sample_image_flat = sample_image.reshape(1, -1)
+ffnn_input_tensor = torch.from_numpy(sample_image_flat).float()
+
+# Prediction with the Neural Network
+with torch.no_grad():
+    output = ffnn_model_loaded(ffnn_input_tensor)
+    _, ffnn_prediction = torch.max(output.data, 1)
+
+# Prediction with the Random Forest
+rf_prediction = rf_model_loaded.predict(sample_image_flat)
+
+print(f"Prediction by the Neural Network: {ffnn_prediction.item()}")
+print(f"Prediction by the Random Forest: {rf_prediction[0]}")
+
+Notes
+The SimpleFFNN class must be defined to allow the PyTorch model to be loaded.
+
+A warning regarding Scikit-learn version incompatibility may appear if the version used for training is not identical to the one in your environment. This is generally non-critical.
+
+RAY AUTRA TECHNOLOGY 2025