diff --git a/src/Components/HMI/ui/public/admin/style_sheet.md b/src/Components/HMI/ui/public/admin/style_sheet.md
deleted file mode 100644
index 4b6496573..000000000
--- a/src/Components/HMI/ui/public/admin/style_sheet.md
+++ /dev/null
@@ -1,92 +0,0 @@
-# Project Echo UI Style Guide (v0.2) — Admin (updated Dec 2025)
-
-This file contains the Project Echo Admin Dashboard style guide (v0.2) with the additional CSS/implementation notes and snippets used in the repo. It includes the sidebar hover/focus updates and examples for integrating the guide's variables into your global stylesheet.
-
-Summary
-- Primary action color: `--primary: #2F6E4F` (green)
-- Accent: `--accent: #DDECE3` (soft green)
-- Page background: `--page: #F5F1EA`
-- Surface background: `--bg: #FFFFFF`
-
-1) Principles
-- Simple & tough: minimal decoration, high contrast, few variants.
-- 8‑pt spacing grid.
-
-2) Theme Tokens (wireframe skin)
-```css
-:root{
-  --page:#F5F1EA; --bg:#FFFFFF;
-  --ink:#1F2D23; --muted:#6F7B74; --border:#E4E1D9;
-  --primary:#2F6E4F; --accent:#DDECE3;
-  --success:#2F6E4F; --warning:#D29B38; --danger:#C8473C;
-  --radius-window:12px; --radius:8px;
-  --space-1:8px; --space-2:16px; --space-3:24px;
-  --shadow-card:0 4px 12px rgba(0,0,0,.08);
-  --shadow-soft:0 1px 2px rgba(0,0,0,.06);
-  --font-sans: Inter, system-ui, "Segoe UI", Roboto, Helvetica, Arial, sans-serif;
-}
-```
-
-3) Sidebar (Left Toolbar) — Implementation notes
-The Admin sidebar should follow the light spec. Key points implemented in the repo:
-- Width: 240px (collapsed: 64px). Background: solid `var(--page)`.
-- Item height: 40px; left padding: 16px; gap: 8px; icon size: 20px.
-- Text: 14px Medium; color `var(--ink)` (inactive 80% via rgba if needed).
-- Active state: 3px left bar in `var(--primary)`, item bg `#EAF3EC`.
-- Hover: light green `#F0F7F2` (consistent with sample CSS below).
-
-4) Accessibility
-- Text contrast ≥ AA.
-- Visible focus on interactive elements: 2px ring in `--primary`.
-- Hit target ≥ 40×40 px for icons/controls.
-
-5) Inline CSS snippets used in the components
-Use these snippets as a starting point; ideally move them into your global stylesheet (e.g., `admin.css`) and prefer variables above hard-coded values.
-
-Sidebar hover / focus / active (recommended)
-```css
-/* Sidebar base */
-.sidebar, .left-sidebar{width:240px;background:var(--page);border-right:1px solid var(--border);}
-.sidebar ul{list-style:none;margin:0;padding:0}
-.sidebar a{display:block;padding:0 var(--space-2);height:40px;line-height:40px;color:var(--ink);font:500 14px var(--font-sans);text-decoration:none;transition:background .12s ease,color .12s ease}
-
-/* Hover: light green */
-.sidebar a:hover{background:#F0F7F2;color:var(--ink)}
-
-/* Focus ring: visible and accessible */
-.sidebar a:focus{outline:2px solid rgba(47,110,79,0.12);outline-offset:2px}
-
-/* Active state: left bar + background */
-.sidebar .active{background:#EAF3EC;position:relative}
-.sidebar .active::before{content:'';position:absolute;left:0;top:0;bottom:0;width:3px;background:var(--primary)}
-
-/* Icon colour rule (use CSS instead of inline styles) */
-.sidebar .icon i{color:var(--primary);width:20px;height:20px;display:inline-block}
-
-/* CTA button styling (full width) */
-.sidebar .cta{display:block;text-align:center;background:var(--primary);color:#fff;padding:10px 12px;border-radius:8px;font-weight:600;text-decoration:none}
-.sidebar .cta:hover{filter:brightness(.96)}
-```
-
-6) Migration notes — what I changed in the repo
-- Replaced the previous icon-heavy sidebar HTML with a minimal text-link sidebar in `sidebar-component.html` and added `sidebar-component2.html` as an alternate copy.
-- Added inline `<style>` blocks in both sidebar files to ensure the hover (#F0F7F2) and focus styles are immediately applied. These should be moved into the global admin stylesheet.
-
-7) Recommended next steps (apply globally)
-1. Add the variables (the `:root` block above) into your global CSS (e.g., `src/styles/admin.css` or similar).
-2. Move the sidebar rules from this file into the global stylesheet and remove inline `<style>` blocks from components.
-3. Add `.active` class logic in your JS router or server-rendering to mark the current page's sidebar item as active.
-4. Use `aria-current="page"` on the active link for accessibility where appropriate.
-
-8) Example: replace inline icon colour styles
-Search for all occurrences of `style="color: #2F6E4F;"` in components and replace with CSS targeting `.sidebar .icon i` to keep styling consistent and centralized.
-
-9) Notes on collapsing behavior
-- Keep `.hide-menu` spans for text; CSS can hide/show depending on `.collapsed` state.
-- Collapsed width: set `.sidebar.collapsed{width:64px}` and hide text labels with `.sidebar.collapsed .hide-menu{display:none}`.
-
-10) Contact
-If you want, I can move these snippets into your project's main stylesheet and remove the inline styles now present in the components.
-
----
-File created/updated: `src/Components/HMI/ui/public/admin/component/style_sheet.md`
diff --git a/src/Components/docker-compose.yml b/src/Components/docker-compose.yml
index 430e497e3..977df175f 100644
--- a/src/Components/docker-compose.yml
+++ b/src/Components/docker-compose.yml
@@ -104,6 +104,10 @@ services:
       - ./API:/app # Mount the API's src directory to /app in the container
       - credentials_volume:/root/.config/gcloud/
       - weather_data:/app/weather_data  # Add a volume for weather data
+    environment:
+      MAIL_STARTTLS: "true"
+      MAIL_SSL_TLS: "false"
+
 
     stdin_open: false
     tty: true
diff --git a/src/Prototypes/Computer Vision/Final/Create Dataset/How to Create A Creatures Dataset_v2.docx b/src/Prototypes/Computer Vision/Final/Create Dataset/How to Create A Creatures Dataset_v2.docx
new file mode 100644
index 000000000..419df55db
Binary files /dev/null and b/src/Prototypes/Computer Vision/Final/Create Dataset/How to Create A Creatures Dataset_v2.docx differ
diff --git a/src/Prototypes/Computer Vision/Final/Create Dataset/creatures.json b/src/Prototypes/Computer Vision/Final/Create Dataset/creatures.json
new file mode 100644
index 000000000..deff77b2e
--- /dev/null
+++ b/src/Prototypes/Computer Vision/Final/Create Dataset/creatures.json	
@@ -0,0 +1,14 @@
+{
+  "birds": [
+    "Acanthagenys rufogularis",
+    "Acanthiza apicalis",
+    "Acanthiza chrysorrhoa",
+    "Acanthiza lineata",
+    "Acanthiza nana",
+    "Acanthiza pusilla",
+    "Acanthiza reguloides",
+    "Acanthiza uropygialis",
+    "Acanthorhynchus tenuirostris",
+    "Accipiter cirrocephalus"
+  ]
+}
\ No newline at end of file
diff --git a/src/Prototypes/Computer Vision/Final/Create Dataset/creatures_augmention.ipynb b/src/Prototypes/Computer Vision/Final/Create Dataset/creatures_augmention.ipynb
new file mode 100644
index 000000000..d20affd91
--- /dev/null
+++ b/src/Prototypes/Computer Vision/Final/Create Dataset/creatures_augmention.ipynb	
@@ -0,0 +1,660 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yOqkhdZMvj8J",
+        "outputId": "c773c40b-08ab-46c3-9ec7-1e566cb7ecf9"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
+            "Found 49 class folders in '/content/drive/MyDrive/BIRDS_DATASET2'.\n",
+            "\n",
+            "Processing class 'Aythya australi_300x300'\n",
+            "Found 140 base images in '/content/drive/MyDrive/BIRDS_DATASET2/Aythya australi_300x300'.\n",
+            "Copied 140 original images to '/content/drive/MyDrive/Birds_Dataset3/Aythya australi_300x300'.\n",
+            "[Aythya australi_300x300] Created 200 images so far...\n",
+            "[Aythya australi_300x300] Created 300 images so far...\n",
+            "[Aythya australi_300x300] Created 400 images so far...\n",
+            "[Aythya australi_300x300] Created 500 images so far...\n",
+            "Done 'Aythya australi_300x300'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/Aythya australi_300x300'.\n",
+            "\n",
+            "Processing class 'Burhinus grallarius_300x300'\n",
+            "Found 293 base images in '/content/drive/MyDrive/BIRDS_DATASET2/Burhinus grallarius_300x300'.\n",
+            "Copied 293 original images to '/content/drive/MyDrive/Birds_Dataset3/Burhinus grallarius_300x300'.\n",
+            "[Burhinus grallarius_300x300] Created 300 images so far...\n",
+            "[Burhinus grallarius_300x300] Created 400 images so far...\n",
+            "[Burhinus grallarius_300x300] Created 500 images so far...\n",
+            "Done 'Burhinus grallarius_300x300'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/Burhinus grallarius_300x300'.\n",
+            "\n",
+            "Processing class 'Eolophus roseicapilla_300x300'\n",
+            "Found 400 base images in '/content/drive/MyDrive/BIRDS_DATASET2/Eolophus roseicapilla_300x300'.\n",
+            "Copied 400 original images to '/content/drive/MyDrive/Birds_Dataset3/Eolophus roseicapilla_300x300'.\n",
+            "[Eolophus roseicapilla_300x300] Created 500 images so far...\n",
+            "Done 'Eolophus roseicapilla_300x300'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/Eolophus roseicapilla_300x300'.\n",
+            "\n",
+            "Processing class 'acanthagenys_rufogularis_300x300a'\n",
+            "Found 27 base images in '/content/drive/MyDrive/BIRDS_DATASET2/acanthagenys_rufogularis_300x300a'.\n",
+            "Copied 27 original images to '/content/drive/MyDrive/Birds_Dataset3/acanthagenys_rufogularis_300x300a'.\n",
+            "[acanthagenys_rufogularis_300x300a] Created 100 images so far...\n",
+            "[acanthagenys_rufogularis_300x300a] Created 200 images so far...\n",
+            "[acanthagenys_rufogularis_300x300a] Created 300 images so far...\n",
+            "[acanthagenys_rufogularis_300x300a] Created 400 images so far...\n",
+            "[acanthagenys_rufogularis_300x300a] Created 500 images so far...\n",
+            "Done 'acanthagenys_rufogularis_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/acanthagenys_rufogularis_300x300a'.\n",
+            "\n",
+            "Processing class 'acanthiza_apicalis_300x300a'\n",
+            "Found 11 base images in '/content/drive/MyDrive/BIRDS_DATASET2/acanthiza_apicalis_300x300a'.\n",
+            "Copied 11 original images to '/content/drive/MyDrive/Birds_Dataset3/acanthiza_apicalis_300x300a'.\n",
+            "[acanthiza_apicalis_300x300a] Created 100 images so far...\n",
+            "[acanthiza_apicalis_300x300a] Created 200 images so far...\n",
+            "[acanthiza_apicalis_300x300a] Created 300 images so far...\n",
+            "[acanthiza_apicalis_300x300a] Created 400 images so far...\n",
+            "[acanthiza_apicalis_300x300a] Created 500 images so far...\n",
+            "Done 'acanthiza_apicalis_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/acanthiza_apicalis_300x300a'.\n",
+            "\n",
+            "Processing class 'acanthiza_chrysorrhoa_300x300a'\n",
+            "Found 22 base images in '/content/drive/MyDrive/BIRDS_DATASET2/acanthiza_chrysorrhoa_300x300a'.\n",
+            "Copied 22 original images to '/content/drive/MyDrive/Birds_Dataset3/acanthiza_chrysorrhoa_300x300a'.\n",
+            "[acanthiza_chrysorrhoa_300x300a] Created 100 images so far...\n",
+            "[acanthiza_chrysorrhoa_300x300a] Created 200 images so far...\n",
+            "[acanthiza_chrysorrhoa_300x300a] Created 300 images so far...\n",
+            "[acanthiza_chrysorrhoa_300x300a] Created 400 images so far...\n",
