Changes to Code

README.md

@@ -1,14 +1,14 @@
----
-title: YOLO Application Toolkit
-emoji: 🌍
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 4.44.1
-app_file: app.py
-pinned: false
-license: mit
-short_description: Inference for various YOLO11 trained models in contexts.
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: YOLO-Application Toolkit
+emoji: 🌍
+colorFrom: yellow
+colorTo: purple
+sdk: gradio
+sdk_version: 4.44.1
+app_file: app.py
+pinned: false
+license: mit
+short_description: Inference for various YOLO11 trained models in contexts.
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,165 +1,181 @@
-from typing import Tuple
-import gradio as gr
-import numpy as np
-import supervision as sv
-from ultralytics import YOLO
-import os
-
-MARKDOWN = """
-<h1 style='text-align: left'>YOLO Application Toolkit 🚀</h1>
-<p>Welcome to the YOLO Application Toolkit! This demo highlights the powerful detection capabilities of various YOLO models pre-trained on different datasets. 🎉
-
-Easily detect objects in images and videos on the go. Perfect for quick experimentation and practical use. 🎉🔍</p>
-
-
-**YOLO11**
-<div style="display: flex; align-items: center;">
-    <a href="https://docs.ultralytics.com/models/yolo11/" style="margin-right: 10px;">
-      <img src="https://badges.aleen42.com/src/github.svg">
-    </a>
-    <a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolo11-object-detection-on-custom-dataset.ipynb?ref=blog.roboflow.com" style="margin-right: 10px;">
-      <img src="https://colab.research.google.com/assets/colab-badge.svg">
-    </a>
-  </div>
-
-Powered by Roboflow [Inference](https://github.com/roboflow/inference), 
-[Supervision](https://github.com/roboflow/supervision) and [Ultralytics](https://github.com/ultralytics/ultralytics).🔥
-"""
-
-# Load models dynamically
-
-MODELS = {
-    "YOLO11m (COCO128)": YOLO("https://huggingface.co/mbar0075/YOLO-Application-Toolkit/resolve/main/yolo11m.pt"),
-    "American Sign Language (ASL) YOLO11m": YOLO("https://huggingface.co/mbar0075/YOLO-Application-Toolkit/resolve/main/yolo11m_sign_language.pt"),
-    # "Microscopic Cell Detection YOLO11m": YOLO("https://huggingface.co/mbar0075/YOLO-Application-Toolkit/resolve/main/yolo11m_microscope_cells.pt"),
-    "Website Screenshots YOLO11m": YOLO("https://huggingface.co/mbar0075/YOLO-Application-Toolkit/resolve/main/yolo11m_website_screenshots.pt"),
-    "Zoo Animals YOLO11m": YOLO("https://huggingface.co/mbar0075/YOLO-Application-Toolkit/resolve/main/yolo11m_zoo_animals.pt"),
-    "Pinned Circuit Boards YOLO11m": YOLO("https://huggingface.co/mbar0075/YOLO-Application-Toolkit/resolve/main/yolo11m_circuit_boards.pt"),
-    "Smoke Detection YOLO11m": YOLO("https://huggingface.co/mbar0075/YOLO-Application-Toolkit/resolve/main/yolo11m_smoke_detection.pt"),
-    "Blood Cell Detection YOLO11m": YOLO("https://huggingface.co/mbar0075/YOLO-Application-Toolkit/resolve/main/yolo11m_blood_cells.pt"),
-}
-
-
-example_dir = "https://huggingface.co/spaces/mbar0075/YOLO-Application-Toolkit/resolve/main/examples/"
-# Your existing example dictionary
-EXAMPLE_DICT = {
-    "YOLO11m (COCO128)": example_dir + "1.jpg",
