diff --git a/.ipynb_checkpoints/Research-checkpoint.ipynb b/.ipynb_checkpoints/Research-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..363fcab7ed6e9634e198cf5555ceb88932c9a245
--- /dev/null
+++ b/.ipynb_checkpoints/Research-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..363fcab7ed6e9634e198cf5555ceb88932c9a245
--- /dev/null
+++ b/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/app-checkpoint.ipynb b/.ipynb_checkpoints/app-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ebac8b2d30c5c8476d46dd1b77bdf2ca6e2d2dbd
--- /dev/null
+++ b/.ipynb_checkpoints/app-checkpoint.ipynb
@@ -0,0 +1,956 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cd0d8fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting gradio\n",
+      "  Downloading gradio-3.36.0-py3-none-any.whl (19.8 MB)\n",
+      "                                              0.0/19.8 MB ? eta -:--:--\n",
+      "                                              0.0/19.8 MB ? eta -:--:--\n",
+      "                                             0.0/19.8 MB 325.1 kB/s eta 0:01:01\n",
+      "                                             0.1/19.8 MB 744.7 kB/s eta 0:00:27\n",
+      "                                              0.3/19.8 MB 1.4 MB/s eta 0:00:14\n",
+      "                                              0.5/19.8 MB 2.2 MB/s eta 0:00:09\n",
+      "     -                                        1.0/19.8 MB 3.7 MB/s eta 0:00:06\n",
+      "     --                                       1.0/19.8 MB 3.4 MB/s eta 0:00:06\n",
+      "     ----                                     2.0/19.8 MB 5.9 MB/s eta 0:00:04\n",
+      "     ----                                     2.1/19.8 MB 5.4 MB/s eta 0:00:04\n",
+      "     ------                                   3.0/19.8 MB 6.9 MB/s eta 0:00:03\n",
+      "     -------                                  3.6/19.8 MB 7.4 MB/s eta 0:00:03\n",
+      "     --------                                 4.1/19.8 MB 7.8 MB/s eta 0:00:03\n",
+      "     ---------                                4.7/19.8 MB 8.1 MB/s eta 0:00:02\n",
+      "     ----------                               5.2/19.8 MB 8.3 MB/s eta 0:00:02\n",
+      "     -----------                              5.7/19.8 MB 8.5 MB/s eta 0:00:02\n",
+      "     ------------                             6.3/19.8 MB 8.8 MB/s eta 0:00:02\n",
+      "     -------------                            6.9/19.8 MB 8.9 MB/s eta 0:00:02\n",
+      "     --------------                           7.4/19.8 MB 9.1 MB/s eta 0:00:02\n",
+      "     ----------------                         7.9/19.8 MB 9.2 MB/s eta 0:00:02\n",
+      "     -----------------                        8.5/19.8 MB 9.3 MB/s eta 0:00:02\n",
+      "     ------------------                       9.0/19.8 MB 9.5 MB/s eta 0:00:02\n",
+      "     -------------------                      9.6/19.8 MB 9.6 MB/s eta 0:00:02\n",
+      "     --------------------                     10.1/19.8 MB 9.7 MB/s eta 0:00:02\n",
+      "     --------------------                    10.7/19.8 MB 11.5 MB/s eta 0:00:01\n",
+      "     ---------------------                   11.1/19.8 MB 11.7 MB/s eta 0:00:01\n",
+      "     -----------------------                 11.7/19.8 MB 11.9 MB/s eta 0:00:01\n",
+      "     -----------------------                 12.1/19.8 MB 11.5 MB/s eta 0:00:01\n",
+      "     ------------------------                12.6/19.8 MB 11.7 MB/s eta 0:00:01\n",
+      "     -------------------------               13.1/19.8 MB 11.3 MB/s eta 0:00:01\n",
+      "     --------------------------              13.5/19.8 MB 11.5 MB/s eta 0:00:01\n",
+      "     ---------------------------             13.9/19.8 MB 11.3 MB/s eta 0:00:01\n",
+      "     ----------------------------            14.4/19.8 MB 11.1 MB/s eta 0:00:01\n",
+      "     -----------------------------           14.9/19.8 MB 10.9 MB/s eta 0:00:01\n",
+      "     ------------------------------          15.4/19.8 MB 10.9 MB/s eta 0:00:01\n",
+      "     -------------------------------         15.8/19.8 MB 10.9 MB/s eta 0:00:01\n",
+      "     --------------------------------        16.3/19.8 MB 10.9 MB/s eta 0:00:01\n",
+      "     ---------------------------------       16.8/19.8 MB 10.7 MB/s eta 0:00:01\n",
+      "     ---------------------------------       17.3/19.8 MB 10.7 MB/s eta 0:00:01\n",
+      "     ----------------------------------      17.7/19.8 MB 10.7 MB/s eta 0:00:01\n",
+      "     -----------------------------------     18.2/19.8 MB 10.6 MB/s eta 0:00:01\n",
+      "     ------------------------------------    18.7/19.8 MB 10.6 MB/s eta 0:00:01\n",
+      "     -------------------------------------   19.1/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.7/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.8/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.8/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.8/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.8/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.8/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.8/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.8/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.8/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.8/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.8/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.8/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     ---------------------------------------- 19.8/19.8 MB 6.7 MB/s eta 0:00:00\n",
+      "Collecting aiofiles (from gradio)\n",
+      "  Using cached aiofiles-23.1.0-py3-none-any.whl (14 kB)\n",
+      "Requirement already satisfied: aiohttp in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (3.8.4)\n",
+      "Collecting altair>=4.2.0 (from gradio)\n",
+      "  Using cached altair-5.0.1-py3-none-any.whl (471 kB)\n",
+      "Collecting fastapi (from gradio)\n",
+      "  Downloading fastapi-0.99.1-py3-none-any.whl (58 kB)\n",
+      "                                              0.0/58.4 kB ? eta -:--:--\n",
+      "     ---------------------------------------- 58.4/58.4 kB 3.0 MB/s eta 0:00:00\n",
+      "Collecting ffmpy (from gradio)\n",
+      "  Using cached ffmpy-0.3.0.tar.gz (4.8 kB)\n",
+      "  Preparing metadata (setup.py): started\n",
+      "  Preparing metadata (setup.py): finished with status 'done'\n",
+      "Collecting gradio-client>=0.2.7 (from gradio)\n",
+      "  Using cached gradio_client-0.2.7-py3-none-any.whl (288 kB)\n",
+      "Collecting httpx (from gradio)\n",
+      "  Using cached httpx-0.24.1-py3-none-any.whl (75 kB)\n",
+      "Requirement already satisfied: huggingface-hub>=0.14.0 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (0.16.2)\n",
+      "Requirement already satisfied: jinja2 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (3.1.2)\n",
+      "Requirement already satisfied: markdown-it-py[linkify]>=2.0.0 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (3.0.0)\n",
+      "Requirement already satisfied: markupsafe in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (2.1.1)\n",
+      "Requirement already satisfied: matplotlib in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (3.7.1)\n",
+      "Collecting mdit-py-plugins<=0.3.3 (from gradio)\n",
+      "  Using cached mdit_py_plugins-0.3.3-py3-none-any.whl (50 kB)\n",
+      "Requirement already satisfied: numpy in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (1.23.5)\n",
+      "Requirement already satisfied: orjson in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (3.9.1)\n",
+      "Requirement already satisfied: pandas in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (2.0.2)\n",
+      "Requirement already satisfied: pillow in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (9.4.0)\n",
+      "Requirement already satisfied: pydantic in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (1.10.9)\n",
+      "Collecting pydub (from gradio)\n",
+      "  Using cached pydub-0.25.1-py2.py3-none-any.whl (32 kB)\n",
+      "Requirement already satisfied: pygments>=2.12.0 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (2.15.1)\n",
+      "Collecting python-multipart (from gradio)\n",
+      "  Using cached python_multipart-0.0.6-py3-none-any.whl (45 kB)\n",
+      "Requirement already satisfied: pyyaml in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (6.0)\n",
+      "Requirement already satisfied: requests in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (2.29.0)\n",
+      "Collecting semantic-version (from gradio)\n",
+      "  Using cached semantic_version-2.10.0-py2.py3-none-any.whl (15 kB)\n",
+      "Collecting uvicorn>=0.14.0 (from gradio)\n",
+      "  Using cached uvicorn-0.22.0-py3-none-any.whl (58 kB)\n",
+      "Collecting websockets>=10.0 (from gradio)\n",
+      "  Using cached websockets-11.0.3-cp310-cp310-win_amd64.whl (124 kB)\n",
+      "Requirement already satisfied: jsonschema>=3.0 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from altair>=4.2.0->gradio) (4.17.3)\n",
+      "Collecting toolz (from altair>=4.2.0->gradio)\n",
+      "  Downloading toolz-0.12.0-py3-none-any.whl (55 kB)\n",
+      "                                              0.0/55.8 kB ? eta -:--:--\n",
+      "     ---------------------------------------- 55.8/55.8 kB ? eta 0:00:00\n",
+      "Requirement already satisfied: typing-extensions>=4.0.1 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from altair>=4.2.0->gradio) (4.6.3)\n",
+      "Requirement already satisfied: fsspec in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio-client>=0.2.7->gradio) (2023.6.0)\n",
+      "Requirement already satisfied: packaging in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio-client>=0.2.7->gradio) (23.0)\n",
+      "Requirement already satisfied: filelock in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from huggingface-hub>=0.14.0->gradio) (3.9.0)\n",
+      "Requirement already satisfied: tqdm>=4.42.1 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from huggingface-hub>=0.14.0->gradio) (4.65.0)\n",
+      "Requirement already satisfied: mdurl~=0.1 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from markdown-it-py[linkify]>=2.0.0->gradio) (0.1.2)\n",
+      "Collecting linkify-it-py<3,>=1 (from markdown-it-py[linkify]>=2.0.0->gradio)\n",
+      "  Using cached linkify_it_py-2.0.2-py3-none-any.whl (19 kB)\n",
+      "INFO: pip is looking at multiple versions of mdit-py-plugins to determine which version is compatible with other requirements. This could take a while.\n",
+      "Collecting mdit-py-plugins<=0.3.3 (from gradio)\n",
+      "  Using cached mdit_py_plugins-0.3.2-py3-none-any.whl (50 kB)\n",
+      "  Using cached mdit_py_plugins-0.3.1-py3-none-any.whl (46 kB)\n",
+      "  Using cached mdit_py_plugins-0.3.0-py3-none-any.whl (43 kB)\n",
+      "  Using cached mdit_py_plugins-0.2.8-py3-none-any.whl (41 kB)\n",
+      "  Using cached mdit_py_plugins-0.2.7-py3-none-any.whl (41 kB)\n",
+      "  Using cached mdit_py_plugins-0.2.6-py3-none-any.whl (39 kB)\n",
+      "  Using cached mdit_py_plugins-0.2.5-py3-none-any.whl (39 kB)\n",
+      "INFO: pip is looking at multiple versions of mdit-py-plugins to determine which version is compatible with other requirements. This could take a while.\n",
+      "  Using cached mdit_py_plugins-0.2.4-py3-none-any.whl (39 kB)\n",
+      "  Using cached mdit_py_plugins-0.2.3-py3-none-any.whl (39 kB)\n",
+      "  Using cached mdit_py_plugins-0.2.2-py3-none-any.whl (39 kB)\n",
+      "  Using cached mdit_py_plugins-0.2.1-py3-none-any.whl (38 kB)\n",
+      "  Using cached mdit_py_plugins-0.2.0-py3-none-any.whl (38 kB)\n",
+      "INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. If you want to abort this run, press Ctrl + C.\n",
+      "  Using cached mdit_py_plugins-0.1.0-py3-none-any.whl (37 kB)\n",
+      "Collecting markdown-it-py[linkify]>=2.0.0 (from gradio)\n",
+      "  Using cached markdown_it_py-3.0.0-py3-none-any.whl (87 kB)\n",
+      "  Using cached markdown_it_py-2.2.0-py3-none-any.whl (84 kB)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from pandas->gradio) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from pandas->gradio) (2023.3)\n",
+      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from pandas->gradio) (2023.3)\n",
+      "Requirement already satisfied: click>=7.0 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from uvicorn>=0.14.0->gradio) (8.1.3)\n",
+      "Collecting h11>=0.8 (from uvicorn>=0.14.0->gradio)\n",
+      "  Using cached h11-0.14.0-py3-none-any.whl (58 kB)\n",
+      "Requirement already satisfied: attrs>=17.3.0 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from aiohttp->gradio) (22.1.0)\n",
+      "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from aiohttp->gradio) (2.0.4)\n",
+      "Requirement already satisfied: multidict<7.0,>=4.5 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from aiohttp->gradio) (6.0.4)\n",
+      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from aiohttp->gradio) (4.0.2)\n",
+      "Requirement already satisfied: yarl<2.0,>=1.0 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from aiohttp->gradio) (1.9.2)\n",
+      "Requirement already satisfied: frozenlist>=1.1.1 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from aiohttp->gradio) (1.3.3)\n",
+      "Requirement already satisfied: aiosignal>=1.1.2 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from aiohttp->gradio) (1.3.1)\n",
+      "Collecting starlette<0.28.0,>=0.27.0 (from fastapi->gradio)\n",
+      "  Using cached starlette-0.27.0-py3-none-any.whl (66 kB)\n",
+      "Requirement already satisfied: certifi in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from httpx->gradio) (2023.5.7)\n",
+      "Collecting httpcore<0.18.0,>=0.15.0 (from httpx->gradio)\n",
+      "  Downloading httpcore-0.17.3-py3-none-any.whl (74 kB)\n",
+      "                                              0.0/74.5 kB ? eta -:--:--\n",
+      "     ---------------------------------------- 74.5/74.5 kB 4.0 MB/s eta 0:00:00\n",
+      "Requirement already satisfied: idna in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from httpx->gradio) (3.4)\n",
+      "Requirement already satisfied: sniffio in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from httpx->gradio) (1.2.0)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from matplotlib->gradio) (1.1.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from matplotlib->gradio) (0.11.0)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from matplotlib->gradio) (4.40.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from matplotlib->gradio) (1.4.4)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from matplotlib->gradio) (3.0.9)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from requests->gradio) (1.26.16)\n",
+      "Requirement already satisfied: colorama in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from click>=7.0->uvicorn>=0.14.0->gradio) (0.4.6)\n",
+      "Requirement already satisfied: anyio<5.0,>=3.0 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from httpcore<0.18.0,>=0.15.0->httpx->gradio) (3.5.0)\n",
+      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from jsonschema>=3.0->altair>=4.2.0->gradio) (0.18.0)\n",
+      "Collecting uc-micro-py (from linkify-it-py<3,>=1->markdown-it-py[linkify]>=2.0.0->gradio)\n",
+      "  Using cached uc_micro_py-1.0.2-py3-none-any.whl (6.2 kB)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from python-dateutil>=2.8.2->pandas->gradio) (1.16.0)\n",
+      "Building wheels for collected packages: ffmpy\n",
+      "  Building wheel for ffmpy (setup.py): started\n",
+      "  Building wheel for ffmpy (setup.py): finished with status 'done'\n",
+      "  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4712 sha256=f93e3038acd8c0ff40f475bf536d01d2ac1b3a1fa1cea06bd7b56ff3422b3f16\n",
+      "  Stored in directory: c:\\users\\hh\\appdata\\local\\pip\\cache\\wheels\\0c\\c2\\0e\\3b9c6845c6a4e35beb90910cc70d9ac9ab5d47402bd62af0df\n",
+      "Successfully built ffmpy\n",
+      "Installing collected packages: pydub, ffmpy, websockets, uc-micro-py, toolz, semantic-version, python-multipart, markdown-it-py, h11, aiofiles, uvicorn, starlette, mdit-py-plugins, linkify-it-py, httpcore, httpx, fastapi, altair, gradio-client, gradio\n",
+      "  Attempting uninstall: markdown-it-py\n",
+      "    Found existing installation: markdown-it-py 3.0.0\n",
+      "    Uninstalling markdown-it-py-3.0.0:\n",
+      "      Successfully uninstalled markdown-it-py-3.0.0\n",
+      "Successfully installed aiofiles-23.1.0 altair-5.0.1 fastapi-0.99.1 ffmpy-0.3.0 gradio-3.36.0 gradio-client-0.2.7 h11-0.14.0 httpcore-0.17.3 httpx-0.24.1 linkify-it-py-2.0.2 markdown-it-py-2.2.0 mdit-py-plugins-0.3.3 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 starlette-0.27.0 toolz-0.12.0 uc-micro-py-1.0.2 uvicorn-0.22.0 websockets-11.0.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "#|default_exp app\n",
+    "# !pip install gradio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7195af18",
+   "metadata": {},
+   "source": [
+    "## Gradio Pets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44eb0ad3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "from fastai.vision.all import *\n",
+    "import gradio as gr\n",
+    "import timm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3295ef11",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "PILImage mode=RGB size=224x224"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "im = PILImage.create('Gold fish.jpg')\n",
+    "im.thumbnail((224,224))\n",
+    "im"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ae2bc6ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#export\n",
+    "learn = load_learner('model.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e0bf9da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "('AMERICAN GOLDFINCH',\n",
+       " tensor(0),\n",
+       " tensor([1.0000e+00, 1.3542e-08, 2.0991e-08, 3.5330e-08, 1.6987e-07, 2.3473e-08]))"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "learn.predict(im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0419ed3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#export\n",
+    "categories = learn.dls.vocab\n",
+    "\n",
+    "def classify_image(img):\n",
+    "    pred,idx,probs = learn.predict(img)\n",
+    "    return dict(zip(categories, map(float,probs)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "762dec00",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'AMERICAN GOLDFINCH': 0.9999998807907104,\n",
+       " 'BARN OWL': 1.3541999521748949e-08,\n",
+       " 'CARMINE BEE-EATER': 2.09909085668869e-08,\n",
+       " 'DOWNY WOODPECKER': 3.5330007364109406e-08,\n",
+       " 'EMPEROR PENGUIN': 1.698742551070609e-07,\n",
+       " 'FLAMINGO': 2.3473393895301342e-08}"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "classify_image(im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "930cf172",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hh\\AppData\\Local\\Temp\\ipykernel_19564\\371635615.py:2: GradioDeprecationWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
+      "  image = gr.inputs.Image(shape=(192, 192))\n",
+      "C:\\Users\\hh\\AppData\\Local\\Temp\\ipykernel_19564\\371635615.py:2: GradioDeprecationWarning: `optional` parameter is deprecated, and it has no effect\n",
+      "  image = gr.inputs.Image(shape=(192, 192))\n",
+      "C:\\Users\\hh\\AppData\\Local\\Temp\\ipykernel_19564\\371635615.py:3: GradioDeprecationWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
+      "  label = gr.outputs.Label()\n",
+      "C:\\Users\\hh\\AppData\\Local\\Temp\\ipykernel_19564\\371635615.py:3: GradioUnusedKwargWarning: You have unused kwarg parameters in Label, please remove them: {'type': 'auto'}\n",
+      "  label = gr.outputs.Label()\n"
+     ]
+    }
+   ],
+   "source": [
+    "#export\n",
+    "image = gr.inputs.Image(shape=(192, 192))\n",
+    "label = gr.outputs.Label()\n",
+    "examples = ['Gold fish.jpg']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f463e23",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:3000/\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<fastapi.applications.FastAPI>,\n",
+       " 'http://127.0.0.1:3000/',\n",
+       " None)"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#export\n",
+    "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
+    "intf.launch(inline=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82774c08",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sequential(\n",
+       "  (0): TimmBody(\n",
+       "    (model): ConvNeXt(\n",
+       "      (stem): Sequential(\n",
+       "        (0): Conv2d(3, 96, kernel_size=(4, 4), stride=(4, 4))\n",
+       "        (1): LayerNorm2d((96,), eps=1e-06, elementwise_affine=True)\n",
+       "      )\n",
+       "      (stages): Sequential(\n",
+       "        (0): ConvNeXtStage(\n",
+       "          (downsample): Identity()\n",
+       "          (blocks): Sequential(\n",
+       "            (0): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(96, 96, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=96)\n",
+       "              (norm): LayerNorm((96,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=96, out_features=384, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (fc2): Linear(in_features=384, out_features=96, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (1): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(96, 96, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=96)\n",
+       "              (norm): LayerNorm((96,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=96, out_features=384, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (fc2): Linear(in_features=384, out_features=96, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (2): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(96, 96, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=96)\n",
+       "              (norm): LayerNorm((96,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=96, out_features=384, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (fc2): Linear(in_features=384, out_features=96, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "          )\n",
+       "        )\n",
+       "        (1): ConvNeXtStage(\n",
+       "          (downsample): Sequential(\n",
+       "            (0): LayerNorm2d((96,), eps=1e-06, elementwise_affine=True)\n",
+       "            (1): Conv2d(96, 192, kernel_size=(2, 2), stride=(2, 2))\n",
+       "          )\n",