+            "[acanthiza_chrysorrhoa_300x300a] Created 500 images so far...\n",
+            "Done 'acanthiza_chrysorrhoa_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/acanthiza_chrysorrhoa_300x300a'.\n",
+            "\n",
+            "Processing class 'acanthiza_lineata_300x300a'\n",
+            "Found 28 base images in '/content/drive/MyDrive/BIRDS_DATASET2/acanthiza_lineata_300x300a'.\n",
+            "Copied 28 original images to '/content/drive/MyDrive/Birds_Dataset3/acanthiza_lineata_300x300a'.\n",
+            "[acanthiza_lineata_300x300a] Created 100 images so far...\n",
+            "[acanthiza_lineata_300x300a] Created 200 images so far...\n",
+            "[acanthiza_lineata_300x300a] Created 300 images so far...\n",
+            "[acanthiza_lineata_300x300a] Created 400 images so far...\n",
+            "[acanthiza_lineata_300x300a] Created 500 images so far...\n",
+            "Done 'acanthiza_lineata_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/acanthiza_lineata_300x300a'.\n",
+            "\n",
+            "Processing class 'acanthiza_nana_300x300a'\n",
+            "Found 17 base images in '/content/drive/MyDrive/BIRDS_DATASET2/acanthiza_nana_300x300a'.\n",
+            "Copied 17 original images to '/content/drive/MyDrive/Birds_Dataset3/acanthiza_nana_300x300a'.\n",
+            "[acanthiza_nana_300x300a] Created 100 images so far...\n",
+            "[acanthiza_nana_300x300a] Created 200 images so far...\n",
+            "[acanthiza_nana_300x300a] Created 300 images so far...\n",
+            "[acanthiza_nana_300x300a] Created 400 images so far...\n",
+            "[acanthiza_nana_300x300a] Created 500 images so far...\n",
+            "Done 'acanthiza_nana_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/acanthiza_nana_300x300a'.\n",
+            "\n",
+            "Processing class 'acanthiza_pusilla_300x300a'\n",
+            "Found 34 base images in '/content/drive/MyDrive/BIRDS_DATASET2/acanthiza_pusilla_300x300a'.\n",
+            "Copied 34 original images to '/content/drive/MyDrive/Birds_Dataset3/acanthiza_pusilla_300x300a'.\n",
+            "[acanthiza_pusilla_300x300a] Created 100 images so far...\n",
+            "[acanthiza_pusilla_300x300a] Created 200 images so far...\n",
+            "[acanthiza_pusilla_300x300a] Created 300 images so far...\n",
+            "[acanthiza_pusilla_300x300a] Created 400 images so far...\n",
+            "[acanthiza_pusilla_300x300a] Created 500 images so far...\n",
+            "Done 'acanthiza_pusilla_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/acanthiza_pusilla_300x300a'.\n",
+            "\n",
+            "Processing class 'acanthiza_uropygialis_300x300a'\n",
+            "Found 10 base images in '/content/drive/MyDrive/BIRDS_DATASET2/acanthiza_uropygialis_300x300a'.\n",
+            "Copied 10 original images to '/content/drive/MyDrive/Birds_Dataset3/acanthiza_uropygialis_300x300a'.\n",
+            "[acanthiza_uropygialis_300x300a] Created 100 images so far...\n",
+            "[acanthiza_uropygialis_300x300a] Created 200 images so far...\n",
+            "[acanthiza_uropygialis_300x300a] Created 300 images so far...\n",
+            "[acanthiza_uropygialis_300x300a] Created 400 images so far...\n",
+            "[acanthiza_uropygialis_300x300a] Created 500 images so far...\n",
+            "Done 'acanthiza_uropygialis_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/acanthiza_uropygialis_300x300a'.\n",
+            "\n",
+            "Processing class 'acanthorhynchus_tenuirostris_300x300a'\n",
+            "Found 55 base images in '/content/drive/MyDrive/BIRDS_DATASET2/acanthorhynchus_tenuirostris_300x300a'.\n",
+            "Copied 55 original images to '/content/drive/MyDrive/Birds_Dataset3/acanthorhynchus_tenuirostris_300x300a'.\n",
+            "[acanthorhynchus_tenuirostris_300x300a] Created 100 images so far...\n",
+            "[acanthorhynchus_tenuirostris_300x300a] Created 200 images so far...\n",
+            "[acanthorhynchus_tenuirostris_300x300a] Created 300 images so far...\n",
+            "[acanthorhynchus_tenuirostris_300x300a] Created 400 images so far...\n",
+            "[acanthorhynchus_tenuirostris_300x300a] Created 500 images so far...\n",
+            "Done 'acanthorhynchus_tenuirostris_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/acanthorhynchus_tenuirostris_300x300a'.\n",
+            "\n",
+            "Processing class 'accipiter_cooperi_300x300a'\n",
+            "Found 5 base images in '/content/drive/MyDrive/BIRDS_DATASET2/accipiter_cooperi_300x300a'.\n",
+            "Copied 5 original images to '/content/drive/MyDrive/Birds_Dataset3/accipiter_cooperi_300x300a'.\n",
+            "[accipiter_cooperi_300x300a] Created 100 images so far...\n",
+            "[accipiter_cooperi_300x300a] Created 200 images so far...\n",
+            "[accipiter_cooperi_300x300a] Created 300 images so far...\n",
+            "[accipiter_cooperi_300x300a] Created 400 images so far...\n",
+            "[accipiter_cooperi_300x300a] Created 500 images so far...\n",
+            "Done 'accipiter_cooperi_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/accipiter_cooperi_300x300a'.\n",
+            "\n",
+            "Processing class 'accipiter_fasciatus_300x300a'\n",
+            "Found 14 base images in '/content/drive/MyDrive/BIRDS_DATASET2/accipiter_fasciatus_300x300a'.\n",
+            "Copied 14 original images to '/content/drive/MyDrive/Birds_Dataset3/accipiter_fasciatus_300x300a'.\n",
+            "[accipiter_fasciatus_300x300a] Created 100 images so far...\n",
+            "[accipiter_fasciatus_300x300a] Created 200 images so far...\n",
+            "[accipiter_fasciatus_300x300a] Created 300 images so far...\n",
+            "[accipiter_fasciatus_300x300a] Created 400 images so far...\n",
+            "[accipiter_fasciatus_300x300a] Created 500 images so far...\n",
+            "Done 'accipiter_fasciatus_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/accipiter_fasciatus_300x300a'.\n",
+            "\n",
+            "Processing class 'acrocephalus_australis_300x300a'\n",
+            "Found 24 base images in '/content/drive/MyDrive/BIRDS_DATASET2/acrocephalus_australis_300x300a'.\n",
+            "Copied 24 original images to '/content/drive/MyDrive/Birds_Dataset3/acrocephalus_australis_300x300a'.\n",
+            "[acrocephalus_australis_300x300a] Created 100 images so far...\n",
+            "[acrocephalus_australis_300x300a] Created 200 images so far...\n",
+            "[acrocephalus_australis_300x300a] Created 300 images so far...\n",
+            "[acrocephalus_australis_300x300a] Created 400 images so far...\n",
+            "[acrocephalus_australis_300x300a] Created 500 images so far...\n",
+            "Done 'acrocephalus_australis_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/acrocephalus_australis_300x300a'.\n",
+            "\n",
+            "Processing class 'alauda_arvensis_300x300a'\n",
+            "Found 75 base images in '/content/drive/MyDrive/BIRDS_DATASET2/alauda_arvensis_300x300a'.\n",
+            "Copied 75 original images to '/content/drive/MyDrive/Birds_Dataset3/alauda_arvensis_300x300a'.\n",
+            "[alauda_arvensis_300x300a] Created 100 images so far...\n",
+            "[alauda_arvensis_300x300a] Created 200 images so far...\n",
+            "[alauda_arvensis_300x300a] Created 300 images so far...\n",
+            "[alauda_arvensis_300x300a] Created 400 images so far...\n",
+            "[alauda_arvensis_300x300a] Created 500 images so far...\n",
+            "Done 'alauda_arvensis_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/alauda_arvensis_300x300a'.\n",
+            "\n",
+            "Processing class 'alisterus_scapularis_300x300a'\n",
+            "Found 93 base images in '/content/drive/MyDrive/BIRDS_DATASET2/alisterus_scapularis_300x300a'.\n",
+            "Copied 93 original images to '/content/drive/MyDrive/Birds_Dataset3/alisterus_scapularis_300x300a'.\n",
+            "[alisterus_scapularis_300x300a] Created 100 images so far...\n",
+            "[alisterus_scapularis_300x300a] Created 200 images so far...\n",
+            "[alisterus_scapularis_300x300a] Created 300 images so far...\n",
+            "[alisterus_scapularis_300x300a] Created 400 images so far...\n",
+            "[alisterus_scapularis_300x300a] Created 500 images so far...\n",
+            "Done 'alisterus_scapularis_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/alisterus_scapularis_300x300a'.\n",
+            "\n",
+            "Processing class 'anas_superciliosa_300x300a'\n",
+            "Found 76 base images in '/content/drive/MyDrive/BIRDS_DATASET2/anas_superciliosa_300x300a'.\n",
+            "Copied 76 original images to '/content/drive/MyDrive/Birds_Dataset3/anas_superciliosa_300x300a'.\n",
+            "[anas_superciliosa_300x300a] Created 100 images so far...\n",
+            "[anas_superciliosa_300x300a] Created 200 images so far...\n",
+            "[anas_superciliosa_300x300a] Created 300 images so far...\n",
+            "[anas_superciliosa_300x300a] Created 400 images so far...\n",
+            "[anas_superciliosa_300x300a] Created 500 images so far...\n",
+            "Done 'anas_superciliosa_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/anas_superciliosa_300x300a'.\n",
+            "\n",
+            "Processing class 'anhinga_novaehollandiae_300x300a'\n",
+            "Found 97 base images in '/content/drive/MyDrive/BIRDS_DATASET2/anhinga_novaehollandiae_300x300a'.\n",
+            "Copied 97 original images to '/content/drive/MyDrive/Birds_Dataset3/anhinga_novaehollandiae_300x300a'.\n",
+            "[anhinga_novaehollandiae_300x300a] Created 100 images so far...\n",
+            "[anhinga_novaehollandiae_300x300a] Created 200 images so far...\n",
+            "[anhinga_novaehollandiae_300x300a] Created 300 images so far...\n",
+            "[anhinga_novaehollandiae_300x300a] Created 400 images so far...\n",
+            "[anhinga_novaehollandiae_300x300a] Created 500 images so far...\n",
+            "Done 'anhinga_novaehollandiae_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/anhinga_novaehollandiae_300x300a'.\n",
+            "\n",
+            "Processing class 'anseranas_semipalmata_300x300a'\n",
+            "Found 69 base images in '/content/drive/MyDrive/BIRDS_DATASET2/anseranas_semipalmata_300x300a'.\n",
+            "Copied 69 original images to '/content/drive/MyDrive/Birds_Dataset3/anseranas_semipalmata_300x300a'.\n",
+            "[anseranas_semipalmata_300x300a] Created 100 images so far...\n",
+            "[anseranas_semipalmata_300x300a] Created 200 images so far...\n",
+            "[anseranas_semipalmata_300x300a] Created 300 images so far...\n",
+            "[anseranas_semipalmata_300x300a] Created 400 images so far...\n",
+            "[anseranas_semipalmata_300x300a] Created 500 images so far...\n",
+            "Done 'anseranas_semipalmata_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/anseranas_semipalmata_300x300a'.\n",
+            "\n",
+            "Processing class 'anthochaera_carunculata_300x300a'\n",
+            "Found 68 base images in '/content/drive/MyDrive/BIRDS_DATASET2/anthochaera_carunculata_300x300a'.\n",
+            "Copied 68 original images to '/content/drive/MyDrive/Birds_Dataset3/anthochaera_carunculata_300x300a'.\n",
+            "[anthochaera_carunculata_300x300a] Created 100 images so far...\n",
+            "[anthochaera_carunculata_300x300a] Created 200 images so far...\n",