-    "American Sign Language (ASL) YOLO11m": example_dir + "2.jpg",
-    # "Microscopic Cell Detection YOLO11m": example_dir + "3.jpg",
-    "Website Screenshots YOLO11m": example_dir + "4.jpg",
-    "Zoo Animals YOLO11m": example_dir + "5.jpg",
-    "Pinned Circuit Boards YOLO11m": example_dir + "6.jpg",
-    "Smoke Detection YOLO11m": example_dir + "7.jpg",
-    "Blood Cell Detection YOLO11m": example_dir + "8.jpg",
-}
-
-LABEL_ANNOTATORS = sv.LabelAnnotator()
-BOUNDING_BOX_ANNOTATORS = sv.BoxAnnotator()
-
-def detect_and_annotate(
-    model,
-    input_image: np.ndarray,
-    confidence_threshold: float,
-    iou_threshold: float,
-    class_id_mapping: dict = None
-) -> np.ndarray:
-    result = model(input_image, conf=confidence_threshold, iou=iou_threshold)[0]
-    detections = sv.Detections.from_ultralytics(result)
-
-    if class_id_mapping:
-        detections.class_id = np.array([class_id_mapping[class_id] for class_id in detections.class_id])
-
-    labels = [f"{class_name} ({confidence:.2f})" for class_name, confidence in zip(detections['class_name'], detections.confidence)]
-    
-    annotated_image = input_image.copy()
-    annotated_image = BOUNDING_BOX_ANNOTATORS.annotate(scene=annotated_image, detections=detections)
-    annotated_image = LABEL_ANNOTATORS.annotate(scene=annotated_image, detections=detections, labels=labels)
-    return annotated_image
-
-def process_image(
-    input_image,
-    yolov11_confidence_threshold: float,
-    iou_threshold: float,
-    model_name: str
-) -> np.ndarray:
-    # Load the selected model from the preloaded models
-    model = MODELS[model_name]
-
-    # Process the image
-    return detect_and_annotate(model, np.array(input_image), yolov11_confidence_threshold, iou_threshold)
-
-# Gradio UI components
-yolo_11s_confidence_threshold_component = gr.Slider(
-    minimum=0,
-    maximum=1.0,
-    value=0.3,
-    step=0.01,
-    label="YOLO11m Confidence Threshold",
-    info=(
-        "The confidence threshold for the YOLO model. Lower the threshold to "
-        "reduce false negatives, enhancing the model's sensitivity to detect "
-        "sought-after objects. Conversely, increase the threshold to minimize false "
-        "positives, preventing the model from identifying objects it shouldn't."
-    )
-)
-
-iou_threshold_component = gr.Slider(
-    minimum=0,
-    maximum=1.0,
-    value=0.5,
-    step=0.01,
-    label="IoU Threshold",
-    info=(
-        "The Intersection over Union (IoU) threshold for non-maximum suppression. "
-        "Decrease the value to lessen the occurrence of overlapping bounding boxes, "
-        "making the detection process stricter. On the other hand, increase the value "
-        "to allow more overlapping bounding boxes, accommodating a broader range of "
-        "detections."
-    )
-)
-
-model_dropdown = gr.Dropdown(
-    choices=list(MODELS.keys()),
-    label="Select Model",
-    value="YOLO11m (COCO128)",
-    info=(
-        "Choose the YOLO model you want to use for object detection. Each model is "
-        "trained on a specific dataset, making them suitable for various detection tasks."