+       "          (blocks): Sequential(\n",
+       "            (0): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)\n",
+       "              (norm): LayerNorm((192,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=192, out_features=768, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (1): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)\n",
+       "              (norm): LayerNorm((192,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=192, out_features=768, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (2): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)\n",
+       "              (norm): LayerNorm((192,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=192, out_features=768, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "          )\n",
+       "        )\n",
+       "        (2): ConvNeXtStage(\n",
+       "          (downsample): Sequential(\n",
+       "            (0): LayerNorm2d((192,), eps=1e-06, elementwise_affine=True)\n",
+       "            (1): Conv2d(192, 384, kernel_size=(2, 2), stride=(2, 2))\n",
+       "          )\n",
+       "          (blocks): Sequential(\n",
+       "            (0): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
+       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (1): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
+       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (2): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
+       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (3): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
+       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (4): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
+       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (5): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
+       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (6): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
+       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (7): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
+       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (8): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
+       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "          )\n",
+       "        )\n",
+       "        (3): ConvNeXtStage(\n",
+       "          (downsample): Sequential(\n",
+       "            (0): LayerNorm2d((384,), eps=1e-06, elementwise_affine=True)\n",
+       "            (1): Conv2d(384, 768, kernel_size=(2, 2), stride=(2, 2))\n",
+       "          )\n",
+       "          (blocks): Sequential(\n",
+       "            (0): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)\n",
+       "              (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (1): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)\n",
+       "              (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (2): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)\n",
+       "              (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "          )\n",
+       "        )\n",
+       "      )\n",
+       "      (norm_pre): Identity()\n",
+       "      (head): Sequential(\n",
+       "        (global_pool): SelectAdaptivePool2d (pool_type=avg, flatten=Identity())\n",
+       "        (norm): LayerNorm2d((768,), eps=1e-06, elementwise_affine=True)\n",
+       "        (flatten): Flatten(start_dim=1, end_dim=-1)\n",
+       "        (drop): Dropout(p=0.0, inplace=False)\n",
+       "        (fc): Identity()\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (1): Sequential(\n",
+       "    (0): AdaptiveConcatPool2d(\n",
+       "      (ap): AdaptiveAvgPool2d(output_size=1)\n",
+       "      (mp): AdaptiveMaxPool2d(output_size=1)\n",
+       "    )\n",
+       "    (1): Flatten(full=False)\n",
+       "    (2): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "    (3): Dropout(p=0.25, inplace=False)\n",
+       "    (4): Linear(in_features=1536, out_features=512, bias=False)\n",
+       "    (5): ReLU(inplace=True)\n",
+       "    (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "    (7): Dropout(p=0.5, inplace=False)\n",
+       "    (8): Linear(in_features=512, out_features=37, bias=False)\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m = learn.model\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10d7900d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Parameter containing:\n",
+       " tensor([ 1.2545e+00,  1.9196e+00,  1.2201e+00,  1.0390e+00, -1.6480e-03,\n",
+       "          7.6568e-01,  8.8830e-01,  1.6302e+00,  7.0489e-01,  3.2909e+00,\n",
+       "          7.8756e-01, -1.2321e-03,  1.0008e+00, -1.1701e-03,  3.2963e+00,\n",
+       "          7.5332e-04,  1.9848e+00,  1.0214e+00,  4.4530e+00,  2.5485e-01,\n",
+       "          2.7261e+00,  9.2749e-01,  1.2365e+00,  4.6786e-03,  1.7861e+00,\n",
+       "          5.4500e-01,  4.6252e+00,  1.1814e-02, -8.0696e-04,  3.4503e+00,\n",
+       "          1.3520e+00,  4.1267e+00,  2.6889e+00,  4.1214e+00,  3.4020e+00,\n",
+       "          8.4680e-01,  7.3639e-01,  3.9801e+00,  1.2857e+00,  6.4153e-01,\n",
+       "          2.6896e+00,  1.1183e+00,  1.1701e+00,  5.5256e-01,  2.3371e+00,\n",
+       "          2.6110e-04,  9.7016e-01,  2.1527e-03,  1.1990e+00,  1.7883e+00,\n",
+       "          4.0231e-01,  4.4849e-01,  9.7238e-01,  3.9889e+00,  6.5864e-01,\n",
+       "          6.8973e-01,  9.8424e-01,  2.7063e+00,  1.2161e+00,  7.5966e-01,\n",
+       "          3.3019e+00,  1.6209e+00,  9.5479e-01,  2.1214e+00,  6.2982e-01,\n",
+       "          4.0345e+00,  8.9406e-01, -1.5776e-03,  4.0855e+00,  1.0646e+00,\n",
+       "          1.3953e+00,  1.6694e+00,  7.7575e-04,  7.6740e-01,  8.8671e-01,\n",
+       "          6.4291e-01,  1.3444e+00,  7.1629e-01,  5.4538e-01,  2.0897e+00,\n",
+       "          1.1951e+00,  3.0924e-01,  2.9660e-01,  1.4705e+00,  4.0818e+00,\n",
+       "         -1.9466e-03,  1.1466e+00,  3.8855e+00,  3.6003e+00,  4.8230e-01,\n",
+       "          2.1677e-01,  1.2715e-03,  6.5100e-01,  3.0062e+00,  3.0463e+00,\n",
+       "          6.9039e-03], requires_grad=True),\n",
+       " Parameter containing:\n",
+       " tensor([-9.7076e-02, -4.1602e-02,  4.1634e+00, -1.0902e-02,  2.5195e-03,\n",
+       "         -2.6698e-02, -3.1112e-02, -8.0897e-02, -1.3977e-01, -6.1426e-02,\n",
+       "          3.2092e-01, -3.3970e-01, -5.7320e-02, -5.0723e-03, -4.5248e-02,\n",
+       "         -2.6788e-02, -4.0929e-02, -3.8162e-02,  8.6140e-03, -2.3497e-02,\n",
+       "          9.3659e-03, -1.6219e-01, -4.0165e+00,  5.3178e-01, -5.3446e-01,\n",
+       "          2.8025e+00,  3.7767e-02, -8.2848e-03, -1.0448e-03, -1.1741e-01,\n",
+       "         -1.3899e-01,  1.9646e-02, -9.6837e-02, -1.3024e-01, -1.9224e-01,\n",
+       "         -6.6514e-02, -3.5839e-02, -1.2878e-01,  1.5046e-01,  7.7289e-04,\n",
+       "         -6.4686e-02,  5.7553e-02, -9.2985e-02, -1.1460e+00, -5.4285e-02,\n",
+       "         -5.4245e-03, -1.8200e-01,  2.2406e-02,  3.9471e-02, -5.9339e-02,\n",
+       "         -4.1768e-02, -5.6190e-02, -4.3428e-02, -1.2965e-02, -1.1264e-01,\n",
+       "          4.9255e-03, -3.7258e-02, -1.5767e-01, -9.7796e-02, -1.8840e-01,\n",
+       "         -1.1216e-01, -1.8182e-01, -3.2907e-02, -2.8298e-02,  1.4188e+00,\n",
+       "         -3.3801e-02, -4.1863e-02, -2.6832e-01, -4.7449e-02, -4.5676e-05,\n",
+       "          2.6773e-01,  1.8772e-01,  6.9789e-01, -3.0746e-01,  8.3766e-02,\n",
+       "         -1.0845e+00,  1.5364e-02, -4.4824e-02, -7.7240e-02, -6.7920e-02,\n",
+       "         -1.3150e-01, -1.6358e-02, -1.7351e-02, -3.9939e-02, -7.2383e-02,\n",
+       "          1.0359e-02, -5.8904e-02, -3.8567e-02, -7.9783e-02, -7.3791e-02,\n",
+       "         -1.0318e-02, -3.7690e-01, -9.9740e-03, -2.7374e-02, -6.3630e-02,\n",
+       "          1.5712e-03], requires_grad=True)]"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "l = m.get_submodule('0.model.stem.1')\n",
+    "list(l.parameters())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "008537b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Parameter containing:\n",
+       " tensor([[ 2.2773e-02, -1.6051e-03,  4.0450e-02,  ...,  1.7370e-03,\n",
+       "          -4.5070e-02,  8.0949e-03],\n",
+       "         [-1.4383e-01,  1.6965e-02,  2.5983e-02,  ...,  1.2606e-02,\n",
+       "          -1.0443e-01,  5.6370e-02],\n",
+       "         [-6.5471e-02, -3.2719e-02,  5.6796e-03,  ..., -4.1571e-02,\n",
+       "           6.5921e-02, -4.0347e-02],\n",
+       "         ...,\n",
+       "         [-8.8080e-03,  6.9815e-02,  7.1424e-05,  ...,  4.0177e-03,\n",
+       "           4.1478e-02, -1.9052e-02],\n",
+       "         [ 2.0792e-03,  3.2267e-02,  2.9801e-02,  ..., -2.9897e-02,\n",
+       "          -3.0278e-02,  5.5432e-02],\n",
+       "         [ 1.2097e-01, -3.5444e-02, -4.6078e-03,  ..., -6.3829e-03,\n",
+       "           2.3691e-02, -1.1242e-02]], requires_grad=True),\n",
+       " Parameter containing:\n",
+       " tensor([-0.4047, -0.7418, -0.4235, -0.1650, -0.3028, -0.1898, -0.5534, -0.6271,\n",
+       "         -0.3008, -0.4254, -0.5997, -0.4107, -0.2172, -1.7935, -0.3170, -0.1163,\n",
+       "         -0.4482, -0.2846, -0.4342, -0.4945, -0.4065, -1.1402, -0.6754, -1.7237,\n",
+       "         -0.2955, -0.2654, -0.2187, -0.3914, -0.4150, -0.4772,  0.2365, -0.7542,\n",
+       "         -0.5852, -0.1820, -1.5272, -0.3626, -2.4689, -2.3461, -0.6109, -0.4115,\n",
+       "         -0.6964, -0.5764, -0.5878, -0.0318, -2.0354, -0.2859, -0.3953, -0.8402,\n",
+       "         -2.2398, -1.0876, -0.2295, -0.9004, -0.7584, -0.8833, -0.3755, -0.4549,\n",
+       "         -0.3835, -0.4047, -2.0231, -1.0263, -0.4106, -1.1564, -0.2224, -0.4250,\n",
+       "         -0.2494, -0.4222, -0.0975, -1.4017, -0.6887, -0.4370, -0.2932, -0.4641,\n",
+       "         -0.4958, -1.2534, -1.0720, -1.2966, -0.6276, -1.4161, -2.3080, -2.4538,\n",
+       "         -0.4259, -0.9987, -0.4638, -0.3147, -0.2416, -0.8744, -0.2829, -1.4208,\n",
+       "         -0.3257, -0.3202, -0.0602, -0.1896, -0.2497, -0.6129, -0.2976, -2.1465,\n",
+       "         -0.4128, -0.3675, -1.9815, -0.3815, -0.3785, -0.2292, -0.3700, -0.3256,\n",
+       "         -0.5584, -2.4192, -0.4590, -1.7748, -0.3996, -0.4092, -0.3518, -0.5332,\n",
+       "         -1.6534, -1.8191,  0.6263, -0.4058,  0.5872, -2.2074, -0.2438, -2.4540,\n",
+       "         -0.2283, -0.6865,  0.6988,  0.6477, -0.6445, -0.3454, -0.3275, -0.5701,\n",
+       "         -0.5173, -0.2774, -0.4090, -0.3018, -0.4874, -0.4954, -0.4073, -0.4356,\n",
+       "         -0.5103, -0.4128, -2.0919, -0.2825, -0.5830, -1.5834,  0.6139, -0.8506,\n",
+       "         -0.4669, -2.1358, -0.3417, -0.3766, -0.3345, -0.3961, -0.3886, -0.5668,\n",
+       "         -0.2224, -1.3059, -0.4601, -0.3928, -0.4665, -0.4214, -0.4755, -0.2865,\n",
+       "         -1.5804, -0.1787, -0.4368, -0.3173,  1.5732, -0.4046, -0.4839, -0.2576,\n",
+       "         -0.5611, -0.4265, -0.2578, -0.3176, -0.4620, -1.9553, -1.9146, -0.3961,\n",
+       "          0.3988, -2.3520, -0.9689, -0.2831, -1.9000, -0.4180,  0.0160, -1.1111,\n",
+       "         -0.4924, -0.3177, -1.8912, -0.3101, -0.8137, -2.3345, -0.3843, -0.3847,\n",
+       "         -0.1974, -0.4444, -1.6233, -2.5485, -0.3178, -1.2715, -1.1479,  0.6149,\n",
+       "         -0.3749, -0.3952, -2.0747, -0.4657, -0.3782, -0.4958, -0.3281, -1.9219,\n",
+       "         -2.0018, -0.5307, -0.2555, -1.1161, -0.3516, -2.2185, -1.1394,  0.5366,\n",
+       "         -0.3218, -2.0387, -0.4656,  0.1850, -0.5830, -0.3129,  0.6182, -0.2124,\n",
+       "         -2.3538, -0.9700, -0.9784, -0.3668, -0.4503, -1.9564, -0.2662, -1.1754,\n",
+       "         -0.4200, -0.9024, -0.3604, -0.5172, -1.1882, -0.4191, -0.4770, -1.5558,\n",
+       "         -0.4011, -0.6518, -0.4817, -0.2422,  0.6909, -0.5080, -0.4303, -0.6068,\n",
+       "         -0.4001, -0.3329, -0.3596, -1.6108, -0.2371, -0.2467, -0.4545,  0.1808,\n",
+       "         -0.3225, -0.3918, -0.3514, -0.3756, -1.2178, -0.4000, -0.3578, -0.2883,\n",
+       "         -1.7485, -0.2364, -0.1599, -0.2640, -0.9769, -1.3066, -0.4148, -0.2663,\n",
+       "         -0.3933, -0.4628, -0.2174,  0.2141, -0.5733, -0.2766, -0.3658, -0.5171,\n",
+       "         -0.3484, -0.3365, -0.6445,  0.6866, -0.3738, -0.2902, -2.0863, -0.4882,\n",
+       "         -0.2597, -1.0497, -1.6616, -0.3399, -0.5111, -0.5661, -0.3029, -0.5048,\n",
+       "         -0.2877, -0.2841, -0.1981, -0.6910, -0.2872, -2.1120, -0.8928, -0.2299,\n",
+       "         -1.5010, -0.4734, -2.2293, -0.4020, -0.2925, -0.4198,  0.6646, -0.3047,\n",
+       "         -0.1687, -0.3750, -0.6434, -2.3348, -0.3102, -1.2732, -0.8192, -1.0592,\n",
+       "         -0.0931, -1.6385,  0.3426, -0.8484, -0.4910, -0.5002, -1.0631, -0.3532,\n",
+       "         -1.1562, -0.3843, -0.3172, -0.6432, -0.9083, -0.6567, -0.6489,  0.6336,\n",
+       "         -0.2663, -1.3203, -1.1623, -1.2032, -2.0576, -0.3001, -1.3597, -0.4614,\n",
+       "         -0.5024, -0.4949, -0.3158, -0.3273, -0.2668, -0.4280, -0.3296, -0.3011,\n",
+       "         -1.6635,  0.6434, -0.9455,  0.6097, -0.4234,  0.3918, -0.4943, -0.4285,\n",
+       "         -0.2588, -0.4951, -2.1992, -0.2601, -0.3935, -0.4564, -0.5817, -0.3487,\n",
+       "         -0.7372, -0.3589, -0.4894, -2.0108,  0.4556, -0.8057, -1.7749, -0.3511,\n",
+       "         -0.5359, -0.2100, -0.3956, -0.4780, -1.1457, -0.3976, -2.2114, -0.2840],\n",
+       "        requires_grad=True)]"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "l = m.get_submodule('0.model.stages.0.blocks.1.mlp.fc1')\n",
+    "list(l.parameters())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff9d5c73",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/train-checkpoint.ipynb b/.ipynb_checkpoints/train-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..98d7676be571c53a1cfa91a484fe7c6584d59259
--- /dev/null
+++ b/.ipynb_checkpoints/train-checkpoint.ipynb
@@ -0,0 +1,401 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7195af18",
+   "metadata": {},
+   "source": [
+    "## Gradio Pets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "370354ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install timm\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44eb0ad3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastai.vision.all import *\n",
+    "import timm\n",
+    "import os\n",
+    "import zipfile\n",
+    "import urllib.request\n",
+    "import os\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from fastai.vision.all import *\n",
+    "\n",
+    "data_dir = \"./data\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "085350d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = \"https://github.com/ddthang86/Bird-dataset/raw/main/Bird_Speciees_Dataset.zip\"\n",
+    "save_path = os.path.join(data_dir, \"Bird_Speciees_Dataset.rar\")\n",
+    "\n",
+    "if not os.path.exists(save_path):\n",
+    "    urllib.request.urlretrieve(url, save_path)\n",
+    "    \n",
+    "    # read by zipfile\n",
+    "    zip = zipfile.ZipFile(save_path)\n",
+    "    zip.extractall(data_dir)\n",
+    "    zip.close()\n",
+    "    \n",
+    "    os.remove(save_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "673d61c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set the path to the data folder\n",
+    "path = Path(\"./data/Bird_Speciees_Dataset\")\n",
+    "\n",
+    "# Get the list of all image file paths in the data folder\n",
+    "image_paths = [os.path.join(path, folder, file) \n",
+    "               for folder in os.listdir(path) \n",
+    "               for file in os.listdir(os.path.join(path, folder))]\n",
+    "\n",
+    "# Get the corresponding labels based on the folder names\n",
+    "labels = [os.path.basename(os.path.dirname(image_path)) \n",
+    "          for image_path in image_paths]\n",
+    "\n",
+    "# # Split the data into training and validation sets\n",
+    "# train_paths, valid_paths, train_labels, valid_labels = train_test_split(image_paths, labels, \n",
+    "#                                                                         test_size=0.2, random_state=42)\n",
+    "\n",
+    "# # Define the transformations to be applied to the images\n",
+    "# # You can modify these according to your requirements\n",
+    "# item_tfms = Resize(224)\n",
+    "# batch_tfms = aug_transforms()\n",
+    "\n",
+    "# # Create the DataLoaders using the training and validation sets\n",
+    "# dls = ImageDataLoaders.from_lists(path, train_paths, train_labels, valid_paths, valid_labels,\n",
+    "#                                   item_tfms=item_tfms, batch_tfms=batch_tfms)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d838c0b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "expected str, bytes or os.PathLike object, not list",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[18], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m dls \u001b[38;5;241m=\u001b[39m ImageDataLoaders\u001b[38;5;241m.\u001b[39mfrom_name_func(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m----> 2\u001b[0m     \u001b[43mget_image_files\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage_paths\u001b[49m\u001b[43m)\u001b[49m, valid_pct\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m, seed\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m,\n\u001b[0;32m      3\u001b[0m     label_func\u001b[38;5;241m=\u001b[39mlabels,\n\u001b[0;32m      4\u001b[0m     item_tfms\u001b[38;5;241m=\u001b[39mResize(\u001b[38;5;241m224\u001b[39m))\n",
+      "File \u001b[1;32m~\\anaconda3\\envs\\cuda118\\lib\\site-packages\\fastai\\data\\transforms.py:61\u001b[0m, in \u001b[0;36mget_image_files\u001b[1;34m(path, recurse, folders)\u001b[0m\n\u001b[0;32m     59\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_image_files\u001b[39m(path, recurse\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, folders\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m     60\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGet image files in `path` recursively, only in `folders`, if specified.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m---> 61\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mget_files\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextensions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mimage_extensions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrecurse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrecurse\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfolders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfolders\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\anaconda3\\envs\\cuda118\\lib\\site-packages\\fastai\\data\\transforms.py:32\u001b[0m, in \u001b[0;36mget_files\u001b[1;34m(path, extensions, recurse, folders, followlinks)\u001b[0m\n\u001b[0;32m     30\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_files\u001b[39m(path, extensions\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, recurse\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, folders\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, followlinks\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m     31\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGet all the files in `path` with optional `extensions`, optionally with `recurse`, only in `folders`, if specified.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m---> 32\u001b[0m     path \u001b[38;5;241m=\u001b[39m \u001b[43mPath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     33\u001b[0m     folders\u001b[38;5;241m=\u001b[39mL(folders)\n\u001b[0;32m     34\u001b[0m     extensions \u001b[38;5;241m=\u001b[39m setify(extensions)\n",
+      "File \u001b[1;32m~\\anaconda3\\envs\\cuda118\\lib\\pathlib.py:960\u001b[0m, in \u001b[0;36mPath.__new__\u001b[1;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[0;32m    958\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m Path:\n\u001b[0;32m    959\u001b[0m     \u001b[38;5;28mcls\u001b[39m \u001b[38;5;241m=\u001b[39m WindowsPath \u001b[38;5;28;01mif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnt\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m PosixPath\n\u001b[1;32m--> 960\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_from_parts\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    961\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_flavour\u001b[38;5;241m.\u001b[39mis_supported:\n\u001b[0;32m    962\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot instantiate \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m on your system\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    963\u001b[0m                               \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m,))\n",
+      "File \u001b[1;32m~\\anaconda3\\envs\\cuda118\\lib\\pathlib.py:594\u001b[0m, in \u001b[0;36mPurePath._from_parts\u001b[1;34m(cls, args)\u001b[0m\n\u001b[0;32m    589\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[0;32m    590\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_from_parts\u001b[39m(\u001b[38;5;28mcls\u001b[39m, args):\n\u001b[0;32m    591\u001b[0m     \u001b[38;5;66;03m# We need to call _parse_args on the instance, so as to get the\u001b[39;00m\n\u001b[0;32m    592\u001b[0m     \u001b[38;5;66;03m# right flavour.\u001b[39;00m\n\u001b[0;32m    593\u001b[0m     \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mobject\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__new__\u001b[39m(\u001b[38;5;28mcls\u001b[39m)\n\u001b[1;32m--> 594\u001b[0m     drv, root, parts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    595\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_drv \u001b[38;5;241m=\u001b[39m drv\n\u001b[0;32m    596\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_root \u001b[38;5;241m=\u001b[39m root\n",
+      "File \u001b[1;32m~\\anaconda3\\envs\\cuda118\\lib\\pathlib.py:578\u001b[0m, in \u001b[0;36mPurePath._parse_args\u001b[1;34m(cls, args)\u001b[0m\n\u001b[0;32m    576\u001b[0m     parts \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m a\u001b[38;5;241m.\u001b[39m_parts\n\u001b[0;32m    577\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 578\u001b[0m     a \u001b[38;5;241m=\u001b[39m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfspath\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    579\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(a, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m    580\u001b[0m         \u001b[38;5;66;03m# Force-cast str subclasses to str (issue #21127)\u001b[39;00m\n\u001b[0;32m    581\u001b[0m         parts\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mstr\u001b[39m(a))\n",
