initial commit

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="MarkdownSettingsMigration">
+    <option name="stateVersion" value="1" />
+  </component>
+</project>

basics.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2024-01-04T16:38:33.550511800Z",
+     "start_time": "2024-01-04T16:38:33.542353Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

main.py

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+from PIL import Image
+import torch
+import torch.nn.functional as F
+from torchvision.models.resnet import resnet50, ResNet50_Weights
+from torchvision.transforms import transforms
+
+# Load the pre-trained model
+model = resnet50(weights=ResNet50_Weights.DEFAULT)
+
+model.eval()
+
+# Define the image transformations
+preprocess = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+
+def is_cat(image_path):
+    # Open the image
+    img = Image.open(image_path)
+
+    # Preprocess the image
+    img_t = preprocess(img)
+    batch_t = torch.unsqueeze(img_t, 0)
+
+    # Make the prediction
+    out = model(batch_t)
+
+    # Apply softmax to get probabilities
+    probabilities = F.softmax(out, dim=1)
+
+    # Get the maximum predicted class and its probability
+    max_prob, max_class = torch.max(probabilities, dim=1)
+    max_prob = max_prob.item()
+    max_class = max_class.item()
+
+    # Check if the maximum predicted class is within the range 281-285
+    if 281 <= max_class <= 285:
+        return max_class, max_prob
+    else:
+        return max_class, None
+
+
+image_path = 'pobrane.jpg'
+max_class, max_prob = is_cat(image_path)
+translator = {
+    281: "tabby cat",
+    282: "tiger cat",
+    283: "persian cat",
+    284: "siamese cat",
+    285: "egyptian cat"
+}
+if max_prob is not None:
+    print(f"The image is recognized as '{translator[max_class]}' with a probability of {round(max_prob * 100, 2)}%")
+else:
+    print(f"The image is not recognized as a class within the range 281-285 ({max_class})")