model update

README.md

@@ -1,65 +1,29 @@
----
-task_categories:
-- object-detection
-tags:
-- yolo
-- yolo11
-- hardhat
-- hat
-base_model:
-- Ultralytics/YOLO11
-widget:
-  - text: "Helmet detection"
-    output:
-      url: example.png
-pipeline_tag: object-detection
-model-index:
-- name: hardhat-or-hat
-  results:
-  - task:
-      type: object-detection
-    dataset:
-      type: safety-equipment
-      name: Safety Equipment
-      args:
-        epochs: 35
-        batch: 2
-        imgsz: 640
-        patience: 5
-        optimizer: SGD
-        lr0: 0.001
-        lrf: 0.01
-        momentum: 0.9
-        weight_decay: 0.0005
-        warmup_epochs: 3
-        warmup_bias_lr: 0.01
-        warmup_momentum: 0.8
-    metrics:
-    - type: precision
-      name: Precision
-      value: 0.844
-    - type: recall
-      name: Recall
-      value: 0.847
-    - type: mAP50
-      name: mAP50
-      value: 0.893
-    - type: mAP50-95
-      name: mAP50-95
-      value: 0.546
----
 
 # Model for detecting Hardhats and Hats
 
 
 <div align="center">
-  <img width="640" alt="luisarizmendi/hardhat-or-hat" src="example.png">
+  <img width="640" alt="luisarizmendi/hardhat-or-hat" src="images/example.png">
 </div>
 
 ## Model binary
 
-You can [download the model from here](https://github.com/luisarizmendi/ai-apps/raw/refs/heads/main/models/luisarizmendi/object-detection-hardhat-or-hat/object-detection-hardhat-or-hat-m.pt)
+You can [download the model from here](https://huggingface.co/luisarizmendi/hardhat-or-hat/tree/main/v2/model/pytorch/best.pt)
+
+
+## Base Model
+
+Ultralytics/YOLO11m
+
+
+## Huggingface page
 
+https://huggingface.co/luisarizmendi/hardhat-or-hat
+
+
+## Model Dataset
+
+[https://universe.roboflow.com/luisarizmendi/hardhat-or-hat](https://universe.roboflow.com/luisarizmendi/hardhat-or-hat)
 
 ## Labels
 
@@ -70,43 +34,34 @@ You can [download the model from here](https://github.com/luisarizmendi/ai-apps/
 ```
 
 
-## Base Model
-
-Ultralytics/YOLO11m
-
 ## Model metrics
 
-```
-YOLO11m summary (fused): 303 layers, 20,032,345 parameters, 0 gradients, 67.7 GFLOPs
-                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)
-                   all       1992      15306      0.844      0.847      0.893      0.546
-                   hat        244        287      0.869      0.811      0.876      0.578
-                helmet       1202       3942      0.916      0.892      0.942       0.61
-             no_helmet        741      11077      0.746      0.838      0.861       0.45
-```
+<div align="center">
+  <img width="640" alt="luisarizmendi/hardhat-or-hat" src="v2/train-val/results.png"> 
+</div>
+
 
 
 <div align="center">
-  <img width="640" alt="luisarizmendi/hardhat-or-hat" src="confusion_matrix_normalized.png"> <img width="640" alt="luisarizmendi/hardhat-or-hat" src="results.png">
+  <img width="640" alt="luisarizmendi/hardhat-or-hat" src="v2/train-val/confusion_matrix_normalized.png"> 
 </div>
 
 
-## Model Dataset
 
-[https://universe.roboflow.com/luisarizmendi/hardhat-or-hat](https://universe.roboflow.com/luisarizmendi/hardhat-or-hat)
+## Model training
 
+You can [review the Jupyter notebook here](dev/prototyping.ipynb)
 
-## Model training
 
-You can [review the Jupyter notebook here](https://github.com/luisarizmendi/ai-apps/blob/main/dev/hardhat-or-hat/train.ipynb)
 
 ### Hyperparameters
 