+      "\u001b[1;31mTypeError\u001b[0m: expected str, bytes or os.PathLike object, not list"
+     ]
+    }
+   ],
+   "source": [
+    "dls = ImageDataLoaders.from_name_func('.',\n",
+    "    get_image_files(image_paths), valid_pct=0.2, seed=42,\n",
+    "    label_func=labels,\n",
+    "    item_tfms=Resize(224))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a362acc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x432 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dls.show_batch(max_n=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be8ae242",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: left;\">\n",
+       "      <th>epoch</th>\n",
+       "      <th>train_loss</th>\n",
+       "      <th>valid_loss</th>\n",
+       "      <th>error_rate</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>1.484187</td>\n",
+       "      <td>0.299950</td>\n",
+       "      <td>0.106225</td>\n",
+       "      <td>00:18</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: left;\">\n",
+       "      <th>epoch</th>\n",
+       "      <th>train_loss</th>\n",
+       "      <th>valid_loss</th>\n",
+       "      <th>error_rate</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>0.432079</td>\n",
+       "      <td>0.328865</td>\n",
+       "      <td>0.109608</td>\n",
+       "      <td>00:20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>0.267597</td>\n",
+       "      <td>0.238919</td>\n",
+       "      <td>0.079161</td>\n",
+       "      <td>00:20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>0.133704</td>\n",
+       "      <td>0.207618</td>\n",
+       "      <td>0.072395</td>\n",
+       "      <td>00:20</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "learn = vision_learner(dls, resnet34, metrics=error_rate)\n",
+    "learn.fine_tune(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "477ef53a-4b5c-4a07-81c2-95b8e7397cac",
+   "metadata": {},
+   "source": [
+    "We could try a better model, based on [this analysis](https://www.kaggle.com/code/jhoward/which-image-models-are-best/). The convnext models work great!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ee4197a-25be-48ac-a167-903bec5186b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['convnext_base',\n",
+       " 'convnext_base_384_in22ft1k',\n",
+       " 'convnext_base_in22ft1k',\n",
+       " 'convnext_base_in22k',\n",
+       " 'convnext_large',\n",
+       " 'convnext_large_384_in22ft1k',\n",
+       " 'convnext_large_in22ft1k',\n",
+       " 'convnext_large_in22k',\n",
+       " 'convnext_nano_hnf',\n",
+       " 'convnext_small',\n",
+       " 'convnext_small_384_in22ft1k',\n",
+       " 'convnext_small_in22ft1k',\n",
+       " 'convnext_small_in22k',\n",
+       " 'convnext_tiny',\n",
+       " 'convnext_tiny_384_in22ft1k',\n",
+       " 'convnext_tiny_hnf',\n",
+       " 'convnext_tiny_hnfd',\n",
+       " 'convnext_tiny_in22ft1k',\n",
+       " 'convnext_tiny_in22k',\n",
+       " 'convnext_xlarge_384_in22ft1k',\n",
+       " 'convnext_xlarge_in22ft1k',\n",
+       " 'convnext_xlarge_in22k']"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "timm.list_models('convnext*')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67d34f88-a580-48b9-9b42-e9d0c7e3e870",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: left;\">\n",
+       "      <th>epoch</th>\n",
+       "      <th>train_loss</th>\n",
+       "      <th>valid_loss</th>\n",
+       "      <th>error_rate</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>1.113290</td>\n",
+       "      <td>0.253742</td>\n",
+       "      <td>0.087957</td>\n",
+       "      <td>00:24</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: left;\">\n",
+       "      <th>epoch</th>\n",
+       "      <th>train_loss</th>\n",
+       "      <th>valid_loss</th>\n",
+       "      <th>error_rate</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>0.293625</td>\n",
+       "      <td>0.205537</td>\n",
+       "      <td>0.067659</td>\n",
+       "      <td>00:27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>0.195267</td>\n",
+       "      <td>0.185939</td>\n",
+       "      <td>0.055480</td>\n",
+       "      <td>00:27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>0.123829</td>\n",
+       "      <td>0.172681</td>\n",
+       "      <td>0.055480</td>\n",
+       "      <td>00:27</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "learn = vision_learner(dls, 'convnext_tiny_in22k', metrics=error_rate).to_fp16()\n",
+    "learn.fine_tune(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5171c7fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn.export('model.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33db2772-5334-4d7c-92b8-513b94de17e2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Gold fish.jpg b/Gold fish.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..75f240a0769d4003f584d4aec95dc2a8a0fca367
Binary files /dev/null and b/Gold fish.jpg differ
diff --git a/Research.ipynb b/Research.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..8be4cdd3440fbb67c523139aaacf9d7541b963d2
--- /dev/null
+++ b/Research.ipynb
@@ -0,0 +1,2095 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec8901bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from fastai.vision.all import *\n",
+    "import timm\n",
+    "import os\n",
+    "import zipfile\n",
+    "import urllib.request\n",
+    "data_dir = \"./data\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eaba0462",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = \"https://github.com/ddthang86/Bird-dataset/raw/main/Bird%20Speciees%20Dataset.zip\"\n",
+    "save_path = os.path.join(data_dir, \"Bird_speciees_dataset.zip\")\n",
+    "\n",
+    "if not os.path.exists(save_path):\n",
+    "    urllib.request.urlretrieve(url, save_path)\n",
+    "    \n",
+    "    # read by zipfile\n",
+    "    zip = zipfile.ZipFile(save_path)\n",
+    "    zip.extractall(data_dir)\n",
+    "    zip.close()\n",
+    "    \n",
+    "    os.remove(save_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "319a9f6b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from fastai.vision.all import *\n",
+    "\n",
+    "# Set the path to the data folder\n",
+    "path = Path(\"./data/Bird Speciees Dataset\")\n",
+    "\n",
+    "# Get the list of all image file paths in the data folder\n",
+    "image_paths = [os.path.join(path, folder, file) \n",
+    "               for folder in os.listdir(path) \n",
+    "               for file in os.listdir(os.path.join(path, folder))]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2de0bbc1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\001.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\002.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\003.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\004.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\005.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\006.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\007.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\008.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\009.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\010.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\011.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\012.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\013.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\014.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\015.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\016.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\017.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\018.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\019.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\020.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\021.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\022.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\023.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\024.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\025.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\026.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\027.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\028.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\029.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\030.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\031.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\032.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\033.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\034.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\035.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\036.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\037.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\038.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\039.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\040.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\041.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\042.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\043.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\044.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\045.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\046.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\047.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\048.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\049.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\050.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\051.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\052.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\053.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\054.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\055.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\056.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\057.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\058.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\059.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\060.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\061.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\062.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\063.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\064.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\065.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\066.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\067.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\068.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\069.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\070.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\071.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\072.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\073.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\074.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\075.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\076.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\077.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\078.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\079.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\080.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\081.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\082.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\083.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\084.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\085.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\086.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\087.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\088.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\089.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\090.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\091.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\092.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\093.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\094.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\095.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\096.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\097.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\098.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\099.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\100.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\101.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\102.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\103.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\104.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\105.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\106.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\107.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\108.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\109.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\110.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\111.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\112.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\113.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\114.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\115.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\116.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\117.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\118.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\119.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\120.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\121.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\122.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\123.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\124.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\125.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\126.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\127.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\128.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\129.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\130.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\131.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\132.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\133.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\134.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\135.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\136.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\137.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\138.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\139.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\140.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\141.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\142.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\143.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\001.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\002.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\003.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\004.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\005.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\006.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\007.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\008.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\009.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\010.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\011.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\012.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\013.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\014.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\015.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\016.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\017.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\018.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\019.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\020.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\021.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\022.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\023.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\024.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\025.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\026.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\027.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\028.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\029.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\030.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\031.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\032.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\033.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\034.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\035.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\036.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\037.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\038.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\039.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\040.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\041.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\042.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\043.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\044.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\045.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\046.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\047.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\048.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\049.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\050.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\051.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\052.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\053.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\054.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\055.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\056.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\057.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\058.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\059.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\060.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\061.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\062.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\063.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\064.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\065.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\066.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\067.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\068.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\069.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\070.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\071.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\072.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\073.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\074.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\075.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\076.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\077.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\078.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\079.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\080.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\081.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\082.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\083.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\084.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\085.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\086.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\087.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\088.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\089.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\090.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\091.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\092.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\093.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\094.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\095.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\096.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\097.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\098.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\099.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\100.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\101.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\102.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\103.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\104.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\105.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\106.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\107.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\108.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\109.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\110.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\111.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\112.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\113.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\114.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\115.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\116.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\117.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\118.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\119.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\120.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\121.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\122.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\123.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\124.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\125.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\126.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\127.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\128.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\129.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\001.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\002.