+            "[anthochaera_carunculata_300x300a] Created 300 images so far...\n",
+            "[anthochaera_carunculata_300x300a] Created 400 images so far...\n",
+            "[anthochaera_carunculata_300x300a] Created 500 images so far...\n",
+            "Done 'anthochaera_carunculata_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/anthochaera_carunculata_300x300a'.\n",
+            "\n",
+            "Processing class 'anthus_novaeseelandiae_300x300a'\n",
+            "Found 15 base images in '/content/drive/MyDrive/BIRDS_DATASET2/anthus_novaeseelandiae_300x300a'.\n",
+            "Copied 15 original images to '/content/drive/MyDrive/Birds_Dataset3/anthus_novaeseelandiae_300x300a'.\n",
+            "[anthus_novaeseelandiae_300x300a] Created 100 images so far...\n",
+            "[anthus_novaeseelandiae_300x300a] Created 200 images so far...\n",
+            "[anthus_novaeseelandiae_300x300a] Created 300 images so far...\n",
+            "[anthus_novaeseelandiae_300x300a] Created 400 images so far...\n",
+            "[anthus_novaeseelandiae_300x300a] Created 500 images so far...\n",
+            "Done 'anthus_novaeseelandiae_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/anthus_novaeseelandiae_300x300a'.\n",
+            "\n",
+            "Processing class 'antigone_antigone_300x300a'\n",
+            "Found 75 base images in '/content/drive/MyDrive/BIRDS_DATASET2/antigone_antigone_300x300a'.\n",
+            "Copied 75 original images to '/content/drive/MyDrive/Birds_Dataset3/antigone_antigone_300x300a'.\n",
+            "[antigone_antigone_300x300a] Created 100 images so far...\n",
+            "[antigone_antigone_300x300a] Created 200 images so far...\n",
+            "[antigone_antigone_300x300a] Created 300 images so far...\n",
+            "[antigone_antigone_300x300a] Created 400 images so far...\n",
+            "[antigone_antigone_300x300a] Created 500 images so far...\n",
+            "Done 'antigone_antigone_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/antigone_antigone_300x300a'.\n",
+            "\n",
+            "Processing class 'antigone_rubicunda_300x300a'\n",
+            "Found 76 base images in '/content/drive/MyDrive/BIRDS_DATASET2/antigone_rubicunda_300x300a'.\n",
+            "Copied 76 original images to '/content/drive/MyDrive/Birds_Dataset3/antigone_rubicunda_300x300a'.\n",
+            "[antigone_rubicunda_300x300a] Created 100 images so far...\n",
+            "[antigone_rubicunda_300x300a] Created 200 images so far...\n",
+            "[antigone_rubicunda_300x300a] Created 300 images so far...\n",
+            "[antigone_rubicunda_300x300a] Created 400 images so far...\n",
+            "[antigone_rubicunda_300x300a] Created 500 images so far...\n",
+            "Done 'antigone_rubicunda_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/antigone_rubicunda_300x300a'.\n",
+            "\n",
+            "Processing class 'aprosmictus_erythropterus_300x300a'\n",
+            "Found 52 base images in '/content/drive/MyDrive/BIRDS_DATASET2/aprosmictus_erythropterus_300x300a'.\n",
+            "Copied 52 original images to '/content/drive/MyDrive/Birds_Dataset3/aprosmictus_erythropterus_300x300a'.\n",
+            "[aprosmictus_erythropterus_300x300a] Created 100 images so far...\n",
+            "[aprosmictus_erythropterus_300x300a] Created 200 images so far...\n",
+            "[aprosmictus_erythropterus_300x300a] Created 300 images so far...\n",
+            "[aprosmictus_erythropterus_300x300a] Created 400 images so far...\n",
+            "[aprosmictus_erythropterus_300x300a] Created 500 images so far...\n",
+            "Done 'aprosmictus_erythropterus_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/aprosmictus_erythropterus_300x300a'.\n",
+            "\n",
+            "Processing class 'artamus_cinereus_300x300a'\n",
+            "Found 34 base images in '/content/drive/MyDrive/BIRDS_DATASET2/artamus_cinereus_300x300a'.\n",
+            "Copied 34 original images to '/content/drive/MyDrive/Birds_Dataset3/artamus_cinereus_300x300a'.\n",
+            "[artamus_cinereus_300x300a] Created 100 images so far...\n",
+            "[artamus_cinereus_300x300a] Created 200 images so far...\n",
+            "[artamus_cinereus_300x300a] Created 300 images so far...\n",
+            "[artamus_cinereus_300x300a] Created 400 images so far...\n",
+            "[artamus_cinereus_300x300a] Created 500 images so far...\n",
+            "Done 'artamus_cinereus_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/artamus_cinereus_300x300a'.\n",
+            "\n",
+            "Processing class 'artamus_cyanopterus_300x300a'\n",
+            "Found 26 base images in '/content/drive/MyDrive/BIRDS_DATASET2/artamus_cyanopterus_300x300a'.\n",
+            "Copied 26 original images to '/content/drive/MyDrive/Birds_Dataset3/artamus_cyanopterus_300x300a'.\n",
+            "[artamus_cyanopterus_300x300a] Created 100 images so far...\n",
+            "[artamus_cyanopterus_300x300a] Created 200 images so far...\n",
+            "[artamus_cyanopterus_300x300a] Created 300 images so far...\n",
+            "[artamus_cyanopterus_300x300a] Created 400 images so far...\n",
+            "[artamus_cyanopterus_300x300a] Created 500 images so far...\n",
+            "Done 'artamus_cyanopterus_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/artamus_cyanopterus_300x300a'.\n",
+            "\n",
+            "Processing class 'artamus_minor_300x300a'\n",
+            "Found 6 base images in '/content/drive/MyDrive/BIRDS_DATASET2/artamus_minor_300x300a'.\n",
+            "Copied 6 original images to '/content/drive/MyDrive/Birds_Dataset3/artamus_minor_300x300a'.\n",
+            "[artamus_minor_300x300a] Created 100 images so far...\n",
+            "[artamus_minor_300x300a] Created 200 images so far...\n",
+            "[artamus_minor_300x300a] Created 300 images so far...\n",
+            "[artamus_minor_300x300a] Created 400 images so far...\n",
+            "[artamus_minor_300x300a] Created 500 images so far...\n",
+            "Done 'artamus_minor_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/artamus_minor_300x300a'.\n",
+            "\n",
+            "Processing class 'artamus_superciliosus_300x300a'\n",
+            "Found 24 base images in '/content/drive/MyDrive/BIRDS_DATASET2/artamus_superciliosus_300x300a'.\n",
+            "Copied 24 original images to '/content/drive/MyDrive/Birds_Dataset3/artamus_superciliosus_300x300a'.\n",
+            "[artamus_superciliosus_300x300a] Created 100 images so far...\n",
+            "[artamus_superciliosus_300x300a] Created 200 images so far...\n",
+            "[artamus_superciliosus_300x300a] Created 300 images so far...\n",
+            "[artamus_superciliosus_300x300a] Created 400 images so far...\n",
+            "[artamus_superciliosus_300x300a] Created 500 images so far...\n",
+            "Done 'artamus_superciliosus_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/artamus_superciliosus_300x300a'.\n",
+            "\n",
+            "Processing class 'avesâ_sp._300x300a'\n",
+            "Found 78 base images in '/content/drive/MyDrive/BIRDS_DATASET2/avesâ_sp._300x300a'.\n",
+            "Copied 78 original images to '/content/drive/MyDrive/Birds_Dataset3/avesâ_sp._300x300a'.\n",
+            "[avesâ_sp._300x300a] Created 100 images so far...\n",
+            "[avesâ_sp._300x300a] Created 200 images so far...\n",
+            "[avesâ_sp._300x300a] Created 300 images so far...\n",
+            "[avesâ_sp._300x300a] Created 400 images so far...\n",
+            "[avesâ_sp._300x300a] Created 500 images so far...\n",
+            "Done 'avesâ_sp._300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/avesâ_sp._300x300a'.\n",
+            "\n",
+            "Processing class 'barnardius_zonarius_300x300a'\n",
+            "Found 95 base images in '/content/drive/MyDrive/BIRDS_DATASET2/barnardius_zonarius_300x300a'.\n",
+            "Copied 95 original images to '/content/drive/MyDrive/Birds_Dataset3/barnardius_zonarius_300x300a'.\n",
+            "[barnardius_zonarius_300x300a] Created 100 images so far...\n",
+            "[barnardius_zonarius_300x300a] Created 200 images so far...\n",
+            "[barnardius_zonarius_300x300a] Created 300 images so far...\n",
+            "[barnardius_zonarius_300x300a] Created 400 images so far...\n",
+            "[barnardius_zonarius_300x300a] Created 500 images so far...\n",
+            "Done 'barnardius_zonarius_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/barnardius_zonarius_300x300a'.\n",
+            "\n",
+            "Processing class 'biziura_lobata_300x300a'\n",
+            "Found 38 base images in '/content/drive/MyDrive/BIRDS_DATASET2/biziura_lobata_300x300a'.\n",
+            "Copied 38 original images to '/content/drive/MyDrive/Birds_Dataset3/biziura_lobata_300x300a'.\n",
+            "[biziura_lobata_300x300a] Created 100 images so far...\n",
+            "[biziura_lobata_300x300a] Created 200 images so far...\n",
+            "[biziura_lobata_300x300a] Created 300 images so far...\n",
+            "[biziura_lobata_300x300a] Created 400 images so far...\n",
+            "[biziura_lobata_300x300a] Created 500 images so far...\n",
+            "Done 'biziura_lobata_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/biziura_lobata_300x300a'.\n",
+            "\n",
+            "Processing class 'cacatua_sanguinea_300x300a'\n",
+            "Found 80 base images in '/content/drive/MyDrive/BIRDS_DATASET2/cacatua_sanguinea_300x300a'.\n",
+            "Copied 80 original images to '/content/drive/MyDrive/Birds_Dataset3/cacatua_sanguinea_300x300a'.\n",
+            "[cacatua_sanguinea_300x300a] Created 100 images so far...\n",
+            "[cacatua_sanguinea_300x300a] Created 200 images so far...\n",
+            "[cacatua_sanguinea_300x300a] Created 300 images so far...\n",
+            "[cacatua_sanguinea_300x300a] Created 400 images so far...\n",
+            "[cacatua_sanguinea_300x300a] Created 500 images so far...\n",
+            "Done 'cacatua_sanguinea_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/cacatua_sanguinea_300x300a'.\n",
+            "\n",
+            "Processing class 'cacomantis_flabelliformis_300x300a'\n",
+            "Found 25 base images in '/content/drive/MyDrive/BIRDS_DATASET2/cacomantis_flabelliformis_300x300a'.\n",
+            "Copied 25 original images to '/content/drive/MyDrive/Birds_Dataset3/cacomantis_flabelliformis_300x300a'.\n",
+            "[cacomantis_flabelliformis_300x300a] Created 100 images so far...\n",
+            "[cacomantis_flabelliformis_300x300a] Created 200 images so far...\n",
+            "[cacomantis_flabelliformis_300x300a] Created 300 images so far...\n",
+            "[cacomantis_flabelliformis_300x300a] Created 400 images so far...\n",
+            "[cacomantis_flabelliformis_300x300a] Created 500 images so far...\n",
+            "Done 'cacomantis_flabelliformis_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/cacomantis_flabelliformis_300x300a'.\n",
+            "\n",
+            "Processing class 'caligavis_chrysops_300x300a'\n",
+            "Found 36 base images in '/content/drive/MyDrive/BIRDS_DATASET2/caligavis_chrysops_300x300a'.\n",
+            "Copied 36 original images to '/content/drive/MyDrive/Birds_Dataset3/caligavis_chrysops_300x300a'.\n",
+            "[caligavis_chrysops_300x300a] Created 100 images so far...\n",
+            "[caligavis_chrysops_300x300a] Created 200 images so far...\n",