-    )
-)
-
-def update_example(model_name):
-    return EXAMPLE_DICT[model_name]
-
-with gr.Blocks() as demo:
-    gr.Markdown(MARKDOWN)
-    
-    with gr.Accordion("Configuration", open=False):
-        yolo_11s_confidence_threshold_component.render()
-        iou_threshold_component.render()
-
-    with gr.Row():
-        model_dropdown.render()
-
-    with gr.Row():
-        image_input_component = gr.Image(type='pil', label='Input Image')
-        yolo_11s_output_component = gr.Image(type='pil', label='YOLO11s Output')
-
-    submit_button = gr.Button(value='Submit', scale=1, variant='primary')
-
-    gr.Examples(
-        fn=process_image,
-        examples=[[EXAMPLE_DICT[i], 0.3, 0.5, i] for i in EXAMPLE_DICT.keys()],
-        inputs=[image_input_component, yolo_11s_confidence_threshold_component, iou_threshold_component, model_dropdown],
-        outputs=[yolo_11s_output_component]
-    )
-
-    model_dropdown.change(fn=update_example, inputs=model_dropdown, outputs=image_input_component)
-
-    submit_button.click(
-        fn=process_image,
-        inputs=[image_input_component, yolo_11s_confidence_threshold_component, iou_threshold_component, model_dropdown],
-        outputs=[yolo_11s_output_component]
-    )
-
+from typing import Tuple
+import gradio as gr
+import numpy as np
+import supervision as sv
+from ultralytics import YOLO
+import os
+
+MARKDOWN = """
+<h1 style='text-align: left'>YOLO-Application Toolkit 🚀</h1>
+<p>Welcome to the YOLO-Application Toolkit! This demo highlights the powerful detection capabilities of various YOLO models pre-trained on different datasets. 🎉
+
+Easily detect different objects for various contexts in images on the go. Perfect for quick experimentation and practical use. 🎉🔍</p>
+
+
+**YOLO11**
+<div style="display: flex; align-items: center;">
+    <a href="https://docs.ultralytics.com/models/yolo11/" style="margin-right: 10px;">
+      <img src="https://badges.aleen42.com/src/github.svg">
+    </a>
+    <a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolo11-object-detection-on-custom-dataset.ipynb?ref=blog.roboflow.com" style="margin-right: 10px;">
+      <img src="https://colab.research.google.com/assets/colab-badge.svg">
+    </a>
+  </div>
+
+Powered by 
+[Ultralytics](https://github.com/ultralytics/ultralytics).🔥
+
+"""
+# Roboflow [Inference](https://github.com/roboflow/inference), [Supervision](https://github.com/roboflow/supervision) and [Ultralytics](https://github.com/ultralytics/ultralytics).🔥
+
+# Load models dynamically
+
+MODELS = {
+    "YOLO11m (COCO128)": YOLO("https://huggingface.co/mbar0075/YOLO-Application-Toolkit/resolve/main/yolo11m.pt"),
+    "American Sign Language (ASL) (YOLO11m)": YOLO("https://huggingface.co/mbar0075/YOLO-Application-Toolkit/resolve/main/yolo11m_sign_language.pt"),
+    # "Microscopic Cell Detection (YOLO11m)": YOLO("https://huggingface.co/mbar0075/YOLO-Application-Toolkit/resolve/main/yolo11m_microscope_cells.pt"),
+    "Website Screenshots (YOLO11m)": YOLO("https://huggingface.co/mbar0075/YOLO-Application-Toolkit/resolve/main/yolo11m_website_screenshots.pt"),
+    "Zoo Animals (YOLO11m)": YOLO("https://huggingface.co/mbar0075/YOLO-Application-Toolkit/resolve/main/yolo11m_zoo_animals.pt"),
+    "Pinned Circuit Boards (YOLO11m)": YOLO("https://huggingface.co/mbar0075/YOLO-Application-Toolkit/resolve/main/yolo11m_circuit_boards.pt"),
+    "Smoke Detection (YOLO11m)": YOLO("https://huggingface.co/mbar0075/YOLO-Application-Toolkit/resolve/main/yolo11m_smoke_detection.pt"),
+    "Blood Cell Detection (YOLO11m)": YOLO("https://huggingface.co/mbar0075/YOLO-Application-Toolkit/resolve/main/yolo11m_blood_cells.pt"),
+    "Coins Detection (YOLO11m)": YOLO("https://huggingface.co/mbar0075/YOLO-Application-Toolkit/resolve/main/yolo11m_coins.pt"),
+    "Pizza Toppings Detection (YOLO11m)": YOLO("https://huggingface.co/mbar0075/YOLO-Application-Toolkit/resolve/main/yolo11m_pizza.pt"),