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\003.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\004.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\005.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\006.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\007.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\008.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\009.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\010.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\011.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\012.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\013.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\014.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\015.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\016.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\017.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\018.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\019.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\020.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\021.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\022.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\023.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\024.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\025.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\026.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\027.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\028.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\029.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\030.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\031.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\032.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\033.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\034.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\035.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\036.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\037.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\038.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\039.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\040.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\041.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\042.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\043.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\044.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\045.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\046.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\047.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\048.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\049.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\050.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\051.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\052.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\053.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\054.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\055.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\056.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\057.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\058.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\059.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\060.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\061.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\062.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\063.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\064.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\065.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\066.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\067.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\068.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\069.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\070.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\071.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\072.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\073.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\074.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\075.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\076.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\077.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\078.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\079.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\080.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\081.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\082.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\083.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\084.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\085.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\086.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\087.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\088.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\089.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\090.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\091.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\092.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\093.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\094.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\095.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\096.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\097.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\098.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\099.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\100.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\101.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\102.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\103.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\104.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\105.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\106.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\107.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\108.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\109.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\110.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\111.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\112.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\113.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\114.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\115.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\116.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\117.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\118.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\119.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\120.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\121.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\122.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\123.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\124.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\125.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\126.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\127.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\128.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\129.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\130.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\131.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\001.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\002.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\003.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\004.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\005.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\006.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\007.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\008.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\009.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\010.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\011.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\012.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\013.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\014.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\015.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\016.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\017.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\018.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\019.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\020.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\021.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\022.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\023.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\024.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\025.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\026.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\027.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\028.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\029.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\030.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\031.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\032.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\033.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\034.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\035.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\036.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\037.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\038.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\039.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\040.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\041.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\042.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\043.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\044.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\045.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\046.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\047.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\048.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\049.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\050.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\051.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\052.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\053.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\054.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\055.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\056.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\057.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\058.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\059.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\060.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\061.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\062.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\063.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\064.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\065.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\066.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\067.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\068.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\069.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\070.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\071.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\072.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\073.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\074.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\075.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\076.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\077.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\078.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\079.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\080.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\081.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\082.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\083.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\084.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\085.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\086.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\087.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\088.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\089.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\090.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\091.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\092.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\093.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\094.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\095.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\096.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\097.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\098.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\099.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\100.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\101.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\102.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\103.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\104.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\105.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\106.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\107.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\108.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\109.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\110.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\111.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\112.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\113.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\114.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\115.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\116.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\117.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\118.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\119.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\120.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\121.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\122.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\123.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\124.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\125.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\126.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\127.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\128.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\129.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\130.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\131.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\132.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\133.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\134.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\135.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\136.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\137.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\001.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\002.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\003.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\004.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\005.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\006.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\007.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\008.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\009.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\010.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\011.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\012.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\013.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\014.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\015.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\016.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\017.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\018.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\019.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\020.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\021.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\022.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\023.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\024.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\025.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\026.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\027.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\028.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\029.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\030.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\031.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\032.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\033.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\034.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\035.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\036.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\037.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\038.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\039.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\040.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\041.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\042.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\043.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\044.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\045.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\046.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\047.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\048.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\049.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\050.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\051.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\052.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\053.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\054.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\055.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\056.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\057.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\058.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\059.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\060.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\061.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\062.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\063.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\064.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\065.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\066.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\067.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\068.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\069.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\070.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\071.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\072.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\073.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\074.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\075.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\076.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\077.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\078.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\079.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\080.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\081.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\082.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\083.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\084.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\085.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\086.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\087.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\088.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\089.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\090.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\091.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\092.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\093.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\094.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\095.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\096.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\097.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\098.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\099.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\100.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\101.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\102.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\103.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\104.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\105.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\106.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\107.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\108.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\109.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\110.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\111.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\112.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\113.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\114.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\115.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\116.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\117.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\118.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\119.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\120.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\121.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\122.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\123.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\124.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\125.