+            "[caligavis_chrysops_300x300a] Created 300 images so far...\n",
+            "[caligavis_chrysops_300x300a] Created 400 images so far...\n",
+            "[caligavis_chrysops_300x300a] Created 500 images so far...\n",
+            "Done 'caligavis_chrysops_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/caligavis_chrysops_300x300a'.\n",
+            "\n",
+            "Processing class 'callocephalon_fimbriatum_300x300a'\n",
+            "Found 50 base images in '/content/drive/MyDrive/BIRDS_DATASET2/callocephalon_fimbriatum_300x300a'.\n",
+            "Copied 50 original images to '/content/drive/MyDrive/Birds_Dataset3/callocephalon_fimbriatum_300x300a'.\n",
+            "[callocephalon_fimbriatum_300x300a] Created 100 images so far...\n",
+            "[callocephalon_fimbriatum_300x300a] Created 200 images so far...\n",
+            "[callocephalon_fimbriatum_300x300a] Created 300 images so far...\n",
+            "[callocephalon_fimbriatum_300x300a] Created 400 images so far...\n",
+            "[callocephalon_fimbriatum_300x300a] Created 500 images so far...\n",
+            "Done 'callocephalon_fimbriatum_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/callocephalon_fimbriatum_300x300a'.\n",
+            "\n",
+            "Processing class 'calyptorhynchus_banksii_300x300a'\n",
+            "Found 97 base images in '/content/drive/MyDrive/BIRDS_DATASET2/calyptorhynchus_banksii_300x300a'.\n",
+            "Copied 97 original images to '/content/drive/MyDrive/Birds_Dataset3/calyptorhynchus_banksii_300x300a'.\n",
+            "[calyptorhynchus_banksii_300x300a] Created 100 images so far...\n",
+            "[calyptorhynchus_banksii_300x300a] Created 200 images so far...\n",
+            "[calyptorhynchus_banksii_300x300a] Created 300 images so far...\n",
+            "[calyptorhynchus_banksii_300x300a] Created 400 images so far...\n",
+            "[calyptorhynchus_banksii_300x300a] Created 500 images so far...\n",
+            "Done 'calyptorhynchus_banksii_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/calyptorhynchus_banksii_300x300a'.\n",
+            "\n",
+            "Processing class 'carduelis_carduelis_300x300a'\n",
+            "Found 71 base images in '/content/drive/MyDrive/BIRDS_DATASET2/carduelis_carduelis_300x300a'.\n",
+            "Copied 71 original images to '/content/drive/MyDrive/Birds_Dataset3/carduelis_carduelis_300x300a'.\n",
+            "[carduelis_carduelis_300x300a] Created 100 images so far...\n",
+            "[carduelis_carduelis_300x300a] Created 200 images so far...\n",
+            "[carduelis_carduelis_300x300a] Created 300 images so far...\n",
+            "[carduelis_carduelis_300x300a] Created 400 images so far...\n",
+            "[carduelis_carduelis_300x300a] Created 500 images so far...\n",
+            "Done 'carduelis_carduelis_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/carduelis_carduelis_300x300a'.\n",
+            "\n",
+            "Processing class 'carterornis_leucotis_300x300a'\n",
+            "Found 5 base images in '/content/drive/MyDrive/BIRDS_DATASET2/carterornis_leucotis_300x300a'.\n",
+            "Copied 5 original images to '/content/drive/MyDrive/Birds_Dataset3/carterornis_leucotis_300x300a'.\n",
+            "[carterornis_leucotis_300x300a] Created 100 images so far...\n",
+            "[carterornis_leucotis_300x300a] Created 200 images so far...\n",
+            "[carterornis_leucotis_300x300a] Created 300 images so far...\n",
+            "[carterornis_leucotis_300x300a] Created 400 images so far...\n",
+            "[carterornis_leucotis_300x300a] Created 500 images so far...\n",
+            "Done 'carterornis_leucotis_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/carterornis_leucotis_300x300a'.\n",
+            "\n",
+            "Processing class 'centropus_phasianinus_300x300a'\n",
+            "Found 48 base images in '/content/drive/MyDrive/BIRDS_DATASET2/centropus_phasianinus_300x300a'.\n",
+            "Copied 48 original images to '/content/drive/MyDrive/Birds_Dataset3/centropus_phasianinus_300x300a'.\n",
+            "[centropus_phasianinus_300x300a] Created 100 images so far...\n",
+            "[centropus_phasianinus_300x300a] Created 200 images so far...\n",
+            "[centropus_phasianinus_300x300a] Created 300 images so far...\n",
+            "[centropus_phasianinus_300x300a] Created 400 images so far...\n",
+            "[centropus_phasianinus_300x300a] Created 500 images so far...\n",
+            "Done 'centropus_phasianinus_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/centropus_phasianinus_300x300a'.\n",
+            "\n",
+            "Processing class 'ceyx_azureus_300x300a'\n",
+            "Found 66 base images in '/content/drive/MyDrive/BIRDS_DATASET2/ceyx_azureus_300x300a'.\n",
+            "Copied 66 original images to '/content/drive/MyDrive/Birds_Dataset3/ceyx_azureus_300x300a'.\n",
+            "[ceyx_azureus_300x300a] Created 100 images so far...\n",
+            "[ceyx_azureus_300x300a] Created 200 images so far...\n",
+            "[ceyx_azureus_300x300a] Created 300 images so far...\n",
+            "[ceyx_azureus_300x300a] Created 400 images so far...\n",
+            "[ceyx_azureus_300x300a] Created 500 images so far...\n",
+            "Done 'ceyx_azureus_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/ceyx_azureus_300x300a'.\n",
+            "\n",
+            "Processing class 'coracina_novaehollandiae_300x300a'\n",
+            "Found 90 base images in '/content/drive/MyDrive/BIRDS_DATASET2/coracina_novaehollandiae_300x300a'.\n",
+            "Copied 90 original images to '/content/drive/MyDrive/Birds_Dataset3/coracina_novaehollandiae_300x300a'.\n",
+            "[coracina_novaehollandiae_300x300a] Created 100 images so far...\n",
+            "[coracina_novaehollandiae_300x300a] Created 200 images so far...\n",
+            "[coracina_novaehollandiae_300x300a] Created 300 images so far...\n",
+            "[coracina_novaehollandiae_300x300a] Created 400 images so far...\n",
+            "[coracina_novaehollandiae_300x300a] Created 500 images so far...\n",
+            "Done 'coracina_novaehollandiae_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/coracina_novaehollandiae_300x300a'.\n",
+            "\n",
+            "Processing class 'coracina_papuensis_300x300a'\n",
+            "Found 11 base images in '/content/drive/MyDrive/BIRDS_DATASET2/coracina_papuensis_300x300a'.\n",
+            "Copied 11 original images to '/content/drive/MyDrive/Birds_Dataset3/coracina_papuensis_300x300a'.\n",
+            "[coracina_papuensis_300x300a] Created 100 images so far...\n",
+            "[coracina_papuensis_300x300a] Created 200 images so far...\n",
+            "[coracina_papuensis_300x300a] Created 300 images so far...\n",
+            "[coracina_papuensis_300x300a] Created 400 images so far...\n",
+            "[coracina_papuensis_300x300a] Created 500 images so far...\n",
+            "Done 'coracina_papuensis_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/coracina_papuensis_300x300a'.\n",
+            "\n",
+            "Processing class 'corcorax_melanorhamphos_300x300a'\n",
+            "Found 41 base images in '/content/drive/MyDrive/BIRDS_DATASET2/corcorax_melanorhamphos_300x300a'.\n",
+            "Copied 41 original images to '/content/drive/MyDrive/Birds_Dataset3/corcorax_melanorhamphos_300x300a'.\n",
+            "[corcorax_melanorhamphos_300x300a] Created 100 images so far...\n",
+            "[corcorax_melanorhamphos_300x300a] Created 200 images so far...\n",
+            "[corcorax_melanorhamphos_300x300a] Created 300 images so far...\n",
+            "[corcorax_melanorhamphos_300x300a] Created 400 images so far...\n",
+            "[corcorax_melanorhamphos_300x300a] Created 500 images so far...\n",
+            "Done 'corcorax_melanorhamphos_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/corcorax_melanorhamphos_300x300a'.\n",
+            "\n",
+            "Processing class 'cormobates_leucophaea_300x300a'\n",
+            "Found 31 base images in '/content/drive/MyDrive/BIRDS_DATASET2/cormobates_leucophaea_300x300a'.\n",
+            "Copied 31 original images to '/content/drive/MyDrive/Birds_Dataset3/cormobates_leucophaea_300x300a'.\n",
+            "[cormobates_leucophaea_300x300a] Created 100 images so far...\n",
+            "[cormobates_leucophaea_300x300a] Created 200 images so far...\n",
+            "[cormobates_leucophaea_300x300a] Created 300 images so far...\n",
+            "[cormobates_leucophaea_300x300a] Created 400 images so far...\n",
+            "[cormobates_leucophaea_300x300a] Created 500 images so far...\n",
+            "Done 'cormobates_leucophaea_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/cormobates_leucophaea_300x300a'.\n",
+            "\n",
+            "Processing class 'corvus_orru_300x300a'\n",
+            "Found 70 base images in '/content/drive/MyDrive/BIRDS_DATASET2/corvus_orru_300x300a'.\n",
+            "Copied 70 original images to '/content/drive/MyDrive/Birds_Dataset3/corvus_orru_300x300a'.\n",
+            "[corvus_orru_300x300a] Created 100 images so far...\n",
+            "[corvus_orru_300x300a] Created 200 images so far...\n",
+            "[corvus_orru_300x300a] Created 300 images so far...\n",
+            "[corvus_orru_300x300a] Created 400 images so far...\n",
+            "[corvus_orru_300x300a] Created 500 images so far...\n",
+            "Done 'corvus_orru_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/corvus_orru_300x300a'.\n",
+            "\n",
+            "Processing class 'coturnix_pectoralis_300x300a'\n",
+            "Found 6 base images in '/content/drive/MyDrive/BIRDS_DATASET2/coturnix_pectoralis_300x300a'.\n",
+            "Copied 6 original images to '/content/drive/MyDrive/Birds_Dataset3/coturnix_pectoralis_300x300a'.\n",
+            "[coturnix_pectoralis_300x300a] Created 100 images so far...\n",
+            "[coturnix_pectoralis_300x300a] Created 200 images so far...\n",
+            "[coturnix_pectoralis_300x300a] Created 300 images so far...\n",
+            "[coturnix_pectoralis_300x300a] Created 400 images so far...\n",
+            "[coturnix_pectoralis_300x300a] Created 500 images so far...\n",
+            "Done 'coturnix_pectoralis_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/coturnix_pectoralis_300x300a'.\n",
+            "\n",
+            "Processing class 'cracticus_nigrogularis_300x300a'\n",
+            "Found 70 base images in '/content/drive/MyDrive/BIRDS_DATASET2/cracticus_nigrogularis_300x300a'.\n",
+            "Copied 70 original images to '/content/drive/MyDrive/Birds_Dataset3/cracticus_nigrogularis_300x300a'.\n",
+            "[cracticus_nigrogularis_300x300a] Created 100 images so far...\n",
+            "[cracticus_nigrogularis_300x300a] Created 200 images so far...\n",
+            "[cracticus_nigrogularis_300x300a] Created 300 images so far...\n",
+            "[cracticus_nigrogularis_300x300a] Created 400 images so far...\n",
+            "[cracticus_nigrogularis_300x300a] Created 500 images so far...\n",
+            "Done 'cracticus_nigrogularis_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/cracticus_nigrogularis_300x300a'.\n",
+            "\n",
+            "Processing class 'cracticus_torquatus_300x300a'\n",
+            "Found 63 base images in '/content/drive/MyDrive/BIRDS_DATASET2/cracticus_torquatus_300x300a'.\n",
+            "Copied 63 original images to '/content/drive/MyDrive/Birds_Dataset3/cracticus_torquatus_300x300a'.\n",