+    "Aquarium Fish Detection (YOLO11m)": YOLO("https://huggingface.co/mbar0075/YOLO-Application-Toolkit/resolve/main/yolo11m_aquarium_fish.pt"),
+    "Pelvis X-ray Detection (YOLO11m)": YOLO("https://huggingface.co/mbar0075/YOLO-Application-Toolkit/resolve/main/yolo11m_pelvis_xray.pt"),
+    "Road Signs Detection (YOLO11m)": YOLO("https://huggingface.co/mbar0075/YOLO-Application-Toolkit/resolve/main/yolo11m_road_signs.pt"),
+}
+
+
+example_dir = "https://huggingface.co/spaces/mbar0075/YOLO-Application-Toolkit/resolve/main/examples/"
+# Your existing example dictionary
+EXAMPLE_DICT = {
+    "YOLO11m (COCO128)": example_dir + "1.jpg",
+    "American Sign Language (ASL) (YOLO11m)": example_dir + "2.jpg",
+    # "Microscopic Cell Detection (YOLO11m)": example_dir + "3.jpg",
+    "Website Screenshots (YOLO11m)": example_dir + "4.jpg",
+    "Zoo Animals (YOLO11m)": example_dir + "5.jpg",
+    "Pinned Circuit Boards (YOLO11m)": example_dir + "6.jpg",
+    "Smoke Detection (YOLO11m)": example_dir + "7.jpg",
+    "Blood Cell Detection (YOLO11m)": example_dir + "8.jpg",
+    "Coins Detection (YOLO11m)": example_dir + "9.jpeg",
+    "Pizza Toppings Detection (YOLO11m)": example_dir + "10.jpg",
+    "Aquarium Fish Detection (YOLO11m)": example_dir + "11.jpg",
+    "Pelvis X-ray Detection (YOLO11m)": example_dir + "12.jpg",
+    "Road Signs Detection (YOLO11m)": example_dir + "13.jpg",
+}
+
+LABEL_ANNOTATORS = sv.LabelAnnotator()
+BOUNDING_BOX_ANNOTATORS = sv.BoxAnnotator()
+
+def detect_and_annotate(
+    model,
+    input_image: np.ndarray,
+    confidence_threshold: float,
+    iou_threshold: float,
+    class_id_mapping: dict = None
+) -> np.ndarray:
+    result = model(input_image, conf=confidence_threshold, iou=iou_threshold)[0]
+    # Extracting Annotated Image
+    return result.plot()
+
+    # For supervision annotations:
+    detections = sv.Detections.from_ultralytics(result)
+
+    if class_id_mapping:
+        detections.class_id = np.array([class_id_mapping[class_id] for class_id in detections.class_id])
+
+    labels = [f"{class_name} ({confidence:.2f})" for class_name, confidence in zip(detections['class_name'], detections.confidence)]
+    
+    annotated_image = input_image.copy()
+    annotated_image = BOUNDING_BOX_ANNOTATORS.annotate(scene=annotated_image, detections=detections)
+    annotated_image = LABEL_ANNOTATORS.annotate(scene=annotated_image, detections=detections, labels=labels)
+    return annotated_image
+
+def process_image(
+    input_image,
+    yolov11_confidence_threshold: float,
+    iou_threshold: float,
+    model_name: str
+) -> np.ndarray:
+    # Load the selected model from the preloaded models
+    model = MODELS[model_name]
+
+    # Process the image
+    return detect_and_annotate(model, np.array(input_image), yolov11_confidence_threshold, iou_threshold)
+
+# Gradio UI components
+yolo_11s_confidence_threshold_component = gr.Slider(
+    minimum=0,
+    maximum=1.0,
+    value=0.3,
+    step=0.01,
+    label="YOLO11m Confidence Threshold",
+    info=(
+        "The confidence threshold for the YOLO model. Lower the threshold to "
+        "reduce false negatives, enhancing the model's sensitivity to detect "
+        "sought-after objects. Conversely, increase the threshold to minimize false "
+        "positives, preventing the model from identifying objects it shouldn't."
+    )
+)
+
+iou_threshold_component = gr.Slider(
+    minimum=0,
+    maximum=1.0,
+    value=0.5,
+    step=0.01,
+    label="IoU Threshold",
+    info=(
+        "The Intersection over Union (IoU) threshold for non-maximum suppression. "
+        "Decrease the value to lessen the occurrence of overlapping bounding boxes, "
+        "making the detection process stricter. On the other hand, increase the value "
+        "to allow more overlapping bounding boxes, accommodating a broader range of "
+        "detections."