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\126.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\127.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\128.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\129.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\130.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\131.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\132.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\133.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\134.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\135.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\136.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\137.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\138.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\139.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\001.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\002.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\003.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\004.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\005.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\006.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\007.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\008.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\009.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\010.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\011.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\012.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\013.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\014.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\015.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\016.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\017.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\018.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\019.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\020.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\021.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\022.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\023.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\024.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\025.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\026.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\027.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\028.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\029.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\030.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\031.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\032.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\033.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\034.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\035.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\036.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\037.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\038.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\039.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\040.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\041.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\042.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\043.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\044.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\045.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\046.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\047.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\048.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\049.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\050.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\051.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\052.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\053.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\054.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\055.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\056.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\057.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\058.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\059.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\060.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\061.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\062.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\063.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\064.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\065.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\066.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\067.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\068.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\069.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\070.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\071.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\072.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\073.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\074.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\075.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\076.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\077.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\078.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\079.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\080.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\081.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\082.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\083.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\084.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\085.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\086.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\087.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\088.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\089.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\090.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\091.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\092.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\093.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\094.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\095.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\096.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\097.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\098.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\099.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\100.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\101.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\102.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\103.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\104.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\105.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\106.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\107.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\108.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\109.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\110.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\111.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\112.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\113.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\114.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\115.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\116.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\117.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\118.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\119.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\120.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\121.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\122.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\123.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\124.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\125.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\126.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\127.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\128.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\129.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\130.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\131.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\132.jpg']"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "image_paths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9cde20cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the corresponding labels based on the folder names\n",
+    "labels = [os.path.basename(os.path.dirname(image_path)) \n",
+    "          for image_path in image_paths]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4641a7c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'AMERICAN GOLDFINCH',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'BARN OWL',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'CARMINE BEE-EATER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'DOWNY WOODPECKER',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'EMPEROR PENGUIN',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO',\n",
+       " 'FLAMINGO']"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "22e5ddfb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split the data into training and validation sets\n",
+    "train_paths, valid_paths, train_labels, valid_labels = train_test_split(image_paths, labels, \n",
+    "                                                                        test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "787cfd53",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\105.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\036.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\085.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\019.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\136.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\119.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\056.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\058.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\048.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\030.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\067.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\089.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\038.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\129.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\117.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\024.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\043.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\110.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\112.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\129.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\140.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\068.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\081.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\085.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\040.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\031.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\055.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\066.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\067.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\079.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\119.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\104.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\133.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\047.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\027.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\024.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\080.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\125.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\064.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\050.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\066.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\112.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\023.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\138.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\095.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\069.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\128.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\019.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\003.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\031.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\065.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\098.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\025.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\046.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\031.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\003.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\010.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\087.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\085.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\041.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\056.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\067.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\121.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\032.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\064.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\026.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\014.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\050.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\097.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\013.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\131.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\092.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\056.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\128.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\034.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\032.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\060.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\004.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\058.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\110.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\068.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\030.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\063.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\073.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\078.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\091.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\117.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\079.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\062.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\111.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\010.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\125.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\032.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\096.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\090.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\083.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\047.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\051.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\125.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\070.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\068.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\055.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\083.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\124.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\118.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\099.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\098.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\100.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\077.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\062.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\070.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\097.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\071.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\127.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\027.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\054.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\085.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\011.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\053.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\111.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\015.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\128.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\088.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\110.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\042.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\049.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\119.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\073.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\073.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\030.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\113.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\021.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\102.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\029.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\082.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\127.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\035.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\045.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\017.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\137.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\113.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\109.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\081.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\057.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\079.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\124.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\008.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\036.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\102.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\057.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\BARN OWL\\\\093.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\040.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\CARMINE BEE-EATER\\\\060.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\AMERICAN GOLDFINCH\\\\003.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\078.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\015.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\121.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\056.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\EMPEROR PENGUIN\\\\136.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\037.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\077.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\DOWNY WOODPECKER\\\\115.jpg',\n",
+       " 'data\\\\Bird Speciees Dataset\\\\FLAMINGO\\\\116.jpg']"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "valid_paths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43b6437d",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "int() argument must be a string, a bytes-like object or a real number, not 'list'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[10], line 7\u001b[0m\n\u001b[0;32m      4\u001b[0m batch_tfms \u001b[38;5;241m=\u001b[39m aug_transforms()\n\u001b[0;32m      6\u001b[0m \u001b[38;5;66;03m# Create the DataLoaders using the training and validation sets\u001b[39;00m\n\u001b[1;32m----> 7\u001b[0m dls \u001b[38;5;241m=\u001b[39m \u001b[43mImageDataLoaders\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_lists\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_paths\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_labels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalid_paths\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalid_labels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      8\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43mitem_tfms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mitem_tfms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_tfms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_tfms\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\anaconda3\\envs\\cuda118\\lib\\site-packages\\fastai\\vision\\data.py:198\u001b[0m, in \u001b[0;36mImageDataLoaders.from_lists\u001b[1;34m(cls, path, fnames, labels, valid_pct, seed, y_block, item_tfms, batch_tfms, img_cls, **kwargs)\u001b[0m\n\u001b[0;32m    192\u001b[0m     y_block \u001b[38;5;241m=\u001b[39m MultiCategoryBlock \u001b[38;5;28;01mif\u001b[39;00m is_listy(labels[\u001b[38;5;241m0\u001b[39m]) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(labels[\u001b[38;5;241m0\u001b[39m]) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m (\n\u001b[0;32m    193\u001b[0m         RegressionBlock \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(labels[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;28mfloat\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m CategoryBlock)\n\u001b[0;32m    194\u001b[0m dblock \u001b[38;5;241m=\u001b[39m DataBlock\u001b[38;5;241m.\u001b[39mfrom_columns(blocks\u001b[38;5;241m=\u001b[39m(ImageBlock(img_cls), y_block),\n\u001b[0;32m    195\u001b[0m                    splitter\u001b[38;5;241m=\u001b[39mRandomSplitter(valid_pct, seed\u001b[38;5;241m=\u001b[39mseed),\n\u001b[0;32m    196\u001b[0m                    item_tfms\u001b[38;5;241m=\u001b[39mitem_tfms,\n\u001b[0;32m    197\u001b[0m                    batch_tfms\u001b[38;5;241m=\u001b[39mbatch_tfms)\n\u001b[1;32m--> 198\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mfrom_dblock(dblock, (fnames, labels), path\u001b[38;5;241m=\u001b[39mpath, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32m~\\anaconda3\\envs\\cuda118\\lib\\site-packages\\fastai\\data\\core.py:284\u001b[0m, in \u001b[0;36mDataLoaders.from_dblock\u001b[1;34m(cls, dblock, source, path, bs, val_bs, shuffle, device, **kwargs)\u001b[0m\n\u001b[0;32m    273\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[0;32m    274\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_dblock\u001b[39m(\u001b[38;5;28mcls\u001b[39m, \n\u001b[0;32m    275\u001b[0m     dblock, \u001b[38;5;66;03m# `DataBlock` object\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    282\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m    283\u001b[0m ):\n\u001b[1;32m--> 284\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m dblock\u001b[38;5;241m.