+            "[cracticus_torquatus_300x300a] Created 100 images so far...\n",
+            "[cracticus_torquatus_300x300a] Created 200 images so far...\n",
+            "[cracticus_torquatus_300x300a] Created 300 images so far...\n",
+            "[cracticus_torquatus_300x300a] Created 400 images so far...\n",
+            "[cracticus_torquatus_300x300a] Created 500 images so far...\n",
+            "Done 'cracticus_torquatus_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/cracticus_torquatus_300x300a'.\n",
+            "\n",
+            "Processing class 'zosterops_lateralis_300x300a'\n",
+            "Found 27 base images in '/content/drive/MyDrive/BIRDS_DATASET2/zosterops_lateralis_300x300a'.\n",
+            "Copied 27 original images to '/content/drive/MyDrive/Birds_Dataset3/zosterops_lateralis_300x300a'.\n",
+            "[zosterops_lateralis_300x300a] Created 100 images so far...\n",
+            "[zosterops_lateralis_300x300a] Created 200 images so far...\n",
+            "[zosterops_lateralis_300x300a] Created 300 images so far...\n",
+            "[zosterops_lateralis_300x300a] Created 400 images so far...\n",
+            "[zosterops_lateralis_300x300a] Created 500 images so far...\n",
+            "Done 'zosterops_lateralis_300x300a'. Saved 500 images total in '/content/drive/MyDrive/Birds_Dataset3/zosterops_lateralis_300x300a'.\n",
+            "\n",
+            "All class folders processed.\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "\n",
+        "drive.mount('/content/drive')\n",
+        "\n",
+        "import os\n",
+        "import random\n",
+        "import numpy as np\n",
+        "from PIL import Image, ImageEnhance, ImageFilter\n",
+        "\n",
+        "# --- ROOT FOLDERS & CONFIG ---\n",
+        "file_path = \"/content/drive/MyDrive/BIRDS_DATASET2\"   # root with class folders\n",
+        "output_path = \"/content/drive/MyDrive/Birds_Dataset3\" # root for augmented class folders\n",
+        "target_count_per_class = 500                         # total images per class\n",
+        "\n",
+        "os.makedirs(output_path, exist_ok=True)\n",
+        "random.seed(42)\n",
+        "np.random.seed(42)\n",
+        "\n",
+        "\n",
+        "def random_augment(img):\n",
+        "    \"\"\"Apply a random sequence of augmentations to a PIL Image (RGB).\"\"\"\n",
+        "    w, h = img.size\n",
+        "\n",
+        "    # Horizontal flip\n",
+        "    if random.random() < 0.5:\n",
+        "        img = img.transpose(Image.FLIP_LEFT_RIGHT)\n",
+        "\n",
+        "    # Small rotation\n",
+        "    if random.random() < 0.5:\n",
+        "        angle = random.uniform(-20, 20)  # degrees\n",
+        "        img = img.rotate(angle, resample=Image.BICUBIC)\n",
+        "\n",
+        "    # Random crop & resize back (acts like zoom/translation)\n",
+        "    if random.random() < 0.7:\n",
+        "        scale = random.uniform(0.7, 1.0)  # 0.7 = tighter crop, 1.0 = no crop\n",
+        "        new_w, new_h = int(w * scale), int(h * scale)\n",
+        "        if new_w < w and new_h < h:\n",
+        "            left = random.randint(0, w - new_w)\n",
+        "            top = random.randint(0, h - new_h)\n",
+        "            img = img.crop((left, top, left + new_w, top + new_h))\n",
+        "            img = img.resize((w, h), Image.LANCZOS)\n",
+        "\n",
+        "    # Brightness\n",
+        "    if random.random() < 0.7:\n",
+        "        enhancer = ImageEnhance.Brightness(img)\n",
+        "        img = enhancer.enhance(random.uniform(0.7, 1.3))\n",
+        "\n",
+        "    # Contrast\n",
+        "    if random.random() < 0.7:\n",
+        "        enhancer = ImageEnhance.Contrast(img)\n",
+        "        img = enhancer.enhance(random.uniform(0.7, 1.3))\n",
+        "\n",
+        "    # Colour / saturation\n",
+        "    if random.random() < 0.7:\n",
+        "        enhancer = ImageEnhance.Color(img)\n",
+        "        img = enhancer.enhance(random.uniform(0.7, 1.3))\n",
+        "\n",
+        "    # Slight blur\n",
+        "    if random.random() < 0.3:\n",
+        "        radius = random.uniform(0.3, 1.0)\n",
+        "        img = img.filter(ImageFilter.GaussianBlur(radius=radius))\n",
+        "\n",
+        "    # Add Gaussian noise\n",
+        "    if random.random() < 0.5:\n",
+        "        arr = np.asarray(img).astype(\"float32\")\n",
+        "        noise = np.random.normal(0, 8, arr.shape)  # mean 0, std 8\n",
+        "        arr = arr + noise\n",
+        "        arr = np.clip(arr, 0, 255).astype(\"uint8\")\n",
+        "        img = Image.fromarray(arr)\n",
+        "\n",
+        "    return img\n",
+        "\n",
+        "\n",
+        "def process_class_dir(input_dir, output_dir, target_count):\n",
+        "    \"\"\"Copy originals and create augmentations for one class folder.\"\"\"\n",
+        "    image_paths = [\n",
+        "        os.path.join(input_dir, f)\n",
+        "        for f in os.listdir(input_dir)\n",
+        "        if f.lower().endswith((\".png\", \".jpg\", \".jpeg\"))\n",
+        "    ]\n",
+        "\n",
+        "    if not image_paths:\n",
+        "        print(f\"No images found in '{input_dir}', skipping.\")\n",
+        "        return\n",
+        "\n",
+        "    os.makedirs(output_dir, exist_ok=True)\n",
+        "\n",
+        "    class_name = os.path.basename(input_dir)\n",
+        "    print(f\"\\nProcessing class '{class_name}'\")\n",
+        "    print(f\"Found {len(image_paths)} base images in '{input_dir}'.\")\n",
+        "\n",
+        "    idx = 0\n",
+        "\n",
+        "    # 1) Copy originals\n",
+        "    for path in image_paths:\n",
+        "        img = Image.open(path).convert(\"RGB\")\n",
+        "        idx += 1\n",
+        "        out_path = os.path.join(output_dir, f\"image_{idx:04d}.png\")\n",
+        "        img.save(out_path)\n",
+        "    print(f\"Copied {idx} original images to '{output_dir}'.\")\n",
+        "\n",
+        "    # 2) Generate augmentations\n",
+        "    while idx < target_count:\n",
+        "        src_path = random.choice(image_paths)\n",
+        "        img = Image.open(src_path).convert(\"RGB\")\n",
+        "        aug = random_augment(img)\n",
+        "\n",
+        "        idx += 1\n",
+        "        out_path = os.path.join(output_dir, f\"image_{idx:04d}.png\")\n",
+        "        aug.save(out_path)\n",
+        "\n",
+        "        if idx % 100 == 0 or idx == target_count:\n",
+        "            print(f\"[{class_name}] Created {idx} images so far...\")\n",
+        "\n",
+        "    print(f\"Done '{class_name}'. Saved {idx} images total in '{output_dir}'.\")\n",
+        "\n",
+        "\n",
+        "# --- MAIN: LOOP OVER ALL CLASS FOLDERS ---\n",
+        "class_folders = [\n",
+        "    d for d in sorted(os.listdir(file_path))\n",
+        "    if os.path.isdir(os.path.join(file_path, d))\n",
+        "]\n",
+        "\n",
+        "if not class_folders:\n",
+        "    raise RuntimeError(f\"No subfolders found in '{file_path}'\")\n",
+        "\n",
+        "print(f\"Found {len(class_folders)} class folders in '{file_path}'.\")\n",
+        "\n",
+        "for folder_name in class_folders:\n",
+        "    in_dir = os.path.join(file_path, folder_name)\n",
+        "    out_dir = os.path.join(output_path, folder_name)\n",
+        "    process_class_dir(in_dir, out_dir, target_count_per_class)\n",
+        "\n",
+        "print(\"\\nAll class folders processed.\")\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/src/Prototypes/Computer Vision/Final/Create Dataset/get_creatures_v2.py b/src/Prototypes/Computer Vision/Final/Create Dataset/get_creatures_v2.py
new file mode 100644
index 000000000..30147c9fb
--- /dev/null
+++ b/src/Prototypes/Computer Vision/Final/Create Dataset/get_creatures_v2.py	
@@ -0,0 +1,124 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Jan  6 18:25:08 2025
+
+@author: jblair
+"""
+
+# -*- coding: utf-8 -*-
+
+import os
+import io
+import zipfile
+import time
+import requests
+from PIL import Image
+import re
+import json
+
+API_URL = "https://commons.wikimedia.org/w/api.php"
+BIRDS_JSON = "creatures.json"   # <--*.json file with scientific bird names
+LIMIT_PER_BIRD = 100          # images per bird
+
+SESSION = requests.Session()
+SESSION.headers.update({
+    "User-Agent": "BirdImageCollector/1.1 (Deakin University Capstone Project; s225149998@deakin.edu.au)"
+})
+
+
+def fetch_image_pages(search_term, limit):
+    params = {
+        "action": "query",
+        "format": "json",
+        "generator": "search",
+        "gsrsearch": search_term,
+        "gsrlimit": 50,          # Wikimedia max per request
+        "gsrnamespace": 6,       # File: namespace (images)
+        "prop": "imageinfo",
+        "iiprop": "url",
+        "iiurlwidth": 300,
+        "iiurlheight": 300,
+    }
+
+    fetched = 0
+    while fetched < limit:
+        resp = SESSION.get(API_URL, params=params, timeout=30)
+        resp.raise_for_status()
+        data = resp.json()
+
+        pages = data.get("query", {}).get("pages", {})
+        if not pages:
+            break
+
+        for page in pages.values():
+            if fetched >= limit:
+                break
+            infos = page.get("imageinfo", [])
+            if not infos:
+                continue
+            info = infos[0]
+            url = info.get("thumburl") or info.get("url")
+            if not url:
+                continue
+            yield url
+            fetched += 1
+
+        cont = data.get("continue")
+        if not cont:
+            break
+        params.update(cont)
+
+
+def download_and_save(url, search_term2, out_dir, idx):
+    resp = SESSION.get(url, timeout=30)
+    resp.raise_for_status()
+    img = Image.open(io.BytesIO(resp.content)).convert("RGB")
+    img = img.resize((300, 300), Image.LANCZOS)
+    filename = f"{search_term2}_{idx:03d}.png"
+    path = os.path.join(out_dir, filename)
+    img.save(path, format="PNG")
+    return path
+
+
+def main():
+    with open(BIRDS_JSON, "r", encoding="utf-8") as f:
+        birds_data = json.load(f)
+
+    birds = birds_data.get("birds", [])
+    print(f"Found {len(birds)} birds in {BIRDS_JSON}")
+
+    for bird_name in birds:
+        search_term = bird_name
+        search_term2 = re.sub(r"\s+", "_", search_term.strip()).lower()
+        out_dir = f"{search_term2}_300x300a"
+        os.makedirs(out_dir, exist_ok=True)
+
+        print(f"\n=== Processing bird: {bird_name} ===")
+        print(f"Output dir: {out_dir}")
+
+        paths = []
+        for i, url in enumerate(fetch_image_pages(search_term, LIMIT_PER_BIRD), start=1):
+            print(f"  Downloading {i}: {url}")
+            try:
+                p = download_and_save(url, search_term2, out_dir, i)
+                paths.append(p)
+                time.sleep(0.2)  # be polite to the API
+            except Exception as e:
+                print(f"  Failed on {url}: {e}")
+
+        if not paths:
+            print(f"  No images downloaded for {bird_name}, skipping zip.")
+            continue
+
+        zip_path = f"{search_term2}_300x300.zip"
+        with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as zf:
+            for p in paths:
+                zf.write(p, arcname=os.path.basename(p))
+
+        print(f"  Saved {len(paths)} images and zipped to {zip_path}")
+
+    print("\nAll done.")