 ```
-epochs: 35
-batch: 2
+base model: yolov11x.pt
+epochs: 150
+batch: 16
 imgsz: 640
-patience: 5
+patience: 15
 optimizer: 'SGD'
 lr0: 0.001
 lrf: 0.01
@@ -117,23 +72,6 @@ warmup_bias_lr: 0.01
 warmup_momentum: 0.8
 ```
 
-### Augmentation
-
-```
-hsv_h=0.015,  # Image HSV-Hue augmentationc
-hsv_s=0.7,   # Image HSV-Saturation augmentation
-hsv_v=0.4,   # Image HSV-Value augmentation
-degrees=10,  # Image rotation (+/- deg)
-translate=0.1,  # Image translation (+/- fraction)
-scale=0.3,   # Image scale (+/- gain)
-shear=0.0,   # Image shear (+/- deg)
-perspective=0.0,  # Image perspective
-flipud=0.1,  # Image flip up-down
-fliplr=0.1,  # Image flip left-right
-mosaic=1.0,  # Image mosaic
-mixup=0.0,   # Image mixup
-```
-
 
 ## Model Usage
 
@@ -144,7 +82,7 @@ If you don't want to run it locally, you can use [this huggingface space](https:
 Remember to check that the Model URL is pointing to the model that you want to test.
 
 <div align="center">
-  <img width="640" alt="luisarizmendi/hardhat-or-hat" src="https://huggingface.co/luisarizmendi/hardhat-or-hat/resolve/main/spaces-example.png">
+  <img width="640" alt="luisarizmendi/hardhat-or-hat" src="images/spaces-example.png">
 </div>
 
 
@@ -161,7 +99,7 @@ opencv-python
 torch
 ```
 
-Then [run the python code below ](https://github.com/luisarizmendi/ai-apps/raw/refs/heads/main/models/luisarizmendi/object-detector-hardhat-or-hat/run_model.py) and open `http://localhost:7860` in a browser to upload and scan the images.
+Then [run the python code below ](dev/object-detection-model-file/pytorch/object-detection-pytorch.py) and open `http://localhost:8800` in a browser to upload and scan the images.
 