\u001b[39mdataloaders(source, path\u001b[38;5;241m=\u001b[39mpath, bs\u001b[38;5;241m=\u001b[39mbs, val_bs\u001b[38;5;241m=\u001b[39mval_bs, shuffle\u001b[38;5;241m=\u001b[39mshuffle, device\u001b[38;5;241m=\u001b[39mdevice, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32m~\\anaconda3\\envs\\cuda118\\lib\\site-packages\\fastai\\data\\block.py:155\u001b[0m, in \u001b[0;36mDataBlock.dataloaders\u001b[1;34m(self, source, path, verbose, **kwargs)\u001b[0m\n\u001b[0;32m    149\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdataloaders\u001b[39m(\u001b[38;5;28mself\u001b[39m, \n\u001b[0;32m    150\u001b[0m     source, \u001b[38;5;66;03m# The data source\u001b[39;00m\n\u001b[0;32m    151\u001b[0m     path:\u001b[38;5;28mstr\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;66;03m# Data source and default `Learner` path \u001b[39;00m\n\u001b[0;32m    152\u001b[0m     verbose:\u001b[38;5;28mbool\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;66;03m# Show verbose messages\u001b[39;00m\n\u001b[0;32m    153\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m    154\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataLoaders:\n\u001b[1;32m--> 155\u001b[0m     dsets \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdatasets\u001b[49m\u001b[43m(\u001b[49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    156\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdls_kwargs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mverbose\u001b[39m\u001b[38;5;124m'\u001b[39m: verbose}\n\u001b[0;32m    157\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m dsets\u001b[38;5;241m.\u001b[39mdataloaders(path\u001b[38;5;241m=\u001b[39mpath, after_item\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitem_tfms, after_batch\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch_tfms, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32m~\\anaconda3\\envs\\cuda118\\lib\\site-packages\\fastai\\data\\block.py:145\u001b[0m, in \u001b[0;36mDataBlock.datasets\u001b[1;34m(self, source, verbose)\u001b[0m\n\u001b[0;32m    143\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msource \u001b[38;5;241m=\u001b[39m source                     ; pv(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCollecting items from \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msource\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, verbose)\n\u001b[0;32m    144\u001b[0m items \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_items \u001b[38;5;129;01mor\u001b[39;00m noop)(source) ; pv(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(items)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m items\u001b[39m\u001b[38;5;124m\"\u001b[39m, verbose)\n\u001b[1;32m--> 145\u001b[0m splits \u001b[38;5;241m=\u001b[39m \u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplitter\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mRandomSplitter\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mitems\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    146\u001b[0m pv(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(splits)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m datasets of sizes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin([\u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mlen\u001b[39m(s))\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39ms\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39msplits])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, verbose)\n\u001b[0;32m    147\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Datasets(items, tfms\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_combine_type_tfms(), splits\u001b[38;5;241m=\u001b[39msplits, dl_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdl_type, n_inp\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_inp, verbose\u001b[38;5;241m=\u001b[39mverbose)\n",
+      "File \u001b[1;32m~\\anaconda3\\envs\\cuda118\\lib\\site-packages\\fastai\\data\\transforms.py:92\u001b[0m, in \u001b[0;36mRandomSplitter.<locals>._inner\u001b[1;34m(o)\u001b[0m\n\u001b[0;32m     91\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_inner\u001b[39m(o):\n\u001b[1;32m---> 92\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m seed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m: \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmanual_seed\u001b[49m\u001b[43m(\u001b[49m\u001b[43mseed\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     93\u001b[0m     rand_idx \u001b[38;5;241m=\u001b[39m L(\u001b[38;5;28mlist\u001b[39m(torch\u001b[38;5;241m.\u001b[39mrandperm(\u001b[38;5;28mlen\u001b[39m(o))\u001b[38;5;241m.\u001b[39mnumpy()))\n\u001b[0;32m     94\u001b[0m     cut \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(valid_pct \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mlen\u001b[39m(o))\n",
+      "File \u001b[1;32m~\\anaconda3\\envs\\cuda118\\lib\\site-packages\\torch\\random.py:36\u001b[0m, in \u001b[0;36mmanual_seed\u001b[1;34m(seed)\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmanual_seed\u001b[39m(seed) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39mGenerator:\n\u001b[0;32m     27\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sets the seed for generating random numbers. Returns a\u001b[39;00m\n\u001b[0;32m     28\u001b[0m \u001b[38;5;124;03m    `torch.Generator` object.\u001b[39;00m\n\u001b[0;32m     29\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     34\u001b[0m \u001b[38;5;124;03m            `0xffff_ffff_ffff_ffff + seed`.\u001b[39;00m\n\u001b[0;32m     35\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m---> 36\u001b[0m     seed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mseed\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     37\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcuda\u001b[39;00m\n\u001b[0;32m     39\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39m_is_in_bad_fork():\n",
+      "\u001b[1;31mTypeError\u001b[0m: int() argument must be a string, a bytes-like object or a real number, not 'list'"
+     ]
+    }
+   ],
+   "source": [
+    "# Define the transformations to be applied to the images\n",
+    "# You can modify these according to your requirements\n",
+    "item_tfms = Resize(224)\n",
+    "batch_tfms = aug_transforms()\n",
+    "\n",
+    "# Create the DataLoaders using the training and validation sets\n",
+    "dls = ImageDataLoaders.from_lists(path, train_paths, train_labels, valid_paths, valid_labels,\n",
+    "                                  item_tfms=item_tfms, batch_tfms=batch_tfms)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e85ecdf1",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'dls' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[11], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Show a batch of images\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[43mdls\u001b[49m\u001b[38;5;241m.\u001b[39mshow_batch()\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'dls' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# Show a batch of images\n",
+    "dls.show_batch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "109e58d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      <progress value='811712512' class='' max='811706944' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [811712512/811706944 05:41&lt;00:00]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "path = untar_data(URLs.PETS)/'images'\n",
+    "\n",
+    "dls = ImageDataLoaders.from_name_func('.',\n",
+    "    get_image_files(path), valid_pct=0.2, seed=42,\n",
+    "    label_func=RegexLabeller(pat = r'^([^/]+)_\\d+'),\n",
+    "    item_tfms=Resize(224))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "267a52a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Path('C:/Users/hh/.fastai/data/oxford-iiit-pet/images')"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c8350de",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/app.ipynb b/app.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e610e81e965666f708200ba44486a1544491a0cb
--- /dev/null
+++ b/app.ipynb
@@ -0,0 +1,1029 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cd0d8fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting gradio\n",
+      "  Downloading gradio-3.36.0-py3-none-any.whl (19.8 MB)\n",
+      "                                              0.0/19.8 MB ? eta -:--:--\n",
+      "                                              0.0/19.8 MB ? eta -:--:--\n",
+      "                                             0.0/19.8 MB 325.1 kB/s eta 0:01:01\n",
+      "                                             0.1/19.8 MB 744.7 kB/s eta 0:00:27\n",
+      "                                              0.3/19.8 MB 1.4 MB/s eta 0:00:14\n",
+      "                                              0.5/19.8 MB 2.2 MB/s eta 0:00:09\n",
+      "     -                                        1.0/19.8 MB 3.7 MB/s eta 0:00:06\n",
+      "     --                                       1.0/19.8 MB 3.4 MB/s eta 0:00:06\n",
+      "     ----                                     2.0/19.8 MB 5.9 MB/s eta 0:00:04\n",
+      "     ----                                     2.1/19.8 MB 5.4 MB/s eta 0:00:04\n",
+      "     ------                                   3.0/19.8 MB 6.9 MB/s eta 0:00:03\n",
+      "     -------                                  3.6/19.8 MB 7.4 MB/s eta 0:00:03\n",
+      "     --------                                 4.1/19.8 MB 7.8 MB/s eta 0:00:03\n",
+      "     ---------                                4.7/19.8 MB 8.1 MB/s eta 0:00:02\n",
+      "     ----------                               5.2/19.8 MB 8.3 MB/s eta 0:00:02\n",
+      "     -----------                              5.7/19.8 MB 8.5 MB/s eta 0:00:02\n",
+      "     ------------                             6.3/19.8 MB 8.8 MB/s eta 0:00:02\n",
+      "     -------------                            6.9/19.8 MB 8.9 MB/s eta 0:00:02\n",
+      "     --------------                           7.4/19.8 MB 9.1 MB/s eta 0:00:02\n",
+      "     ----------------                         7.9/19.8 MB 9.2 MB/s eta 0:00:02\n",
+      "     -----------------                        8.5/19.8 MB 9.3 MB/s eta 0:00:02\n",
+      "     ------------------                       9.0/19.8 MB 9.5 MB/s eta 0:00:02\n",
+      "     -------------------                      9.6/19.8 MB 9.6 MB/s eta 0:00:02\n",
+      "     --------------------                     10.1/19.8 MB 9.7 MB/s eta 0:00:02\n",
+      "     --------------------                    10.7/19.8 MB 11.5 MB/s eta 0:00:01\n",
+      "     ---------------------                   11.1/19.8 MB 11.7 MB/s eta 0:00:01\n",
+      "     -----------------------                 11.7/19.8 MB 11.9 MB/s eta 0:00:01\n",
+      "     -----------------------                 12.1/19.8 MB 11.5 MB/s eta 0:00:01\n",
+      "     ------------------------                12.6/19.8 MB 11.7 MB/s eta 0:00:01\n",
+      "     -------------------------               13.1/19.8 MB 11.3 MB/s eta 0:00:01\n",
+      "     --------------------------              13.5/19.8 MB 11.5 MB/s eta 0:00:01\n",
+      "     ---------------------------             13.9/19.8 MB 11.3 MB/s eta 0:00:01\n",
+      "     ----------------------------            14.4/19.8 MB 11.1 MB/s eta 0:00:01\n",
+      "     -----------------------------           14.9/19.8 MB 10.9 MB/s eta 0:00:01\n",
+      "     ------------------------------          15.4/19.8 MB 10.9 MB/s eta 0:00:01\n",
+      "     -------------------------------         15.8/19.8 MB 10.9 MB/s eta 0:00:01\n",
+      "     --------------------------------        16.3/19.8 MB 10.9 MB/s eta 0:00:01\n",
+      "     ---------------------------------       16.8/19.8 MB 10.7 MB/s eta 0:00:01\n",
+      "     ---------------------------------       17.3/19.8 MB 10.7 MB/s eta 0:00:01\n",
+      "     ----------------------------------      17.7/19.8 MB 10.7 MB/s eta 0:00:01\n",
+      "     -----------------------------------     18.2/19.8 MB 10.6 MB/s eta 0:00:01\n",
+      "     ------------------------------------    18.7/19.8 MB 10.6 MB/s eta 0:00:01\n",
+      "     -------------------------------------   19.1/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.7/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.8/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.8/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.8/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.8/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.8/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.8/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.8/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.8/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.8/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.8/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     --------------------------------------  19.8/19.8 MB 10.4 MB/s eta 0:00:01\n",
+      "     ---------------------------------------- 19.8/19.8 MB 6.7 MB/s eta 0:00:00\n",
+      "Collecting aiofiles (from gradio)\n",
+      "  Using cached aiofiles-23.1.0-py3-none-any.whl (14 kB)\n",
+      "Requirement already satisfied: aiohttp in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (3.8.4)\n",
+      "Collecting altair>=4.2.0 (from gradio)\n",
+      "  Using cached altair-5.0.1-py3-none-any.whl (471 kB)\n",
+      "Collecting fastapi (from gradio)\n",
+      "  Downloading fastapi-0.99.1-py3-none-any.whl (58 kB)\n",
+      "                                              0.0/58.4 kB ? eta -:--:--\n",
+      "     ---------------------------------------- 58.4/58.4 kB 3.0 MB/s eta 0:00:00\n",
+      "Collecting ffmpy (from gradio)\n",
+      "  Using cached ffmpy-0.3.0.tar.gz (4.8 kB)\n",
+      "  Preparing metadata (setup.py): started\n",
+      "  Preparing metadata (setup.py): finished with status 'done'\n",
+      "Collecting gradio-client>=0.2.7 (from gradio)\n",
+      "  Using cached gradio_client-0.2.7-py3-none-any.whl (288 kB)\n",
+      "Collecting httpx (from gradio)\n",
+      "  Using cached httpx-0.24.1-py3-none-any.whl (75 kB)\n",
+      "Requirement already satisfied: huggingface-hub>=0.14.0 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (0.16.2)\n",
+      "Requirement already satisfied: jinja2 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (3.1.2)\n",
+      "Requirement already satisfied: markdown-it-py[linkify]>=2.0.0 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (3.0.0)\n",
+      "Requirement already satisfied: markupsafe in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (2.1.1)\n",
+      "Requirement already satisfied: matplotlib in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (3.7.1)\n",
+      "Collecting mdit-py-plugins<=0.3.3 (from gradio)\n",
+      "  Using cached mdit_py_plugins-0.3.3-py3-none-any.whl (50 kB)\n",
+      "Requirement already satisfied: numpy in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (1.23.5)\n",
+      "Requirement already satisfied: orjson in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (3.9.1)\n",
+      "Requirement already satisfied: pandas in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (2.0.2)\n",
+      "Requirement already satisfied: pillow in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (9.4.0)\n",
+      "Requirement already satisfied: pydantic in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (1.10.9)\n",
+      "Collecting pydub (from gradio)\n",
+      "  Using cached pydub-0.25.1-py2.py3-none-any.whl (32 kB)\n",
+      "Requirement already satisfied: pygments>=2.12.0 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (2.15.1)\n",
+      "Collecting python-multipart (from gradio)\n",
+      "  Using cached python_multipart-0.0.6-py3-none-any.whl (45 kB)\n",
+      "Requirement already satisfied: pyyaml in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (6.0)\n",
+      "Requirement already satisfied: requests in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio) (2.29.0)\n",
+      "Collecting semantic-version (from gradio)\n",
+      "  Using cached semantic_version-2.10.0-py2.py3-none-any.whl (15 kB)\n",
+      "Collecting uvicorn>=0.14.0 (from gradio)\n",
+      "  Using cached uvicorn-0.22.0-py3-none-any.whl (58 kB)\n",
+      "Collecting websockets>=10.0 (from gradio)\n",
+      "  Using cached websockets-11.0.3-cp310-cp310-win_amd64.whl (124 kB)\n",
+      "Requirement already satisfied: jsonschema>=3.0 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from altair>=4.2.0->gradio) (4.17.3)\n",
+      "Collecting toolz (from altair>=4.2.0->gradio)\n",
+      "  Downloading toolz-0.12.0-py3-none-any.whl (55 kB)\n",
+      "                                              0.0/55.8 kB ? eta -:--:--\n",
+      "     ---------------------------------------- 55.8/55.8 kB ? eta 0:00:00\n",
+      "Requirement already satisfied: typing-extensions>=4.0.1 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from altair>=4.2.0->gradio) (4.6.3)\n",
+      "Requirement already satisfied: fsspec in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio-client>=0.2.7->gradio) (2023.6.0)\n",
+      "Requirement already satisfied: packaging in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from gradio-client>=0.2.7->gradio) (23.0)\n",
+      "Requirement already satisfied: filelock in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from huggingface-hub>=0.14.0->gradio) (3.9.0)\n",
+      "Requirement already satisfied: tqdm>=4.42.1 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from huggingface-hub>=0.14.0->gradio) (4.65.0)\n",
+      "Requirement already satisfied: mdurl~=0.1 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from markdown-it-py[linkify]>=2.0.0->gradio) (0.1.2)\n",
+      "Collecting linkify-it-py<3,>=1 (from markdown-it-py[linkify]>=2.0.0->gradio)\n",
+      "  Using cached linkify_it_py-2.0.2-py3-none-any.whl (19 kB)\n",
+      "INFO: pip is looking at multiple versions of mdit-py-plugins to determine which version is compatible with other requirements. This could take a while.\n",
+      "Collecting mdit-py-plugins<=0.3.3 (from gradio)\n",
+      "  Using cached mdit_py_plugins-0.3.2-py3-none-any.whl (50 kB)\n",
+      "  Using cached mdit_py_plugins-0.3.1-py3-none-any.whl (46 kB)\n",
+      "  Using cached mdit_py_plugins-0.3.0-py3-none-any.whl (43 kB)\n",
+      "  Using cached mdit_py_plugins-0.2.8-py3-none-any.whl (41 kB)\n",
+      "  Using cached mdit_py_plugins-0.2.7-py3-none-any.whl (41 kB)\n",
+      "  Using cached mdit_py_plugins-0.2.6-py3-none-any.whl (39 kB)\n",
+      "  Using cached mdit_py_plugins-0.2.5-py3-none-any.whl (39 kB)\n",
+      "INFO: pip is looking at multiple versions of mdit-py-plugins to determine which version is compatible with other requirements. This could take a while.\n",
+      "  Using cached mdit_py_plugins-0.2.4-py3-none-any.whl (39 kB)\n",
+      "  Using cached mdit_py_plugins-0.2.3-py3-none-any.whl (39 kB)\n",
+      "  Using cached mdit_py_plugins-0.2.2-py3-none-any.whl (39 kB)\n",
+      "  Using cached mdit_py_plugins-0.2.1-py3-none-any.whl (38 kB)\n",
+      "  Using cached mdit_py_plugins-0.2.0-py3-none-any.whl (38 kB)\n",
+      "INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. If you want to abort this run, press Ctrl + C.\n",
+      "  Using cached mdit_py_plugins-0.1.0-py3-none-any.whl (37 kB)\n",
+      "Collecting markdown-it-py[linkify]>=2.0.0 (from gradio)\n",
+      "  Using cached markdown_it_py-3.0.0-py3-none-any.whl (87 kB)\n",
+      "  Using cached markdown_it_py-2.2.0-py3-none-any.whl (84 kB)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from pandas->gradio) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from pandas->gradio) (2023.3)\n",
+      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from pandas->gradio) (2023.3)\n",
+      "Requirement already satisfied: click>=7.0 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from uvicorn>=0.14.0->gradio) (8.1.3)\n",
+      "Collecting h11>=0.8 (from uvicorn>=0.14.0->gradio)\n",
+      "  Using cached h11-0.14.0-py3-none-any.whl (58 kB)\n",
+      "Requirement already satisfied: attrs>=17.3.0 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from aiohttp->gradio) (22.1.0)\n",
+      "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from aiohttp->gradio) (2.0.4)\n",
+      "Requirement already satisfied: multidict<7.0,>=4.5 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from aiohttp->gradio) (6.0.4)\n",
+      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from aiohttp->gradio) (4.0.2)\n",
+      "Requirement already satisfied: yarl<2.0,>=1.0 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from aiohttp->gradio) (1.9.2)\n",
+      "Requirement already satisfied: frozenlist>=1.1.1 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from aiohttp->gradio) (1.3.3)\n",
+      "Requirement already satisfied: aiosignal>=1.1.2 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from aiohttp->gradio) (1.3.1)\n",
+      "Collecting starlette<0.28.0,>=0.27.0 (from fastapi->gradio)\n",
+      "  Using cached starlette-0.27.0-py3-none-any.whl (66 kB)\n",
+      "Requirement already satisfied: certifi in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from httpx->gradio) (2023.5.7)\n",
+      "Collecting httpcore<0.18.0,>=0.15.0 (from httpx->gradio)\n",
+      "  Downloading httpcore-0.17.3-py3-none-any.whl (74 kB)\n",
+      "                                              0.0/74.5 kB ? eta -:--:--\n",
+      "     ---------------------------------------- 74.5/74.5 kB 4.0 MB/s eta 0:00:00\n",
+      "Requirement already satisfied: idna in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from httpx->gradio) (3.4)\n",
+      "Requirement already satisfied: sniffio in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from httpx->gradio) (1.2.0)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from matplotlib->gradio) (1.1.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from matplotlib->gradio) (0.11.0)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from matplotlib->gradio) (4.40.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from matplotlib->gradio) (1.4.4)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from matplotlib->gradio) (3.0.9)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from requests->gradio) (1.26.16)\n",