+
+
+if __name__ == "__main__":
+    main()
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diff --git a/src/Prototypes/Computer Vision/Final/Inference/bird_classifier_int8a.tflite b/src/Prototypes/Computer Vision/Final/Inference/bird_classifier_int8a.tflite
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diff --git a/src/Prototypes/Computer Vision/Final/Inference/class_names.csv b/src/Prototypes/Computer Vision/Final/Inference/class_names.csv
new file mode 100644
index 000000000..19cd76f83
--- /dev/null
+++ b/src/Prototypes/Computer Vision/Final/Inference/class_names.csv	
@@ -0,0 +1,50 @@
+Aquila_audax_300x300
+Aythya australi_300x300
+Burhinus grallarius_300x300
+Cacatua_galerita_300x300
+Eolophus roseicapilla_300x300
+acanthagenys_rufogularis_300x300a
+acanthiza_apicalis_300x300a
+acanthiza_chrysorrhoa_300x300a
+acanthiza_lineata_300x300a
+acanthiza_nana_300x300a
+acanthiza_pusilla_300x300a
+acanthiza_uropygialis_300x300a
+acanthorhynchus_tenuirostris_300x300a
+accipiter_cooperi_300x300a
+accipiter_fasciatus_300x300a
+acrocephalus_australis_300x300a
+alisterus_scapularis_300x300a
+anas_superciliosa_300x300a
+anhinga_novaehollandiae_300x300a
+anseranas_semipalmata_300x300a
+anthochaera_carunculata_300x300a
+anthus_novaeseelandiae_300x300a
+antigone_antigone_300x300a
+antigone_rubicunda_300x300a
+aprosmictus_erythropterus_300x300a
+artamus_cinereus_300x300a
+artamus_cyanopterus_300x300a
+artamus_minor_300x300a
+artamus_superciliosus_300x300a
+avesâ_sp._300x300a
+barnardius_zonarius_300x300a
+biziura_lobata_300x300a
+cacatua_sanguinea_300x300a
+cacomantis_flabelliformis_300x300a
+caligavis_chrysops_300x300a
+callocephalon_fimbriatum_300x300a
+calyptorhynchus_banksii_300x300a
+carduelis_carduelis_300x300a
+carterornis_leucotis_300x300a
+centropus_phasianinus_300x300a
+ceyx_azureus_300x300a
+coracina_novaehollandiae_300x300a
+coracina_papuensis_300x300a
+corcorax_melanorhamphos_300x300a
+cormobates_leucophaea_300x300a
+corvus_orru_300x300a
+coturnix_pectoralis_300x300a
+cracticus_nigrogularis_300x300a
+cracticus_torquatus_300x300a
+zosterops_lateralis_300x300a
diff --git a/src/Prototypes/Computer Vision/Final/Inference/classify_stills_v2.py b/src/Prototypes/Computer Vision/Final/Inference/classify_stills_v2.py
new file mode 100644
index 000000000..efa2c99d7
--- /dev/null
+++ b/src/Prototypes/Computer Vision/Final/Inference/classify_stills_v2.py	
@@ -0,0 +1,65 @@
+# -*- coding: utf-8 -*-
+"""Inference_Birds.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1zOszzPc19vuHCzrqwFrG5bteqImxYzgG
+"""
+
+import os
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+import csv
+from google.colab import drive
+
+drive.mount('/content/drive')
+
+# ---------------- CONFIG ----------------
+MODEL_PATH = "/content/drive/MyDrive/Birds_Dataset3_model/bird_classifier_float_v1.tflite"
+CSV_PATH = "/content/drive/MyDrive/Birds_Dataset3_model/class_names.csv"
+TEST_DIR = "/content/drive/MyDrive/Birds_Dataset3_model/Testing"
+IMG_SIZE = 300
+
+# ---------------- LOAD MODEL ----------------
+interpreter = tf.lite.Interpreter(model_path=MODEL_PATH)
+interpreter.allocate_tensors()
+
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+input_dtype = input_details[0]["dtype"]
+print("Model input:", input_dtype)
+
+# ---------------- LOAD CLASSES ----------------
+class_names = []
+with open(CSV_PATH, "r") as f:
+    for row in csv.reader(f):
+        if row:
+            class_names.append(row[0])
+
+# ---------------- PREPROCESS ----------------
+def preprocess(path):
+    img = Image.open(path).convert("RGB")
+    img = img.resize((IMG_SIZE, IMG_SIZE))
+    img = np.array(img, dtype=np.float32)  # 0–255
+    return np.expand_dims(img, axis=0)
+
+
+# ---------------- INFERENCE ----------------
+for file in sorted(os.listdir(TEST_DIR)):
+    if not file.lower().endswith(("jpg", "png", "jpeg")):
+        continue
+
+    path = os.path.join(TEST_DIR, file)
+    input_data = preprocess(path)
+
+    interpreter.set_tensor(input_details[0]["index"], input_data)
+    interpreter.invoke()
+
+    output = interpreter.get_tensor(output_details[0]["index"])[0]
+    idx = np.argmax(output)
+    conf = output[idx]
+
+    print(f"{file} → {class_names[idx]} ({conf*100:.1f}%)")
\ No newline at end of file
diff --git a/src/Prototypes/Computer Vision/Final/Inference/classify_video_v2.py b/src/Prototypes/Computer Vision/Final/Inference/classify_video_v2.py
new file mode 100644
index 000000000..ffae8f293
--- /dev/null
+++ b/src/Prototypes/Computer Vision/Final/Inference/classify_video_v2.py	
@@ -0,0 +1,184 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Dec 30 11:46:38 2025
+
+@author: james
+"""
+
+import os
+import csv
+import re
+import cv2
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+from datetime import datetime
+
+# ---------------- CONFIG ----------------
+MODEL_PATH = r"C:\Users\james\.spyder-py3\bird_classifier_float.tflite"
+CSV_PATH   = r"C:\Users\james\.spyder-py3\class_names.csv"
+VIDEO_PATH = r"C:\Users\james\.spyder-py3\birds.mp4"
+
+IMG_SIZE = 300
+
+FRAME_STRIDE = 36
+DISPLAY_SECONDS = 2.0
+WINDOW_NAME = "Bird Classifier (TFLite)"
+
+ACCURACY_THRESHOLD = 60.0   # percent (e.g. 70.0 = only show/save ≥70%)
+
+OUTPUT_DIR = r"C:\Users\james\.spyder-py3"
+VIDEO_SAVE_DIR = r"C:\temp"  # <-- save annotated video copy here
+
+TIMESTAMP = datetime.now().strftime("%Y%m%d_%H%M%S")
+PRED_CSV_PATH = os.path.join(OUTPUT_DIR, f"video_predictions_{TIMESTAMP}.csv")
+OUT_VIDEO_PATH = os.path.join(VIDEO_SAVE_DIR, f"annotated_video_{TIMESTAMP}.mp4")
+
+# ---------------- LOAD MODEL ----------------
+interpreter = tf.lite.Interpreter(model_path=MODEL_PATH)
+interpreter.allocate_tensors()
+
+input_details  = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+in_index  = input_details[0]["index"]
+out_index = output_details[0]["index"]
+
+# ---------------- LOAD CLASSES ----------------
+class_names = []
+with open(CSV_PATH, "r", newline="", encoding="utf-8") as f:
+    for row in csv.reader(f):
+        if row:
+            class_names.append(row[0])
+
+# ---------------- HELPERS ----------------
+def preprocess_bgr_frame(frame_bgr: np.ndarray) -> np.ndarray:
+    frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
+    img = Image.fromarray(frame_rgb).resize((IMG_SIZE, IMG_SIZE))
+    x = np.array(img, dtype=np.float32)
+    return np.expand_dims(x, axis=0)
+
+def softmax_if_needed(raw: np.ndarray) -> np.ndarray:
+    raw = raw.astype(np.float32)
+    s = float(raw.sum())
+    if raw.min() >= 0.0 and raw.max() <= 1.0 and 0.95 <= s <= 1.05:
+        return raw
+    e = np.exp(raw - np.max(raw))
+    return e / np.sum(e)
+
+def clean_label(label: str) -> str:
+    # Remove trailing image-size suffixes like _300x300, _300x300a, etc.
+    return re.sub(r"_\d+x\d+[a-zA-Z]*$", "", label)
+
+
+def draw_highlighted_text(
+    img: np.ndarray,
+    text: str,
+    org: tuple,
+    font=cv2.FONT_HERSHEY_SIMPLEX,
+    font_scale: float = 1.0,
+    thickness: int = 2,
+    pad: int = 8,
+):
+    (tw, th), baseline = cv2.getTextSize(text, font, font_scale, thickness)
+    x, y = org
+    x0 = x - pad
+    y0 = y - th - pad
+    x1 = x + tw + pad
+    y1 = y + baseline + pad
+
+    x0 = max(0, x0)
+    y0 = max(0, y0)
+    x1 = min(img.shape[1] - 1, x1)
+    y1 = min(img.shape[0] - 1, y1)
+
+    cv2.rectangle(img, (x0, y0), (x1, y1), (0, 255, 0), -1)  # green highlight
+    cv2.putText(img, text, (x, y), font, font_scale, (0, 0, 0), thickness, cv2.LINE_AA)  # black text
+
+# ---------------- VIDEO ----------------
+os.makedirs(VIDEO_SAVE_DIR, exist_ok=True)
+
+cap = cv2.VideoCapture(VIDEO_PATH)
+if not cap.isOpened():
+    raise FileNotFoundError(f"Could not open video: {VIDEO_PATH}")
+
+fps = cap.get(cv2.CAP_PROP_FPS) or 30.0
+w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH) or 0)
+h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT) or 0)
+
+writer_video = None
+
+frame_idx = -1
+last_text = ""
+last_text_until_sec = -1.0
+
+with open(PRED_CSV_PATH, "w", newline="", encoding="utf-8") as fcsv:
+    writer = csv.writer(fcsv)
+    writer.writerow([
+        "run_timestamp",
+        "video_path",
+        "frame_index",
+        "video_time_sec",
+        "label",
+        "prob_percent"
+    ])
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        if writer_video is None:
+            if w <= 0 or h <= 0:
+                h, w = frame.shape[:2]
+            fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+            writer_video = cv2.VideoWriter(OUT_VIDEO_PATH, fourcc, float(fps), (w, h))
+
+        frame_idx += 1
+        video_time_sec = frame_idx / fps
+
+        if FRAME_STRIDE > 0 and frame_idx % FRAME_STRIDE == 0:
+            input_data = preprocess_bgr_frame(frame)
+
+            interpreter.set_tensor(in_index, input_data)
+            interpreter.invoke()
+
+            output = interpreter.get_tensor(out_index)[0]
+            probs = softmax_if_needed(output)
+
+            idx = int(np.argmax(probs))
+            prob = float(probs[idx]) * 100.0
+
+            if prob >= ACCURACY_THRESHOLD:
+                raw_label = class_names[idx] if idx < len(class_names) else str(idx)
+                label = clean_label(raw_label)
+
+                last_text = f"{label}  {prob:.1f}%"
+                last_text_until_sec = video_time_sec + DISPLAY_SECONDS
+
+                writer.writerow([
+                    TIMESTAMP,
+                    VIDEO_PATH,
+                    frame_idx,
+                    f"{video_time_sec:.3f}",
+                    label,
+                    f"{prob:.2f}"
+                ])
+
+        if video_time_sec <= last_text_until_sec and last_text:
+            draw_highlighted_text(frame, last_text, (20, 50))
+
+        cv2.imshow(WINDOW_NAME, frame)
+        if writer_video is not None:
+            writer_video.write(frame)
+
+        if (cv2.waitKey(1) & 0xFF) in (ord("q"), 27):
+            break
+
+cap.release()
+if writer_video is not None:
+    writer_video.release()
+cv2.destroyAllWindows()
+
+print("Predictions saved to:", PRED_CSV_PATH)
+print("Annotated video saved to:", OUT_VIDEO_PATH)
diff --git a/src/Prototypes/Computer Vision/Inference/requirements.txt b/src/Prototypes/Computer Vision/Final/Inference/requirements.txt
similarity index 100%
rename from src/Prototypes/Computer Vision/Inference/requirements.txt
rename to src/Prototypes/Computer Vision/Final/Inference/requirements.txt
diff --git a/src/Prototypes/Computer Vision/Final/Train Model/class_names.csv b/src/Prototypes/Computer Vision/Final/Train Model/class_names.csv
new file mode 100644
index 000000000..93881d4fc
--- /dev/null
+++ b/src/Prototypes/Computer Vision/Final/Train Model/class_names.csv	
@@ -0,0 +1,51 @@
+Aquila_audax_300x300
+Aythya australi_300x300
+Burhinus grallarius_300x300
+Cacatua_galerita_300x300
+Eolophus roseicapilla_300x300
+acanthagenys_rufogularis_300x300a
+acanthiza_apicalis_300x300a
+acanthiza_chrysorrhoa_300x300a
+acanthiza_lineata_300x300a
+acanthiza_nana_300x300a
+acanthiza_pusilla_300x300a
+acanthiza_uropygialis_300x300a
+acanthorhynchus_tenuirostris_300x300a
+accipiter_cooperi_300x300a
+accipiter_fasciatus_300x300a
+acrocephalus_australis_300x300a
+alisterus_scapularis_300x300a
+anas_superciliosa_300x300a
+anhinga_novaehollandiae_300x300a
+anseranas_semipalmata_300x300a
+anthochaera_carunculata_300x300a
+anthus_novaeseelandiae_300x300a
+antigone_antigone_300x300a
+antigone_rubicunda_300x300a
+aprosmictus_erythropterus_300x300a
+artamus_cinereus_300x300a
+artamus_cyanopterus_300x300a
+artamus_minor_300x300a
+artamus_superciliosus_300x300a
+avesâ_sp._300x300a
+barnardius_zonarius_300x300a
+biziura_lobata_300x300a
+cacatua_sanguinea_300x300a
+cacomantis_flabelliformis_300x300a
+caligavis_chrysops_300x300a
+callocephalon_fimbriatum_300x300a
+calyptorhynchus_banksii_300x300a
+carduelis_carduelis_300x300a
+carterornis_leucotis_300x300a
+centropus_phasianinus_300x300a
+ceyx_azureus_300x300a
+coracina_novaehollandiae_300x300a
+coracina_papuensis_300x300a
+corcorax_melanorhamphos_300x300a
+cormobates_leucophaea_300x300a
+corvus_orru_300x300a
+coturnix_pectoralis_300x300a
+cracticus_nigrogularis_300x300a
+cracticus_torquatus_300x300a
+zosterops_lateralis_300x300a
+
diff --git a/src/Prototypes/Computer Vision/Train Model/requirements.txt b/src/Prototypes/Computer Vision/Final/Train Model/requirements.txt