+    )
+)
+
+model_dropdown = gr.Dropdown(
+    choices=list(MODELS.keys()),
+    label="Select Model",
+    value="YOLO11m (COCO128)",
+    info=(
+        "Choose the YOLO model you want to use for object detection. Each model is "
+        "trained on a specific dataset, making them suitable for various detection tasks."
+    )
+)
+
+def update_example(model_name):
+    return EXAMPLE_DICT[model_name]
+
+with gr.Blocks() as demo:
+    gr.Markdown(MARKDOWN)
+    
+    with gr.Accordion("Configuration", open=False):
+        yolo_11s_confidence_threshold_component.render()
+        iou_threshold_component.render()
+
+    with gr.Row():
+        model_dropdown.render()
+
+    with gr.Row():
+        image_input_component = gr.Image(type='pil', label='Input Image')
+        yolo_11s_output_component = gr.Image(type='pil', label='YOLO11s Output')
+
+    submit_button = gr.Button(value='Submit', scale=1, variant='primary')
+
+    gr.Examples(
+        fn=process_image,
+        examples=[[EXAMPLE_DICT[i], 0.3, 0.5, i] for i in EXAMPLE_DICT.keys()],
+        inputs=[image_input_component, yolo_11s_confidence_threshold_component, iou_threshold_component, model_dropdown],
+        outputs=[yolo_11s_output_component]
+    )
+
+    model_dropdown.change(fn=update_example, inputs=model_dropdown, outputs=image_input_component)
+
+    submit_button.click(
+        fn=process_image,
+        inputs=[image_input_component, yolo_11s_confidence_threshold_component, iou_threshold_component, model_dropdown],
+        outputs=[yolo_11s_output_component]
+    )
+
 demo.launch(debug=False, show_error=True, max_threads=1)

train_YOLOv11.ipynb

@@ -0,0 +1,369 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<center><h1>YOLO11 Training</h1>\n",
+        "<h2>Matthias Bartolo</h2>\n",
+        "\n",
+        "</center>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<h3>Package Imports</h3>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "aFbKjvakDnZE",
+        "outputId": "dbe28130-1112-4141-e265-65d3f6b06acc"
+      },
+      "outputs": [],
+      "source": [
+        "# !pip install --upgrade roboflow ultralytics"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dWZ2DvvEDnZF"
+      },
+      "source": [
+        "**<h3>Required libraries.</h3>**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "9Zbf3zFkDnZG"
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "import os\n",
+        "import ultralytics\n",
+        "import locale\n",
+        "import glob\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "from IPython import display\n",
+        "from ultralytics import YOLO\n",
+        "from IPython.display import display, Image\n",
+        "from roboflow import Roboflow\n",
+        "\n",
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FyRdDYkqAKN4"
+      },
+      "source": [
+        "**<h3>Using GPU if one is available.</h3>**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!nvidia-smi"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dgNdkO48DnZG",
+        "outputId": "11ca77d8-7b09-4d02-a899-0dfdc5aaac2d"
+      },
+      "outputs": [],
+      "source": [
+        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+        "print(device)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CjpPg4mGKc1v",
+        "outputId": "7035eeee-b2d1-438c-bb57-188bd34ea8eb"
+      },
+      "outputs": [],
+      "source": [
+        "# Retrieving the current working directory\n",
+        "HOME = os.getcwd()\n",
+        "print(HOME)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3C3EO_2zNChu"
+      },
+      "source": [
+        "**<h3>Downloading the Roboflow dataset.</h3>**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BSd93ZJzZZKt",
+        "outputId": "cd51037c-4df9-415e-ecd8-540168fb544a"
+      },
+      "outputs": [],
+      "source": [
+        "if not os.path.isdir(os.path.join(HOME, 'datasets')):\n",
+        "    os.mkdir(os.path.join(HOME, 'datasets'))\n",
+        "os.chdir(os.path.join(HOME, 'datasets'))\n",
+        "\n",
+        "\n",
+        "rf = Roboflow(api_key=\"<ROBOFLOW API KEY>\")\n",
+        "project = rf.workspace(\"<WORKSPACE>\").project(\"<PROJECT>\")\n",
+        "version = project.version(2)\n",