+      "Requirement already satisfied: colorama in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from click>=7.0->uvicorn>=0.14.0->gradio) (0.4.6)\n",
+      "Requirement already satisfied: anyio<5.0,>=3.0 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from httpcore<0.18.0,>=0.15.0->httpx->gradio) (3.5.0)\n",
+      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from jsonschema>=3.0->altair>=4.2.0->gradio) (0.18.0)\n",
+      "Collecting uc-micro-py (from linkify-it-py<3,>=1->markdown-it-py[linkify]>=2.0.0->gradio)\n",
+      "  Using cached uc_micro_py-1.0.2-py3-none-any.whl (6.2 kB)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages (from python-dateutil>=2.8.2->pandas->gradio) (1.16.0)\n",
+      "Building wheels for collected packages: ffmpy\n",
+      "  Building wheel for ffmpy (setup.py): started\n",
+      "  Building wheel for ffmpy (setup.py): finished with status 'done'\n",
+      "  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4712 sha256=f93e3038acd8c0ff40f475bf536d01d2ac1b3a1fa1cea06bd7b56ff3422b3f16\n",
+      "  Stored in directory: c:\\users\\hh\\appdata\\local\\pip\\cache\\wheels\\0c\\c2\\0e\\3b9c6845c6a4e35beb90910cc70d9ac9ab5d47402bd62af0df\n",
+      "Successfully built ffmpy\n",
+      "Installing collected packages: pydub, ffmpy, websockets, uc-micro-py, toolz, semantic-version, python-multipart, markdown-it-py, h11, aiofiles, uvicorn, starlette, mdit-py-plugins, linkify-it-py, httpcore, httpx, fastapi, altair, gradio-client, gradio\n",
+      "  Attempting uninstall: markdown-it-py\n",
+      "    Found existing installation: markdown-it-py 3.0.0\n",
+      "    Uninstalling markdown-it-py-3.0.0:\n",
+      "      Successfully uninstalled markdown-it-py-3.0.0\n",
+      "Successfully installed aiofiles-23.1.0 altair-5.0.1 fastapi-0.99.1 ffmpy-0.3.0 gradio-3.36.0 gradio-client-0.2.7 h11-0.14.0 httpcore-0.17.3 httpx-0.24.1 linkify-it-py-2.0.2 markdown-it-py-2.2.0 mdit-py-plugins-0.3.3 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 starlette-0.27.0 toolz-0.12.0 uc-micro-py-1.0.2 uvicorn-0.22.0 websockets-11.0.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "#|default_exp app\n",
+    "# !pip install gradio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7195af18",
+   "metadata": {},
+   "source": [
+    "## Gradio Pets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44eb0ad3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "from fastai.vision.all import *\n",
+    "import gradio as gr\n",
+    "import timm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3295ef11",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "PILImage mode=RGB size=224x224"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "im = PILImage.create('Gold fish.jpg')\n",
+    "im.thumbnail((224,224))\n",
+    "im"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ae2bc6ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#export\n",
+    "learn = load_learner('model.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e0bf9da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "('AMERICAN GOLDFINCH',\n",
+       " tensor(0),\n",
+       " tensor([1.0000e+00, 1.3542e-08, 2.0991e-08, 3.5330e-08, 1.6987e-07, 2.3473e-08]))"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "learn.predict(im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0419ed3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#export\n",
+    "categories = learn.dls.vocab\n",
+    "\n",
+    "def classify_image(img):\n",
+    "    pred,idx,probs = learn.predict(img)\n",
+    "    return dict(zip(categories, map(float,probs)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "762dec00",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'AMERICAN GOLDFINCH': 0.9999998807907104,\n",
+       " 'BARN OWL': 1.3541999521748949e-08,\n",
+       " 'CARMINE BEE-EATER': 2.09909085668869e-08,\n",
+       " 'DOWNY WOODPECKER': 3.5330007364109406e-08,\n",
+       " 'EMPEROR PENGUIN': 1.698742551070609e-07,\n",
+       " 'FLAMINGO': 2.3473393895301342e-08}"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "classify_image(im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "930cf172",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hh\\AppData\\Local\\Temp\\ipykernel_19564\\371635615.py:2: GradioDeprecationWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
+      "  image = gr.inputs.Image(shape=(192, 192))\n",
+      "C:\\Users\\hh\\AppData\\Local\\Temp\\ipykernel_19564\\371635615.py:2: GradioDeprecationWarning: `optional` parameter is deprecated, and it has no effect\n",
+      "  image = gr.inputs.Image(shape=(192, 192))\n",
+      "C:\\Users\\hh\\AppData\\Local\\Temp\\ipykernel_19564\\371635615.py:3: GradioDeprecationWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
+      "  label = gr.outputs.Label()\n",
+      "C:\\Users\\hh\\AppData\\Local\\Temp\\ipykernel_19564\\371635615.py:3: GradioUnusedKwargWarning: You have unused kwarg parameters in Label, please remove them: {'type': 'auto'}\n",
+      "  label = gr.outputs.Label()\n"
+     ]
+    }
+   ],
+   "source": [
+    "#export\n",
+    "image = gr.inputs.Image(shape=(192, 192))\n",
+    "label = gr.outputs.Label()\n",
+    "examples = ['Gold fish.jpg']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f463e23",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7860\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#export\n",
+    "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
+    "intf.launch(inline=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82774c08",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sequential(\n",
+       "  (0): TimmBody(\n",
+       "    (model): ConvNeXt(\n",
+       "      (stem): Sequential(\n",
+       "        (0): Conv2d(3, 96, kernel_size=(4, 4), stride=(4, 4))\n",
+       "        (1): LayerNorm2d((96,), eps=1e-06, elementwise_affine=True)\n",
+       "      )\n",
+       "      (stages): Sequential(\n",
+       "        (0): ConvNeXtStage(\n",
+       "          (downsample): Identity()\n",
+       "          (blocks): Sequential(\n",
+       "            (0): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(96, 96, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=96)\n",
+       "              (norm): LayerNorm((96,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=96, out_features=384, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
+       "                (fc2): Linear(in_features=384, out_features=96, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (shortcut): Identity()\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (1): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(96, 96, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=96)\n",
+       "              (norm): LayerNorm((96,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=96, out_features=384, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
+       "                (fc2): Linear(in_features=384, out_features=96, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (shortcut): Identity()\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (2): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(96, 96, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=96)\n",
+       "              (norm): LayerNorm((96,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=96, out_features=384, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
+       "                (fc2): Linear(in_features=384, out_features=96, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (shortcut): Identity()\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "          )\n",
+       "        )\n",
+       "        (1): ConvNeXtStage(\n",
+       "          (downsample): Sequential(\n",
+       "            (0): LayerNorm2d((96,), eps=1e-06, elementwise_affine=True)\n",
+       "            (1): Conv2d(96, 192, kernel_size=(2, 2), stride=(2, 2))\n",
+       "          )\n",
+       "          (blocks): Sequential(\n",
+       "            (0): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)\n",
+       "              (norm): LayerNorm((192,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=192, out_features=768, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
+       "                (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (shortcut): Identity()\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (1): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)\n",
+       "              (norm): LayerNorm((192,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=192, out_features=768, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
+       "                (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (shortcut): Identity()\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (2): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)\n",
+       "              (norm): LayerNorm((192,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=192, out_features=768, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
+       "                (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (shortcut): Identity()\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "          )\n",
+       "        )\n",
+       "        (2): ConvNeXtStage(\n",
+       "          (downsample): Sequential(\n",
+       "            (0): LayerNorm2d((192,), eps=1e-06, elementwise_affine=True)\n",
+       "            (1): Conv2d(192, 384, kernel_size=(2, 2), stride=(2, 2))\n",
+       "          )\n",
+       "          (blocks): Sequential(\n",
+       "            (0): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
+       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
+       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (shortcut): Identity()\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (1): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
+       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
+       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (shortcut): Identity()\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (2): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
+       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
+       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (shortcut): Identity()\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (3): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
+       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
+       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (shortcut): Identity()\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (4): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
+       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
+       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (shortcut): Identity()\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (5): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
+       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
+       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (shortcut): Identity()\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (6): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
+       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
+       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (shortcut): Identity()\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (7): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
+       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
+       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (shortcut): Identity()\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (8): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)\n",
+       "              (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
+       "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (shortcut): Identity()\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "          )\n",
+       "        )\n",
+       "        (3): ConvNeXtStage(\n",
+       "          (downsample): Sequential(\n",
+       "            (0): LayerNorm2d((384,), eps=1e-06, elementwise_affine=True)\n",
+       "            (1): Conv2d(384, 768, kernel_size=(2, 2), stride=(2, 2))\n",
+       "          )\n",
+       "          (blocks): Sequential(\n",
+       "            (0): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)\n",
+       "              (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
+       "                (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (shortcut): Identity()\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (1): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)\n",
+       "              (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
+       "                (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (shortcut): Identity()\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "            (2): ConvNeXtBlock(\n",
+       "              (conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)\n",
+       "              (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
+       "              (mlp): Mlp(\n",
+       "                (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
+       "                (act): GELU()\n",
+       "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
+       "                (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
+       "                (drop2): Dropout(p=0.0, inplace=False)\n",
+       "              )\n",
+       "              (shortcut): Identity()\n",
+       "              (drop_path): Identity()\n",
+       "            )\n",
+       "          )\n",
+       "        )\n",
+       "      )\n",
+       "      (norm_pre): Identity()\n",
+       "      (head): NormMlpClassifierHead(\n",
+       "        (global_pool): SelectAdaptivePool2d (pool_type=avg, flatten=Identity())\n",
+       "        (norm): LayerNorm2d((768,), eps=1e-06, elementwise_affine=True)\n",
+       "        (flatten): Flatten(start_dim=1, end_dim=-1)\n",
+       "        (pre_logits): Identity()\n",
+       "        (drop): Dropout(p=0.0, inplace=False)\n",
+       "        (fc): Identity()\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (1): Sequential(\n",
+       "    (0): AdaptiveConcatPool2d(\n",
+       "      (ap): AdaptiveAvgPool2d(output_size=1)\n",
+       "      (mp): AdaptiveMaxPool2d(output_size=1)\n",
+       "    )\n",
+       "    (1): fastai.layers.Flatten(full=False)\n",
+       "    (2): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "    (3): Dropout(p=0.25, inplace=False)\n",
+       "    (4): Linear(in_features=1536, out_features=512, bias=False)\n",
+       "    (5): ReLU(inplace=True)\n",
+       "    (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "    (7): Dropout(p=0.5, inplace=False)\n",
+       "    (8): Linear(in_features=512, out_features=6, bias=False)\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "m = learn.model\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10d7900d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Parameter containing:\n",
+       " tensor([ 1.2545e+00,  1.9196e+00,  1.2201e+00,  1.0390e+00, -1.6480e-03,\n",
+       "          7.6568e-01,  8.8830e-01,  1.6302e+00,  7.0489e-01,  3.2909e+00,\n",
+       "          7.8756e-01, -1.2321e-03,  1.0008e+00, -1.1701e-03,  3.2963e+00,\n",
+       "          7.5332e-04,  1.9848e+00,  1.0214e+00,  4.4530e+00,  2.5485e-01,\n",
+       "          2.7261e+00,  9.2749e-01,  1.2365e+00,  4.6786e-03,  1.7861e+00,\n",
+       "          5.4500e-01,  4.6252e+00,  1.1814e-02, -8.0696e-04,  3.4503e+00,\n",
+       "          1.3520e+00,  4.1267e+00,  2.6889e+00,  4.1214e+00,  3.4020e+00,\n",
+       "          8.4680e-01,  7.3639e-01,  3.9801e+00,  1.2857e+00,  6.4153e-01,\n",
+       "          2.6896e+00,  1.1183e+00,  1.1701e+00,  5.5256e-01,  2.3371e+00,\n",
+       "          2.6110e-04,  9.7016e-01,  2.1527e-03,  1.1990e+00,  1.7883e+00,\n",
+       "          4.0231e-01,  4.4849e-01,  9.7238e-01,  3.9889e+00,  6.5864e-01,\n",
+       "          6.8973e-01,  9.8424e-01,  2.7063e+00,  1.2161e+00,  7.5966e-01,\n",
+       "          3.3019e+00,  1.6209e+00,  9.5479e-01,  2.1214e+00,  6.2982e-01,\n",
+       "          4.0345e+00,  8.9406e-01, -1.5776e-03,  4.0855e+00,  1.0646e+00,\n",
+       "          1.3953e+00,  1.6694e+00,  7.7575e-04,  7.6740e-01,  8.8671e-01,\n",
+       "          6.4291e-01,  1.3444e+00,  7.1629e-01,  5.4538e-01,  2.0897e+00,\n",
+       "          1.1951e+00,  3.0924e-01,  2.9660e-01,  1.4705e+00,  4.0818e+00,\n",
+       "         -1.9466e-03,  1.1466e+00,  3.8855e+00,  3.6003e+00,  4.8230e-01,\n",
+       "          2.1677e-01,  1.2715e-03,  6.5100e-01,  3.0062e+00,  3.0463e+00,\n",
+       "          6.9039e-03], requires_grad=True),\n",
+       " Parameter containing:\n",
+       " tensor([-9.7076e-02, -4.1602e-02,  4.1634e+00, -1.0902e-02,  2.5195e-03,\n",
+       "         -2.6698e-02, -3.1112e-02, -8.0897e-02, -1.3977e-01, -6.1426e-02,\n",
+       "          3.2092e-01, -3.3970e-01, -5.7320e-02, -5.0723e-03, -4.5248e-02,\n",
+       "         -2.6788e-02, -4.0929e-02, -3.8162e-02,  8.6140e-03, -2.3497e-02,\n",
+       "          9.3659e-03, -1.6219e-01, -4.0165e+00,  5.3178e-01, -5.3446e-01,\n",
+       "          2.8025e+00,  3.7767e-02, -8.2848e-03, -1.0448e-03, -1.1741e-01,\n",
+       "         -1.3899e-01,  1.9646e-02, -9.6837e-02, -1.3024e-01, -1.9224e-01,\n",
+       "         -6.6514e-02, -3.5839e-02, -1.2878e-01,  1.5046e-01,  7.7289e-04,\n",
+       "         -6.4686e-02,  5.7553e-02, -9.2985e-02, -1.1460e+00, -5.4285e-02,\n",
+       "         -5.4245e-03, -1.8200e-01,  2.2406e-02,  3.9471e-02, -5.9339e-02,\n",
+       "         -4.1768e-02, -5.6190e-02, -4.3428e-02, -1.2965e-02, -1.1264e-01,\n",
+       "          4.9255e-03, -3.7258e-02, -1.5767e-01, -9.7796e-02, -1.8840e-01,\n",
+       "         -1.1216e-01, -1.8182e-01, -3.2907e-02, -2.8298e-02,  1.4188e+00,\n",
+       "         -3.3801e-02, -4.1863e-02, -2.6832e-01, -4.7449e-02, -4.5676e-05,\n",
+       "          2.6773e-01,  1.8772e-01,  6.9789e-01, -3.0746e-01,  8.3766e-02,\n",
+       "         -1.0845e+00,  1.5364e-02, -4.4824e-02, -7.7240e-02, -6.7920e-02,\n",
+       "         -1.3150e-01, -1.6358e-02, -1.7351e-02, -3.9939e-02, -7.2383e-02,\n",
+       "          1.0359e-02, -5.8904e-02, -3.8567e-02, -7.9783e-02, -7.3791e-02,\n",
+       "         -1.0318e-02, -3.7690e-01, -9.9740e-03, -2.7374e-02, -6.3630e-02,\n",
+       "          1.5712e-03], requires_grad=True)]"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "l = m.get_submodule('0.model.stem.1')\n",
+    "list(l.parameters())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "008537b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Parameter containing:\n",
+       " tensor([[ 2.2773e-02, -1.6051e-03,  4.0450e-02,  ...,  1.7370e-03,\n",
+       "          -4.5070e-02,  8.0949e-03],\n",
+       "         [-1.4383e-01,  1.6965e-02,  2.5983e-02,  ...,  1.2606e-02,\n",
+       "          -1.0443e-01,  5.6370e-02],\n",
+       "         [-6.5471e-02, -3.2719e-02,  5.6796e-03,  ..., -4.1571e-02,\n",
+       "           6.5921e-02, -4.0347e-02],\n",
+       "         ...,\n",
+       "         [-8.8080e-03,  6.9815e-02,  7.1424e-05,  ...,  4.0177e-03,\n",
+       "           4.1478e-02, -1.9052e-02],\n",
+       "         [ 2.0792e-03,  3.2267e-02,  2.9801e-02,  ..., -2.9897e-02,\n",
+       "          -3.0278e-02,  5.5432e-02],\n",
+       "         [ 1.2097e-01, -3.5444e-02, -4.6078e-03,  ..., -6.3829e-03,\n",
+       "           2.3691e-02, -1.1242e-02]], requires_grad=True),\n",
+       " Parameter containing:\n",
+       " tensor([-0.4047, -0.7418, -0.4235, -0.1650, -0.3028, -0.1898, -0.5534, -0.6271,\n",
+       "         -0.3008, -0.4254, -0.5997, -0.4107, -0.2172, -1.7935, -0.3170, -0.1163,\n",
+       "         -0.4482, -0.2846, -0.4342, -0.4945, -0.4065, -1.1402, -0.6754, -1.7237,\n",
+       "         -0.2955, -0.2654, -0.2187, -0.3914, -0.4150, -0.4772,  0.2365, -0.7542,\n",
+       "         -0.5852, -0.1820, -1.5272, -0.3626, -2.4689, -2.3461, -0.6109, -0.4115,\n",
+       "         -0.6964, -0.5764, -0.5878, -0.0318, -2.0354, -0.2859, -0.3953, -0.8402,\n",
+       "         -2.2398, -1.0876, -0.2295, -0.9004, -0.7584, -0.8833, -0.3755, -0.4549,\n",
+       "         -0.3835, -0.4047, -2.0231, -1.0263, -0.4106, -1.1564, -0.2224, -0.4250,\n",
+       "         -0.2494, -0.4222, -0.0975, -1.4017, -0.6887, -0.4370, -0.2932, -0.4641,\n",
+       "         -0.4958, -1.2534, -1.0720, -1.2966, -0.6276, -1.4161, -2.3080, -2.4538,\n",
+       "         -0.4259, -0.9987, -0.4638, -0.3147, -0.2416, -0.8744, -0.2829, -1.4208,\n",
+       "         -0.3257, -0.3202, -0.0602, -0.1896, -0.2497, -0.6129, -0.2976, -2.1465,\n",
+       "         -0.4128, -0.3675, -1.9815, -0.3815, -0.3785, -0.2292, -0.3700, -0.3256,\n",
+       "         -0.5584, -2.4192, -0.4590, -1.7748, -0.3996, -0.4092, -0.3518, -0.5332,\n",
+       "         -1.6534, -1.8191,  0.6263, -0.4058,  0.5872, -2.2074, -0.2438, -2.4540,\n",
+       "         -0.2283, -0.6865,  0.6988,  0.6477, -0.6445, -0.3454, -0.3275, -0.5701,\n",
+       "         -0.5173, -0.2774, -0.4090, -0.3018, -0.4874, -0.4954, -0.4073, -0.4356,\n",
+       "         -0.5103, -0.4128, -2.0919, -0.2825, -0.5830, -1.5834,  0.6139, -0.8506,\n",
+       "         -0.4669, -2.1358, -0.3417, -0.3766, -0.3345, -0.3961, -0.3886, -0.5668,\n",
+       "         -0.2224, -1.3059, -0.4601, -0.3928, -0.4665, -0.4214, -0.4755, -0.2865,\n",
+       "         -1.5804, -0.1787, -0.4368, -0.3173,  1.5732, -0.4046, -0.4839, -0.2576,\n",
+       "         -0.5611, -0.4265, -0.2578, -0.3176, -0.4620, -1.9553, -1.9146, -0.3961,\n",
+       "          0.3988, -2.3520, -0.9689, -0.2831, -1.9000, -0.4180,  0.0160, -1.1111,\n",
+       "         -0.4924, -0.3177, -1.8912, -0.3101, -0.8137, -2.3345, -0.3843, -0.3847,\n",
+       "         -0.1974, -0.4444, -1.6233, -2.5485, -0.3178, -1.2715, -1.1479,  0.6149,\n",
+       "         -0.3749, -0.3952, -2.0747, -0.4657, -0.3782, -0.4958, -0.3281, -1.9219,\n",
+       "         -2.0018, -0.5307, -0.2555, -1.1161, -0.3516, -2.2185, -1.1394,  0.5366,\n",
+       "         -0.3218, -2.0387, -0.4656,  0.1850, -0.5830, -0.3129,  0.6182, -0.2124,\n",
+       "         -2.3538, -0.9700, -0.9784, -0.3668, -0.4503, -1.9564, -0.2662, -1.1754,\n",
+       "         -0.4200, -0.9024, -0.3604, -0.5172, -1.1882, -0.4191, -0.4770, -1.5558,\n",
+       "         -0.4011, -0.6518, -0.4817, -0.2422,  0.6909, -0.5080, -0.4303, -0.6068,\n",
+       "         -0.4001, -0.3329, -0.3596, -1.6108, -0.2371, -0.2467, -0.4545,  0.1808,\n",