similarity index 100%
rename from src/Prototypes/Computer Vision/Train Model/requirements.txt
rename to src/Prototypes/Computer Vision/Final/Train Model/requirements.txt
diff --git a/src/Prototypes/Computer Vision/Final/Train Model/train_model_v2.ipynb b/src/Prototypes/Computer Vision/Final/Train Model/train_model_v2.ipynb
new file mode 100644
index 000000000..769db8ecb
--- /dev/null
+++ b/src/Prototypes/Computer Vision/Final/Train Model/train_model_v2.ipynb	
@@ -0,0 +1,622 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "dutLD5cb0ACe",
+        "outputId": "68d74b89-b9b4-4d27-d153-2e39f8f44d5e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
+            "Found 24000 files belonging to 50 classes.\n",
+            "Using 19200 files for training.\n",
+            "Found 24000 files belonging to 50 classes.\n",
+            "Using 4800 files for validation.\n",
+            "Classes: 50\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipython-input-3711385349.py:72: UserWarning: `input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n",
+            "  base = tf.keras.applications.MobileNetV2(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/18\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m176s\u001b[0m 403ms/step - accuracy: 0.0555 - loss: 3.8840 - val_accuracy: 0.3565 - val_loss: 3.0886\n",
+            "Epoch 2/18\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 43ms/step - accuracy: 0.3132 - loss: 2.9720 - val_accuracy: 0.5775 - val_loss: 2.4408\n",
+            "Epoch 3/18\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 43ms/step - accuracy: 0.5119 - loss: 2.3604 - val_accuracy: 0.6771 - val_loss: 1.9972\n",
+            "Epoch 4/18\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 43ms/step - accuracy: 0.6178 - loss: 1.9539 - val_accuracy: 0.7267 - val_loss: 1.6882\n",
+            "Epoch 5/18\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 43ms/step - accuracy: 0.6798 - loss: 1.6552 - val_accuracy: 0.7604 - val_loss: 1.4625\n",
+            "Epoch 6/18\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 43ms/step - accuracy: 0.7247 - loss: 1.4460 - val_accuracy: 0.7854 - val_loss: 1.2917\n",
+            "Epoch 7/18\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 43ms/step - accuracy: 0.7534 - loss: 1.2850 - val_accuracy: 0.8017 - val_loss: 1.1580\n",
+            "Epoch 8/18\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 43ms/step - accuracy: 0.7789 - loss: 1.1576 - val_accuracy: 0.8171 - val_loss: 1.0495\n",
+            "Epoch 9/18\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 43ms/step - accuracy: 0.7990 - loss: 1.0462 - val_accuracy: 0.8300 - val_loss: 0.9608\n",
+            "Epoch 10/18\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 43ms/step - accuracy: 0.8133 - loss: 0.9625 - val_accuracy: 0.8417 - val_loss: 0.8867\n",
+            "Epoch 11/18\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 43ms/step - accuracy: 0.8262 - loss: 0.8937 - val_accuracy: 0.8502 - val_loss: 0.8226\n",
+            "Epoch 12/18\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 43ms/step - accuracy: 0.8409 - loss: 0.8268 - val_accuracy: 0.8583 - val_loss: 0.7677\n",
+            "Epoch 13/18\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 43ms/step - accuracy: 0.8480 - loss: 0.7710 - val_accuracy: 0.8679 - val_loss: 0.7197\n",
+            "Epoch 14/18\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 43ms/step - accuracy: 0.8611 - loss: 0.7173 - val_accuracy: 0.8758 - val_loss: 0.6776\n",
+            "Epoch 15/18\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 43ms/step - accuracy: 0.8694 - loss: 0.6810 - val_accuracy: 0.8840 - val_loss: 0.6400\n",
+            "Epoch 16/18\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 43ms/step - accuracy: 0.8710 - loss: 0.6489 - val_accuracy: 0.8913 - val_loss: 0.6063\n",
+            "Epoch 17/18\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 43ms/step - accuracy: 0.8808 - loss: 0.6092 - val_accuracy: 0.8967 - val_loss: 0.5760\n",
+            "Epoch 18/18\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 43ms/step - accuracy: 0.8887 - loss: 0.5796 - val_accuracy: 0.9019 - val_loss: 0.5493\n",
+            "Epoch 19/26\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m50s\u001b[0m 126ms/step - accuracy: 0.7764 - loss: 0.8154 - val_accuracy: 0.8877 - val_loss: 0.4437\n",
+            "Epoch 20/26\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 47ms/step - accuracy: 0.8958 - loss: 0.4121 - val_accuracy: 0.9085 - val_loss: 0.3493\n",
+            "Epoch 21/26\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 47ms/step - accuracy: 0.9297 - loss: 0.3014 - val_accuracy: 0.9296 - val_loss: 0.2843\n",
+            "Epoch 22/26\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 47ms/step - accuracy: 0.9484 - loss: 0.2384 - val_accuracy: 0.9446 - val_loss: 0.2343\n",
+            "Epoch 23/26\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 47ms/step - accuracy: 0.9612 - loss: 0.1917 - val_accuracy: 0.9552 - val_loss: 0.1960\n",
+            "Epoch 24/26\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 48ms/step - accuracy: 0.9712 - loss: 0.1525 - val_accuracy: 0.9665 - val_loss: 0.1673\n",
+            "Epoch 25/26\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 47ms/step - accuracy: 0.9798 - loss: 0.1270 - val_accuracy: 0.9729 - val_loss: 0.1462\n",
+            "Epoch 26/26\n",
+            "\u001b[1m247/247\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 47ms/step - accuracy: 0.9841 - loss: 0.1057 - val_accuracy: 0.9762 - val_loss: 0.1284\n",
+            "Training time (stage 1): 0:05:55 (355.6 s)\n",
+            "Training time (stage 2): 0:02:12 (132.3 s)\n",
+            "Training time (total):   0:08:07 (487.8 s)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saved accuracy chart: /content/drive/MyDrive/Birds_Dataset3_model/training_validation_accuracy.png\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saved loss chart: /content/drive/MyDrive/Birds_Dataset3_model/training_validation_loss.png\n",
+            "Saved artifact at '/content/drive/MyDrive/Birds_Dataset3_model'. The following endpoints are available:\n",
+            "\n",
+            "* Endpoint 'serve'\n",
+            "  args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 300, 300, 3), dtype=tf.float32, name='keras_tensor_158')\n",
+            "Output Type:\n",
+            "  TensorSpec(shape=(None, 50), dtype=tf.float32, name=None)\n",
+            "Captures:\n",
+            "  134973978813136: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
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+            "  134970941541840: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
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+            "  134970941538768: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
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+            "  134970941542800: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
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+            "  134970941543568: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
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+            "  134970891831248: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  134970891829712: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  134970891830864: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  134970891831056: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  134970891829328: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  134970891832208: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  134970891831632: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  134970891831824: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  134970891832016: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  134970891829904: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  134970891833168: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  134970891833552: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "  134970891833360: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
+            "SavedModel exported to: /content/drive/MyDrive/Birds_Dataset3_model\n",
+            "Saved FLOAT TFLite: /content/drive/MyDrive/Birds_Dataset3_model/bird_classifier_float.tflite\n"
+          ]
+        }
+      ],
+      "source": [
+        "# -*- coding: utf-8 -*-\n",
+        "import os\n",
+        "import time\n",
+        "import csv\n",
+        "from datetime import timedelta\n",
+        "\n",
+        "import tensorflow as tf\n",
+        "import matplotlib.pyplot as plt\n",
+        "from google.colab import drive\n",
+        "\n",
+        "drive.mount('/content/drive')\n",
+        "\n",
+        "# ---------------- CONFIG ----------------\n",
+        "DATA_DIR = \"/content/drive/MyDrive/Birds_Dataset3\"\n",
+        "EXPORT_DIR = \"/content/drive/MyDrive/Birds_Dataset3_model\"\n",
+        "CSV_OUTPUT = f\"{EXPORT_DIR}/class_names.csv\"\n",
+        "\n",
+        "IMG_SIZE = 300\n",
+        "BATCH_SIZE = 78 #64\n",
+        "AUTOTUNE = tf.data.AUTOTUNE\n",
+        "\n",
+        "INITIAL_EPOCHS = 18 #10\n",
+        "FINE_TUNE_EPOCHS = 8 #5\n",
+        "TOTAL_EPOCHS = INITIAL_EPOCHS + FINE_TUNE_EPOCHS\n",
+        "\n",
+        "os.makedirs(EXPORT_DIR, exist_ok=True)\n",
+        "\n",
+        "# ---------------- LOAD DATA ----------------\n",
+        "train_ds = tf.keras.utils.image_dataset_from_directory(\n",
+        "    DATA_DIR,\n",
+        "    labels=\"inferred\",\n",
+        "    label_mode=\"categorical\",\n",
+        "    validation_split=0.2,\n",
+        "    subset=\"training\",\n",
+        "    seed=42,\n",
+        "    image_size=(IMG_SIZE, IMG_SIZE),\n",
+        "    batch_size=BATCH_SIZE,\n",
+        ")\n",
+        "\n",
+        "val_ds = tf.keras.utils.image_dataset_from_directory(\n",
+        "    DATA_DIR,\n",
+        "    labels=\"inferred\",\n",
+        "    label_mode=\"categorical\",\n",
+        "    validation_split=0.2,\n",
+        "    subset=\"validation\",\n",
+        "    seed=42,\n",
+        "    image_size=(IMG_SIZE, IMG_SIZE),\n",
+        "    batch_size=BATCH_SIZE,\n",
+        ")\n",
+        "\n",
+        "class_names = train_ds.class_names\n",
+        "num_classes = len(class_names)\n",
+        "print(\"Classes:\", num_classes)\n",
+        "\n",
+        "# ---------------- PREPROCESS / AUGMENTATION ----------------\n",
+        "data_augmentation = tf.keras.Sequential([\n",
+        "    tf.keras.layers.RandomFlip(\"horizontal\"),\n",
+        "    tf.keras.layers.RandomRotation(0.05),\n",
+        "    tf.keras.layers.RandomZoom(0.1),\n",
+        "])\n",
+        "\n",
+        "def prep(ds, aug=False):\n",
+        "    ds = ds.map(lambda x, y: (tf.cast(x, tf.float32), y), num_parallel_calls=AUTOTUNE)\n",
+        "    if aug:\n",
+        "        ds = ds.map(lambda x, y: (data_augmentation(x, training=True), y), num_parallel_calls=AUTOTUNE)\n",
+        "    return ds.cache().prefetch(AUTOTUNE)\n",
+        "\n",
+        "train_ds = prep(train_ds.shuffle(1000), aug=True)\n",
+        "val_ds = prep(val_ds, aug=False)\n",
+        "\n",
+        "# ---------------- BUILD MODEL ----------------\n",
+        "base = tf.keras.applications.MobileNetV2(\n",
+        "    input_shape=(IMG_SIZE, IMG_SIZE, 3),\n",
+        "    include_top=False,\n",
+        "    weights=\"imagenet\",\n",
+        ")\n",
+        "base.trainable = False\n",
+        "\n",
+        "inputs = tf.keras.Input(shape=(IMG_SIZE, IMG_SIZE, 3))\n",
+        "x = tf.keras.applications.mobilenet_v2.preprocess_input(inputs)\n",
+        "x = base(x, training=False)\n",
+        "x = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
+        "x = tf.keras.layers.Dropout(0.3)(x)\n",
+        "outputs = tf.keras.layers.Dense(num_classes, activation=\"softmax\")(x)\n",
+        "model = tf.keras.Model(inputs, outputs)\n",
+        "\n",
+        "model.compile(\n",
+        "    optimizer=tf.keras.optimizers.Adam(1e-4),\n",
+        "    loss=\"categorical_crossentropy\",\n",
+        "    metrics=[\"accuracy\"],\n",
+        ")\n",
+        "\n",
+        "# ---------------- TRAIN (TIMED) ----------------\n",