+        "dataset = version.download(\"yolov11\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "**<h3>Training the YOLO11 model.</h3>**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_WRhnBXjDnZH",
+        "outputId": "09bcb11a-5656-4c53-8564-51b29cc08d6c"
+      },
+      "outputs": [],
+      "source": [
+        "# Specifying the paths\n",
+        "model_path = 'yolo11m.pt'\n",
+        "\n",
+        "yaml_path  = dataset.location+\"/data.yaml\"\n",
+        "\n",
+        "# Creating YOLO object\n",
+        "model = YOLO(model_path)\n",
+        "\n",
+        "# Specifying training parameters\n",
+        "num_epochs = 100  # Number of epochs\n",
+        "batch_size = 16 #8 # Adjust based on GPU memory\n",
+        "image_size = 640  # Decrease for faster training\n",
+        "\n",
+        "# Training configuration\n",
+        "train_config = {\n",
+        "    'data': yaml_path,\n",
+        "    'imgsz': image_size,\n",
+        "    'batch': batch_size,\n",
+        "    'epochs': num_epochs,\n",
+        "    'device': 0,  # Use GPU 0\n",
+        "    # 'workers': 1,  # Number of data loading workers\n",
+        "    'optimizer': 'Adam',  # Use Adam optimizer\n",
+        "    'cache': True,#'disk',  # Cache images for faster training\n",
+        "    'patience': 10,  # epochs to wait before decreasing LR\n",
+        "    'val': True,  # Run validation during training\n",
+        "    'plots': True,  # Run plots during training\n",
+        "}\n",
+        "\n",
+        "# Train the model\n",
+        "model.train(**train_config)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6ODk1VTlevxn"
+      },
+      "source": [
+        "**<h3>Validating the YOLO11 model on the Validation subset.</h3>**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IkrxsRHoV67H",
+        "outputId": "ecb3eac0-3a4f-4df6-fdb0-9db829ad7a76"
+      },
+      "outputs": [],
+      "source": [
+        "locale.getpreferredencoding = lambda: \"UTF-8\"\n",
+        "# !pip install aspose-words"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YpyuwrNlXc1P",
+        "outputId": "f4718557-cd29-4208-d5fa-ad0776a893b7"
+      },
+      "outputs": [],
+      "source": [
+        "model.val() #This will output a train file however it will be on the validation data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "**<h3>Validating the YOLO11 model on the Testing subset.</h3>**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model.val(split='test') #This will output a train file however it will be on the test data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i4eASbcWkQBq"
+      },
+      "source": [
+        "**<h3>Testing the YOLO11 model on the Testing subset.</h3>**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Wjc1ctZykYuf",
+        "outputId": "38b730a6-7d15-4f9f-b812-e814fc861557"
+      },
+      "outputs": [],
+      "source": [
+        "!yolo task=detect \\\n",
+        "mode=predict \\\n",
+        "model={HOME}/datasets/runs/detect/train1/weights/best.pt \\\n",
+        "source={dataset.location}/test/images \\\n",
+        "save=True"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uBkrV5y5X9CH"
+      },
+      "source": [
+        "**<h3>Training Results.</h3>**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        },
+        "id": "mWCxLBpMbKoQ",
+        "outputId": "722f7b87-4d71-4e44-85aa-0c6a33f08362"
+      },
+      "outputs": [],
+      "source": [
+        "from IPython.display import Image as IPyImage\n",
+        "\n",
+        "IPyImage(filename=f'{HOME}/datasets/runs/detect/train/results.png', width=600)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "t6EZwLBNfjKP"
+      },
+      "source": [
+        "**<h3>Testing Resultant Images.</h3>**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "background_save": true
+        },
+        "id": "mzkcnDekgUWf"
+      },
+      "outputs": [],
+      "source": [
+        "counter =0\n",
+        "limit = 10\n",
+        "for image_path in glob.glob(f'{HOME}/datasets/runs/detect/predict/*.jpg'):\n",
+        "      display(Image(filename=image_path))\n",
+        "      print(\"\\n\")\n",
+        "      counter += 1\n",
+        "      if counter == limit:\n",
+        "          break"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}