 
 ```
@@ -172,7 +110,7 @@ import os
 import cv2
 import torch
 
-DEFAULT_MODEL_URL = "https://github.com/luisarizmendi/ai-apps/raw/refs/heads/main/models/luisarizmendi/object-detection-hardhat-or-hat/object-detection-hardhat-or-hat-m.pt"
+DEFAULT_MODEL_URL = "https://huggingface.co/luisarizmendi/hardhat-or-hat/tree/main/v2/model/pytorch/best.pt"
 
 def detect_objects_in_files(model_input, files):
     """

dev/object-detection-model-file/pytorch/Containerfile

@@ -0,0 +1,21 @@
+FROM registry.access.redhat.com/ubi9/python-39:latest AS base
+
+USER root
+
+RUN dnf install -y \
+    mesa-libGL \
+    && dnf clean all \
+    && python3 -m ensurepip --upgrade
+    
+COPY requirements.txt /opt/app-root/src/
+
+RUN python3 -m pip install --upgrade pip
+RUN python3 -m pip install --no-cache-dir  -r /opt/app-root/src/requirements.txt
+
+COPY object-detection-pytorch.py /opt/app-root/src
+
+WORKDIR /opt/app-root/src
+
+EXPOSE 8800
+
+CMD ["python", "object-detection-pytorch.py"]

run_model.py → dev/object-detection-model-file/pytorch/object-detection-pytorch.py

@@ -2,10 +2,19 @@ import gradio as gr
 from ultralytics import YOLO
 from PIL import Image
 import os
-import cv2
-import torch
+import cv2 
+import torch 
 
-DEFAULT_MODEL_URL = "https://github.com/luisarizmendi/ai-apps/raw/refs/heads/main/models/luisarizmendi/object-detector-hardhat-or-hat/object-detector-hardhat-or-hat.pt"
+
+def load_model(model_input):
+    model = YOLO(model_input)
+    if torch.cuda.is_available():
+        model.to('cuda') 
+        print("Using GPU for inference")
+    else:
+        print("Using CPU for inference")
+        
+    return model
 
 def detect_objects_in_files(model_input, files):
     """
@@ -14,37 +23,32 @@ def detect_objects_in_files(model_input, files):
     if not files:
         return "No files uploaded.", []
 
-    model = YOLO(str(model_input))
-    if torch.cuda.is_available():
-        model.to('cuda')
-        print("Using GPU for inference")
-    else:
-        print("Using CPU for inference")
-
+    model = load_model(model_input)
+    
     results_images = []
     for file in files:
         try:
             image = Image.open(file).convert("RGB")
-            results = model(image)
+            results = model(image) 
             result_img_bgr = results[0].plot()
             result_img_rgb = cv2.cvtColor(result_img_bgr, cv2.COLOR_BGR2RGB)
-            results_images.append(result_img_rgb)
-
+            results_images.append(result_img_rgb)   
+         
             # If you want that images appear one by one (slower)
-            #yield "Processing image...", results_images
-
+            #yield "Processing image...", results_images  
+                
         except Exception as e:
             return f"Error processing file: {file}. Exception: {str(e)}", []
 
-    del model
+    del model  
     torch.cuda.empty_cache()
-
+    
     return "Processing completed.", results_images
 
 interface = gr.Interface(
     fn=detect_objects_in_files,
     inputs=[
-        gr.Textbox(value=DEFAULT_MODEL_URL, label="Model URL", placeholder="Enter the model URL"),
+        gr.File(label="Upload Model file"),
         gr.Files(file_types=["image"], label="Select Images"),
     ],
     outputs=[
@@ -56,4 +60,4 @@ interface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    interface.launch()
+    interface.launch(server_name="0.0.0.0", server_port=8800)

dev/object-detection-model-file/pytorch/requirements.txt

@@ -0,0 +1,5 @@
+gradio
+ultralytics
+Pillow
+opencv-python
+torch

dev/prototyping.ipynb

@@ -0,0 +1,460 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# YOLOv11 Training with Roboflow Dataset\n",
+    "\n",
+    "This notebook demonstrates how to train a YOLOv11 model using a dataset from Roboflow. It includes:\n",
+    "- Automatic GPU/CPU detection\n",
+    "- Configurable training parameters\n",
+    "- Training visualization and analysis\n",
+    "\n",
+    "## Step 1: Install Dependencies\n",
+    "First, we'll install the required packages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For Training\n",
+    "!pip install ultralytics roboflow \n",
+    "\n",
+    "# For Storage\n",
+    "!pip install minio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Import Libraries\n",
+    "Import all necessary libraries for training and analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Common\n",
+    "import  os\n",
+    "\n",
+    "# For Dataset manipulation\n",
+    "import yaml\n",
+    "from roboflow import Roboflow\n",
+    "\n",
+    "# For training\n",
+    "import torch\n",
+    "from ultralytics import YOLO\n",
+    "\n",
+    "# For Storage\n",
+    "from minio import Minio\n",
+    "from minio.error import S3Error"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Download Dataset from Roboflow\n",
+    "Connect to Roboflow and download the dataset. Make sure to use your own API key and project details.\n",
+    "\n",
+    "**Remember to replace the placeholders with your values**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rf = Roboflow(api_key=\"xxxxxxxxxxxxxxxxx\")  # Replace with your API key\n",
+    "project = rf.workspace(\"yyyyyyyyyyyyyy\").project(\"zzzzzzzzzzzzzzzzzzz\") # Replace with your workspace and project names\n",
+    "version = project.version(1111111111111111111111111111) # Replace with your version number\n",