+        "total_start = time.perf_counter()\n",
+        "\n",
+        "stage1_start = time.perf_counter()\n",
+        "history1 = model.fit(train_ds, epochs=INITIAL_EPOCHS, validation_data=val_ds)\n",
+        "stage1_end = time.perf_counter()\n",
+        "\n",
+        "# ---------------- FINE TUNE ----------------\n",
+        "base.trainable = True\n",
+        "fine_tune_at = len(base.layers) - 30\n",
+        "for layer in base.layers[:fine_tune_at]:\n",
+        "    layer.trainable = False\n",
+        "\n",
+        "model.compile(\n",
+        "    optimizer=tf.keras.optimizers.Adam(1e-5),\n",
+        "    loss=\"categorical_crossentropy\",\n",
+        "    metrics=[\"accuracy\"],\n",
+        ")\n",
+        "\n",
+        "stage2_start = time.perf_counter()\n",
+        "history2 = model.fit(\n",
+        "    train_ds,\n",
+        "    initial_epoch=INITIAL_EPOCHS,\n",
+        "    epochs=TOTAL_EPOCHS,\n",
+        "    validation_data=val_ds\n",
+        ")\n",
+        "stage2_end = time.perf_counter()\n",
+        "\n",
+        "total_end = time.perf_counter()\n",
+        "\n",
+        "stage1_s = stage1_end - stage1_start\n",
+        "stage2_s = stage2_end - stage2_start\n",
+        "total_s = total_end - total_start\n",
+        "\n",
+        "print(\"Training time (stage 1):\", str(timedelta(seconds=int(stage1_s))), f\"({stage1_s:.1f} s)\")\n",
+        "print(\"Training time (stage 2):\", str(timedelta(seconds=int(stage2_s))), f\"({stage2_s:.1f} s)\")\n",
+        "print(\"Training time (total):  \", str(timedelta(seconds=int(total_s))), f\"({total_s:.1f} s)\")\n",
+        "\n",
+        "# ---------------- PLOT TRAINING CURVES ----------------\n",
+        "acc = history1.history.get(\"accuracy\", []) + history2.history.get(\"accuracy\", [])\n",
+        "val_acc = history1.history.get(\"val_accuracy\", []) + history2.history.get(\"val_accuracy\", [])\n",
+        "loss = history1.history.get(\"loss\", []) + history2.history.get(\"loss\", [])\n",
+        "val_loss = history1.history.get(\"val_loss\", []) + history2.history.get(\"val_loss\", [])\n",
+        "\n",
+        "epochs_range = range(1, len(acc) + 1)\n",
+        "\n",
+        "plt.figure(figsize=(10, 5))\n",
+        "plt.plot(epochs_range, acc, label=\"Training Accuracy\")\n",
+        "plt.plot(epochs_range, val_acc, label=\"Validation Accuracy\")\n",
+        "plt.xlabel(\"Epoch\")\n",
+        "plt.ylabel(\"Accuracy\")\n",
+        "plt.title(\"Training vs Validation Accuracy\")\n",
+        "plt.legend()\n",
+        "acc_chart_path = f\"{EXPORT_DIR}/training_validation_accuracy.png\"\n",
+        "plt.savefig(acc_chart_path, dpi=150, bbox_inches=\"tight\")\n",
+        "plt.show()\n",
+        "print(\"Saved accuracy chart:\", acc_chart_path)\n",
+        "\n",
+        "plt.figure(figsize=(10, 5))\n",
+        "plt.plot(epochs_range, loss, label=\"Training Loss\")\n",
+        "plt.plot(epochs_range, val_loss, label=\"Validation Loss\")\n",
+        "plt.xlabel(\"Epoch\")\n",
+        "plt.ylabel(\"Loss\")\n",
+        "plt.title(\"Training vs Validation Loss\")\n",
+        "plt.legend()\n",
+        "loss_chart_path = f\"{EXPORT_DIR}/training_validation_loss.png\"\n",
+        "plt.savefig(loss_chart_path, dpi=150, bbox_inches=\"tight\")\n",
+        "plt.show()\n",
+        "print(\"Saved loss chart:\", loss_chart_path)\n",
+        "\n",
+        "# ---------------- EXPORT KERAS MODEL ----------------\n",
+        "model.export(EXPORT_DIR)\n",
+        "print(\"SavedModel exported to:\", EXPORT_DIR)\n",
+        "\n",
+        "# ---------------- EXPORT FLOAT TFLITE (BEST QUALITY) ----------------\n",
+        "converter = tf.lite.TFLiteConverter.from_saved_model(EXPORT_DIR)\n",
+        "tflite_float = converter.convert()\n",
+        "\n",
+        "float_path = f\"{EXPORT_DIR}/bird_classifier_float.tflite\"\n",
+        "with open(float_path, \"wb\") as f:\n",
+        "    f.write(tflite_float)\n",
+        "print(\"Saved FLOAT TFLite:\", float_path)\n",
+        "\n",
+        "# ---------------- INT8 QUANTIZATION WITH REPRESENTATIVE DATA ----------------\n",
+        "def rep_data_gen():\n",
+        "    for images, _ in train_ds.take(100):\n",
+        "        yield [images]\n",
+        "\n",
+        "converter = tf.lite.TFLiteConverter.from_saved_model(EXPORT_DIR)\n",
+        "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
+        "converter.representative_dataset = rep_data_gen\n",
+        "converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\n",
+        "converter.inference_input_type = tf.uint8\n",
+        "converter.inference_output_type = tf.uint8\n",
+        "\n",
+        "tflite_int8 = converter.convert()\n",
+        "\n",
+        "int8_path = f\"{EXPORT_DIR}/bird_classifier_int8.tflite\"\n",
+        "with open(int8_path, \"wb\") as f:\n",
+        "    f.write(tflite_int8)\n",
+        "print(\"Saved INT8 TFLite:\", int8_path)\n",
+        "\n",
+        "# ---------------- SAVE CLASS NAMES ----------------\n",
+        "with open(CSV_OUTPUT, \"w\", newline=\"\") as f:\n",
+        "    writer = csv.writer(f)\n",
+        "    for name in class_names:\n",
+        "        writer.writerow([name])\n",
+        "print(\"Saved classes:\", CSV_OUTPUT)\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "A100",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/src/Prototypes/Computer Vision/Final/reademe.md b/src/Prototypes/Computer Vision/Final/reademe.md
new file mode 100644
index 000000000..be273a3b8
--- /dev/null
+++ b/src/Prototypes/Computer Vision/Final/reademe.md	
@@ -0,0 +1,135 @@
+# Bird / Creature Image Classification Pipeline
+
+This repository contains a full end-to-end workflow for building an image-classification model for creatures (e.g., birds). It is organised into **three folders**, each representing a major stage in the pipeline:
+
+1. **Create Dataset** � Automatically collect and augment creature images  
+2. **Train Model** � Train a TensorFlow classifier and export a quantized TFLite model  
+3. **Inference** � Run classification on images or video using the exported model  
+
+---
+
+# 1. Folder Structure
+
+project/
+�
++-- Create Dataset/
+�   +-- get_creatures_v2.py
+�   +-- augment_creatures_v2.py
+�   +-- creatures.json
+�   +-- How to Create A Creatures Dataset_v2.docx
+�
++-- Train Model/
+� 	+-- train_model_v1.py
+�   +-- requirements.txt
+�   +-- class_names.csv
+�
+�
++-- Inference/
+    +-- classify_video_v1.py
+    +-- classify_stills_v1.py
+	+-- requirements.txt
+    +-- class_names.csv
+
+---
+
+# 2. CREATE DATASET
+
+## 2.1 get_creatures_v2.py � Download & standardise images
+
+Run locally - Reads creatures.json and for each class, downloads images from Wikimedia Commons, converts them to 300�300 RGB, and outputs folder with class name.
+
+Run:
+```
+python get_creatures_v2.py
+```
+
+Copy folders to Google Drive.
+---
+
+## 2.2 augment_creatures_v1.py � Dataset expansion
+
+Creates 500 augmented images for each class using flips, rotation, blur, noise and colour shifts.
+
+Run script in Google Colab,
+---
+
+## 2.3 Documentation
+
+�How to Create A Creatures Dataset_v1.docx�  
+Explains dataset creation in non-technical language.
+
+---
+
+# 3. TRAIN MODEL
+
+## train_model_v1.ipynb � Train MobileNetV2 + export TFLite
+
+Trains a deep-learning classifier in Google Colab and exports:
+
+- TensorFlow SavedModel  
+- Quantized TFLite model  
+- Accuracy/loss plots
+- saves class names in training order to class_names.csv - this is essential for inference.
+
+---
+
+# 4. INFERENCE
+
+## classify_stills_v2.py � Video classification
+
+Loads the TFLite model and runs classification on still images. Class_names.csv is required for correct output of class names.
+
+Run:
+```
+python classify_video_v2.py
+```
+
+Press **Q** to quit window.
+
+---
+
+## classify_video_v2.py � Video classification
+
+Loads the TFLite model and runs classification on frames from a video file or camera feed.  Class_names.csv is required for correct output of class names.
+
+Run:
+```
+python classify_video_v2.py
+```
+
+Press **Q** to quit window.
+# 5. Pipeline Summary
+
+STEP 1 � Dataset  
+- Run get_creatures  
+- Run augment_creatures  
+
+STEP 2 � Training  
+- Run train_model  
+
+STEP 3 � Inference  
+- Update MODEL_PATH  
+- Run classify_video or Classify_stills 
+
+---
+
+# 6. Notes
+
+- Class_names.csv (Created from training script) is required for inference. 
+- training 50 classes on Google Colab with A100 GPUs takes < 10 mins
+- TFLite is for inference only  
+
+---
+
+# 7. Troubleshooting
+
+Wrong predictions ? class order mismatch  
+Video slow ? increase FRAME_STRIDE  
+�No images found� ? check folder paths
+
+---
+
+# 8. Credits
+
+Scripts by James Blair (2026).  
+Images from Wikimedia Commons (licensing varies).
diff --git a/src/Prototypes/Computer Vision/Create Dataset/How to Create A Creatures Dataset_v1.docx b/src/Prototypes/Computer Vision/POC/Create Dataset/How to Create A Creatures Dataset_v1.docx
similarity index 100%
rename from src/Prototypes/Computer Vision/Create Dataset/How to Create A Creatures Dataset_v1.docx
rename to src/Prototypes/Computer Vision/POC/Create Dataset/How to Create A Creatures Dataset_v1.docx
diff --git a/src/Prototypes/Computer Vision/Create Dataset/augment_creatures_v1.py b/src/Prototypes/Computer Vision/POC/Create Dataset/augment_creatures_v1.py
similarity index 100%
rename from src/Prototypes/Computer Vision/Create Dataset/augment_creatures_v1.py
rename to src/Prototypes/Computer Vision/POC/Create Dataset/augment_creatures_v1.py
diff --git a/src/Prototypes/Computer Vision/Create Dataset/get_creatures_v1.py b/src/Prototypes/Computer Vision/POC/Create Dataset/get_creatures_v1.py
similarity index 100%
rename from src/Prototypes/Computer Vision/Create Dataset/get_creatures_v1.py
rename to src/Prototypes/Computer Vision/POC/Create Dataset/get_creatures_v1.py
diff --git a/src/Prototypes/Computer Vision/Inference/classify_stills_v1.py b/src/Prototypes/Computer Vision/POC/Inference/classify_stills_v1.py
similarity index 100%
rename from src/Prototypes/Computer Vision/Inference/classify_stills_v1.py
rename to src/Prototypes/Computer Vision/POC/Inference/classify_stills_v1.py
diff --git a/src/Prototypes/Computer Vision/Inference/classify_video_v1.py b/src/Prototypes/Computer Vision/POC/Inference/classify_video_v1.py
similarity index 100%
rename from src/Prototypes/Computer Vision/Inference/classify_video_v1.py
rename to src/Prototypes/Computer Vision/POC/Inference/classify_video_v1.py
diff --git a/src/Prototypes/Computer Vision/POC/Inference/requirements.txt b/src/Prototypes/Computer Vision/POC/Inference/requirements.txt
new file mode 100644
index 000000000..7f1336828
--- /dev/null
+++ b/src/Prototypes/Computer Vision/POC/Inference/requirements.txt	
@@ -0,0 +1,4 @@
+tensorflow==2.12.0 
+numpy==1.23.5 
+opencv-python==4.8.1.78 
+Pillow==10.2.0 
\ No newline at end of file
diff --git a/src/Prototypes/Computer Vision/Train Model/Computer Vision on Edge Devices_v1.docx b/src/Prototypes/Computer Vision/POC/Train Model/Computer Vision on Edge Devices_v1.docx
similarity index 100%
rename from src/Prototypes/Computer Vision/Train Model/Computer Vision on Edge Devices_v1.docx
rename to src/Prototypes/Computer Vision/POC/Train Model/Computer Vision on Edge Devices_v1.docx
diff --git a/src/Prototypes/Computer Vision/POC/Train Model/requirements.txt b/src/Prototypes/Computer Vision/POC/Train Model/requirements.txt
new file mode 100644
index 000000000..7f1336828
--- /dev/null
+++ b/src/Prototypes/Computer Vision/POC/Train Model/requirements.txt	
@@ -0,0 +1,4 @@
+tensorflow==2.12.0 
+numpy==1.23.5 
+opencv-python==4.8.1.78 
+Pillow==10.2.0 
\ No newline at end of file
diff --git a/src/Prototypes/Computer Vision/Train Model/train_model_v1.py b/src/Prototypes/Computer Vision/POC/Train Model/train_model_v1.py
similarity index 100%
rename from src/Prototypes/Computer Vision/Train Model/train_model_v1.py
rename to src/Prototypes/Computer Vision/POC/Train Model/train_model_v1.py
diff --git a/src/Prototypes/Computer Vision/reademe.md b/src/Prototypes/Computer Vision/POC/reademe.md
similarity index 100%
rename from src/Prototypes/Computer Vision/reademe.md
rename to src/Prototypes/Computer Vision/POC/reademe.md