+    "dataset = version.download(\"yolov11\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You'll need to explicitly specify the paths to each data split (training, validation, and test) in your configuration. This ensures YOLO can correctly locate and utilize your dataset files.\n",
+    "\n",
+    "This is done in the `data.yaml` file. If you open that file you will see these paths that you need to update:\n",
+    "\n",
+    "```\n",
+    "train: ../train/images\n",
+    "val: ../valid/images\n",
+    "test: ../test/images\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f\"Dataset downloaded to: {dataset.location}\")\n",
+    "\n",
+    "dataset_yaml_path = f\"{dataset.location}/data.yaml\"\n",
+    "\n",
+    "with open(dataset_yaml_path, \"r\") as file:\n",
+    "    data_config = yaml.safe_load(file)\n",
+    "\n",
+    "data_config[\"train\"] = f\"{dataset.location}/train/images\"\n",
+    "data_config[\"val\"] = f\"{dataset.location}/valid/images\"\n",
+    "data_config[\"test\"] = f\"{dataset.location}/test/images\"\n",
+    "\n",
+    "with open(dataset_yaml_path, \"w\") as file:\n",
+    "    yaml.safe_dump(data_config, file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: Configure Hyperparameters\n",
+    "Set up GPU/CPU detection (code automatically detects and use GPU if available)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(f\"Using device: {device} ({'GPU' if device.type == 'cuda' else 'CPU'})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define all training parameters in a single configuration dictionary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "CONFIG = {\n",
+    "    'name': 'yolo_hardhat',\n",
+    "    'model': 'yolo11m.pt',  # Model size options: n, s, m, l, x\n",
+    "    'data': dataset.location + \"/data.yaml\",\n",
+    "    'epochs': 1,  # Set the number of epochs (keep 1 for Mock Training)\n",
+    "    'batch': 1 ,  # Adjust batch size based on device\n",
+    "    'imgsz': 640,\n",
+    "    'patience': 15,\n",
+    "    'device': device,\n",
+    "    \n",
+    "    # Optimizer settings\n",
+    "    'optimizer': 'SGD',\n",
+    "    'lr0': 0.001,\n",
+    "    'lrf': 0.005,\n",
+    "    'momentum': 0.9,\n",
+    "    'weight_decay': 0.0005,\n",
+    "    'warmup_epochs': 3,\n",
+    "    'warmup_bias_lr': 0.01,\n",
+    "    'warmup_momentum': 0.8,\n",
+    "    'amp': False,\n",
+    "    \n",
+    "    # Data augmentation settings\n",
+    "    'augment': True,\n",
+    "    'hsv_h': 0.015,  # HSV-Hue augmentation\n",
+    "    'hsv_s': 0.7,    # HSV-Saturation augmentation\n",
+    "    'hsv_v': 0.4,    # HSV-Value augmentation\n",
+    "    'degrees': 10,    # Image rotation (+/- deg)\n",
+    "    'translate': 0.1, # Image translation\n",
+    "    'scale': 0.3,    # Image scale\n",
+    "    'shear': 0.0,    # Image shear\n",
+    "    'perspective': 0.0,  # Image perspective\n",
+    "    'flipud': 0.1,   # Flip up-down\n",
+    "    'fliplr': 0.1,   # Flip left-right\n",
+    "    'mosaic': 1.0,   # Mosaic augmentation\n",
+    "    'mixup': 0.0,    # Mixup augmentation\n",
+    "}\n",
+    "\n",
+    "# Configure PyTorch for GPU memory allocation\n",
+    "os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5: Load Model\n",
+    "Initialize the YOLO model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = YOLO(CONFIG['model'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6: Start Training\n",
+    "\n",
+    "Begin the training process. By default, the `train` method handles both \"training\" and \"validation\" sets. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_train = model.train(\n",
+    "    name=CONFIG['name'],\n",
+    "    data=CONFIG['data'],\n",
+    "    epochs=CONFIG['epochs'],\n",
+    "    batch=CONFIG['batch'],\n",
+    "    imgsz=CONFIG['imgsz'],\n",
+    "    patience=CONFIG['patience'],\n",
+    "    device=CONFIG['device'],\n",
+    "    verbose=True,\n",
+    "    \n",
+    "    # Optimizer parameters\n",
+    "    optimizer=CONFIG['optimizer'],\n",
+    "    lr0=CONFIG['lr0'],\n",
+    "    lrf=CONFIG['lrf'],\n",
+    "    momentum=CONFIG['momentum'],\n",
+    "    weight_decay=CONFIG['weight_decay'],\n",
+    "    warmup_epochs=CONFIG['warmup_epochs'],\n",
+    "    warmup_bias_lr=CONFIG['warmup_bias_lr'],\n",
+    "    warmup_momentum=CONFIG['warmup_momentum'],\n",
+    "    amp=CONFIG['amp'],\n",
+    "    \n",
+    "    # Augmentation parameters\n",
+    "    augment=CONFIG['augment'],\n",
+    "    hsv_h=CONFIG['hsv_h'],\n",
+    "    hsv_s=CONFIG['hsv_s'],\n",
+    "    hsv_v=CONFIG['hsv_v'],\n",
+    "    degrees=CONFIG['degrees'],\n",
+    "    translate=CONFIG['translate'],\n",
+    "    scale=CONFIG['scale'],\n",
+    "    shear=CONFIG['shear'],\n",
+    "    perspective=CONFIG['perspective'],\n",
+    "    flipud=CONFIG['flipud'],\n",
+    "    fliplr=CONFIG['fliplr'],\n",
+    "    mosaic=CONFIG['mosaic'],\n",
+    "    mixup=CONFIG['mixup'],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 7: Evaluate Model\n",
+    "\n",
+    " Evaluate the model on the test set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_test = model.val(data=CONFIG['data'], split='test', device=CONFIG['device'], imgsz=CONFIG['imgsz'])\n",
+    "\n",
+    "#print(\"Test Results:\", results_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 8: (Optional) Model Export\n",
+    "\n",
+    "Export the trained YOLO model to ONNX format for deployment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.export(format='onnx', imgsz=CONFIG['imgsz'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Export the trained YOLO model to TorchScript"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#model.export(format=\"torchscript\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 9: Store the Model\n",
+    "\n",
+    "Save the trained model to the Object Storage system configured in your Workbench connection. \n",
+    "\n",
+    "Start by getting the credentials and configuring variables for accessing Object Storage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "AWS_S3_ENDPOINT_NAME = os.getenv(\"AWS_S3_ENDPOINT\", \"\").replace('https://', '').replace('http://', '')\n",
+    "AWS_ACCESS_KEY_ID = os.getenv(\"AWS_ACCESS_KEY_ID\")\n",
+    "AWS_SECRET_ACCESS_KEY = os.getenv(\"AWS_SECRET_ACCESS_KEY\")\n",
+    "AWS_S3_BUCKET = os.getenv(\"AWS_S3_BUCKET\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the S3 client."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client = Minio(\n",
+    "    AWS_S3_ENDPOINT_NAME,\n",
+    "    access_key=AWS_ACCESS_KEY_ID,\n",
+    "    secret_key=AWS_SECRET_ACCESS_KEY,\n",
+    "    secure=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select files to be uploaded (files generated while training and validating the model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_path_train = results_train.save_dir\n",
+    "weights_path = os.path.join(model_path_train, \"weights\")\n",
+    "model_path_test = results_test.save_dir\n",
+    "\n",
+    "files_train = [os.path.join(model_path_train, f) for f in os.listdir(model_path_train) if os.path.isfile(os.path.join(model_path_train, f))]\n",
+    "files_models = [os.path.join(weights_path, f) for f in os.listdir(weights_path) if os.path.isfile(os.path.join(weights_path, f))]\n",
+    "files_test = [os.path.join(model_path_test, f) for f in os.listdir(model_path_test) if os.path.isfile(os.path.join(model_path_test, f))]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Upload the files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "directory_name= os.path.basename(model_path_train)\n",
+    "\n",
+    "for file_path_train in files_train:\n",
+    "    try:\n",
+    "        client.fput_object(AWS_S3_BUCKET, \"prototype/notebook/\" + directory_name + \"/train-val/\" + os.path.basename(file_path_train), file_path_train)\n",
+    "        print(f\"'{os.path.basename(file_path_train)}' is successfully uploaded as object to bucket '{AWS_S3_BUCKET}'.\")\n",
+    "    except S3Error as e:\n",
+    "        print(\"Error occurred: \", e)\n",
+    "\n",
+    "for file_path_model in files_models:\n",
+    "    try:\n",
+    "        client.fput_object(AWS_S3_BUCKET, \"prototype/notebook/\" + directory_name + \"/\" + os.path.basename(file_path_model), file_path_model)\n",
+    "        print(f\"'{os.path.basename(file_path_model)}' is successfully uploaded as object to bucket '{AWS_S3_BUCKET}'.\")\n",
+    "    except S3Error as e:\n",
+    "        print(\"Error occurred: \", e)\n",
+    "\n",
+    "for file_path_test in files_test:\n",
+    "    try:\n",
+    "        client.fput_object(AWS_S3_BUCKET, \"prototype/notebook/\" + directory_name + \"/test/\" + os.path.basename(file_path_test), file_path_test)\n",
+    "        print(f\"'{os.path.basename(file_path_test)}' is successfully uploaded as object to bucket '{AWS_S3_BUCKET}'.\")\n",
+    "    except S3Error as e:\n",
+    "        print(\"Error occurred: \", e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 10: Remove local files\n",
+    "\n",
+    "Once you uploaded the Model data to the Object Storage, you can remove the local files to save disk space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!rm -rf {model_path_train}\n",
+    "!rm -rf {model_path_test}"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "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": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

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+task: detect
+mode: train
+model: yolo11x.pt
+data: /opt/app-root/src/Hardhat-or-Hat-4/data.yaml
+epochs: 65
+time: null
+patience: 100
+batch: 16
+imgsz: 640
+save: true
+save_period: -1
+cache: false
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+workers: 8
+project: null
+name: hardhat-v1
+exist_ok: false
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+seed: 0
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+stream_buffer: false
+visualize: false
+augment: false
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+classes: null
+retina_masks: false
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+show: false
+save_frames: false
+save_txt: false
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+show_labels: true
+show_conf: true
+show_boxes: true
+line_width: null
+format: torchscript
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+dynamic: false
+simplify: true
+opset: null
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+nms: false
+lr0: 0.01
+lrf: 0.01
+momentum: 0.937
+weight_decay: 0.0005
+warmup_epochs: 3.0
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+box: 7.5
+cls: 0.5
+dfl: 1.5
+pose: 12.0
+kobj: 1.0
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