+       "         -0.3225, -0.3918, -0.3514, -0.3756, -1.2178, -0.4000, -0.3578, -0.2883,\n",
+       "         -1.7485, -0.2364, -0.1599, -0.2640, -0.9769, -1.3066, -0.4148, -0.2663,\n",
+       "         -0.3933, -0.4628, -0.2174,  0.2141, -0.5733, -0.2766, -0.3658, -0.5171,\n",
+       "         -0.3484, -0.3365, -0.6445,  0.6866, -0.3738, -0.2902, -2.0863, -0.4882,\n",
+       "         -0.2597, -1.0497, -1.6616, -0.3399, -0.5111, -0.5661, -0.3029, -0.5048,\n",
+       "         -0.2877, -0.2841, -0.1981, -0.6910, -0.2872, -2.1120, -0.8928, -0.2299,\n",
+       "         -1.5010, -0.4734, -2.2293, -0.4020, -0.2925, -0.4198,  0.6646, -0.3047,\n",
+       "         -0.1687, -0.3750, -0.6434, -2.3348, -0.3102, -1.2732, -0.8192, -1.0592,\n",
+       "         -0.0931, -1.6385,  0.3426, -0.8484, -0.4910, -0.5002, -1.0631, -0.3532,\n",
+       "         -1.1562, -0.3843, -0.3172, -0.6432, -0.9083, -0.6567, -0.6489,  0.6336,\n",
+       "         -0.2663, -1.3203, -1.1623, -1.2032, -2.0576, -0.3001, -1.3597, -0.4614,\n",
+       "         -0.5024, -0.4949, -0.3158, -0.3273, -0.2668, -0.4280, -0.3296, -0.3011,\n",
+       "         -1.6635,  0.6434, -0.9455,  0.6097, -0.4234,  0.3918, -0.4943, -0.4285,\n",
+       "         -0.2588, -0.4951, -2.1992, -0.2601, -0.3935, -0.4564, -0.5817, -0.3487,\n",
+       "         -0.7372, -0.3589, -0.4894, -2.0108,  0.4556, -0.8057, -1.7749, -0.3511,\n",
+       "         -0.5359, -0.2100, -0.3956, -0.4780, -1.1457, -0.3976, -2.2114, -0.2840],\n",
+       "        requires_grad=True)]"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "l = m.get_submodule('0.model.stages.0.blocks.1.mlp.fc1')\n",
+    "list(l.parameters())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff9d5c73",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..2602047fd65ea3d1d583b7f6e1212f0746183136
--- /dev/null
+++ b/app.py
@@ -0,0 +1,27 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: . (unless otherwise specified).
+
+__all__ = ['learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']
+
+# Cell
+from fastai.vision.all import *
+import gradio as gr
+import timm
+
+# Cell
+learn = load_learner('model.pkl')
+
+# Cell
+categories = learn.dls.vocab
+
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float,probs)))
+
+# Cell
+image = gr.inputs.Image(shape=(192, 192))
+label = gr.outputs.Label()
+examples = ['Gold fish.jpg']
+
+# Cell
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch()
\ No newline at end of file
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/001.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/001.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..36df597a6f51ec5ce255c180a5089b18a185f081
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/001.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/002.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/002.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..75f240a0769d4003f584d4aec95dc2a8a0fca367
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/002.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/003.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/003.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..da55ea1e0b34e4cc302c3e8802221de3387757c9
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/003.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/004.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/004.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7e9487b534171d3f12275401f0154b693334c2b4
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/004.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/005.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/005.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..77d0be962802bd477e3e6dd2f83fff2f82bbcea6
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/005.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/006.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/006.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3b3bfd562ba1695c9a80949e4a28c0155a5d7333
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/006.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/007.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/007.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d26f4044b9b4a39d12b568b1860c14f1ac1e9d15
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/007.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/008.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/008.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5934e5d9dd142beb7b426d906039c47e33d9c7eb
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/008.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/009.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/009.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a89fc727e95bc97fab146e69f00d1bd911577883
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/009.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/010.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/010.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..00e978302ccf05ec3bb889e8bf4e9a0caec41494
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/010.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/011.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/011.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..30c00e6f866d34a3d2bbe3f0ecfc0e7b5d0c1914
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/011.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/012.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/012.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..586333dff6b2b53e657d01ac178ce7283d4d55aa
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/012.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/013.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/013.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d01e285d5f781ebb1b124cdfc022772984dd9e72
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/013.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/014.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/014.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a465f94ae46c301643de6d120cb796db3345abe3
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/014.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/015.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/015.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..26d4619a864cf2f6ef8b28e35e70514e0cbc96d0
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/015.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/016.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/016.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3b3a4abfa1d9d993f5bce030b06dad5d30974684
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/016.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/017.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/017.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..51b201c30b4852c46944fa7cf3fe5681d23e06ac
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/017.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/018.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/018.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..17e28774035269a10a599ac714b5f782ddaf3452
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/018.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/019.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/019.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..68f371be3434f0b77c2f94ef5d249a5a5c7a179b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/019.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/020.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/020.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e4ccee373c0e0063e94214751e3af5152b14f414
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/020.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/021.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/021.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3e4d5fb321a9c8daedfa5ebf99100eb5dddb491b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/021.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/022.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/022.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f04cd47fa5cfca06d011a90e4dcd30f77eae3e3d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/022.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/023.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/023.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0422e7a7f4a8d804984c2fda75f8e0e889bd233b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/023.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/024.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/024.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..166a92c8f991a4523a0378ee9dea8d16a7b799b3
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/024.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/025.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/025.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ef418a6d822c0017fc87c3d5783e478b4fcad216
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/025.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/026.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/026.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..11b231648a607d692459d7749589017460675b46
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/026.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/027.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/027.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4ab2097a6932cb10818f6a7b0d1e14d3d5e417ed
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/027.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/028.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/028.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..69a2633e25a0d8bf7afe5ca3333e090c84035145
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/028.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/029.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/029.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e159610ee9cef860b7585bf255af83393940bf36
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/029.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/030.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/030.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..bd565658667f6e68faaf95e86121013110ce5df8
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/030.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/031.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/031.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4e6c84ab78e581721c05b4858fccada9e1b7a1a7
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/031.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/032.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/032.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..404fb9ce78b4e102fd5bd23f17aba5616a7993a3
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/032.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/033.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/033.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fa3c493b33f515a2ccb7ced2207843d4c5329d26
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/033.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/034.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/034.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4159506d83d0bd31607b2a04e48d473366e21d3c
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/034.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/035.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/035.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9c179adc60f69a81ecf740089cbc8d3d7941d1c4
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/035.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/036.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/036.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e13b69d9967e8cdf665ee26fb2506ceb10c8a992
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/036.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/037.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/037.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..00af0abb978d19ef8dc5b95f39f7a5fb741a041a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/037.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/038.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/038.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..941f6a674bb96f57c6084637b0bd54ef5b7a6d63
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/038.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/039.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/039.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..95b7577e4388338c612b704c7123e69336536bca
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/039.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/040.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/040.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6b2f5a24103c0bc6a37036e34b0ed740e3b7b19f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/040.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/041.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/041.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d9e6b43d6136a73bde39e0f098a83a2f1eff1b4d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/041.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/042.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/042.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..eb7d64364ab64d8d0f9af258b463b33ad5f3dac1
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/042.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/043.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/043.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5efe05bea62034ea6df374891e6f9c422c06d837
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/043.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/044.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/044.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b9f27203eee504cf379164f880ff255886b56c6e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/044.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/045.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/045.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d1aefe8945dd4c7051540db79788027d40bf0e3a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/045.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/046.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/046.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..cba67da06839569ac1f831b86643a48e7feb68e2
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/046.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/047.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/047.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..57a3360693a88259b3d13d84ebcec4a97754406d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/047.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/048.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/048.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..821a385980b9828e1760d49ca7d51adedb3f4298
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/048.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/049.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/049.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..500da342f69d730ff928df46b34caabc5ff37bbc
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/049.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/050.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/050.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3b1fc8f26a1c39cdf7333b71ead0479029e45849
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/050.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/051.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/051.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5c0cffb3999ce1de7f7a145b2e527972c9da8913
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/051.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/052.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/052.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..855296c228aa8c368d5f62d67829e87d18f07c19
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/052.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/053.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/053.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ca516e28b1123154a43f2722a5927fd59a82659f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/053.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/054.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/054.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a3b6f5b21ee0ff46c5face911f572b3b6b8b75e5
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/054.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/055.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/055.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..63d3f30a126773258f8ba36967f5b85a47c41c4e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/055.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/056.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/056.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a388f2d5f4e30470ca3fe35396425688106a8ac2
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/056.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/057.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/057.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4cd690d8a6fa7aa17c0b6b1eb7904a7a9ecb5844
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/057.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/058.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/058.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..78547b03694b8f7219c029c802d27442cd508f35
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/058.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/059.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/059.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b13ebbd3fba157d703e1ee6f5eec00956809a1dd
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/059.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/060.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/060.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1f4987b1d4668ff96346074b646ec520166f752e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/060.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/061.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/061.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ff42d95d880d0656b677fd407491ab77c236fc3c
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/061.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/062.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/062.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ff559d4a4ddec5293c01a4e0afcf8814dc00072b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/062.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/063.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/063.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..53a1fec023cbf643bd9490691acfdd907a27ad16
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/063.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/064.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/064.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..37dd0a7ed191a0759ce554158a4b176b68a37d6a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/064.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/065.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/065.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b21b4f507507a714ca75da7b086f6bb313acbc01
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/065.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/066.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/066.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..bac9162a723f0477c7b4a81f052dc76bd0c99d32
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/066.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/067.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/067.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f781c28c683213b7ee8893c7ce82218fe794d820
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/067.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/068.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/068.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..627b0360d6433191c8ce679bcc79598e8d780cae
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/068.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/069.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/069.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b4ce931707c9e0179202d2d10f18a729ad9cb649
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/069.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/070.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/070.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d857e72c2f51ebd20ed60e62c99b8f383e35aeb0
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/070.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/071.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/071.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ff01663cbb28273e2e01778f01a10ec189efa670
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/071.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/072.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/072.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b9a5053e324c5b66e2df9354da540b2dc43c1644
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/072.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/073.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/073.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8ba0ea8acbfe06f764e1eb5a6bbe7d8ebdd1d655
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/073.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/074.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/074.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..00633762f26d4e5cc33a368f26f396383887047c
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/074.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/075.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/075.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c9d015ae7ba423f802c94c2bfd70663dd040ecca
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/075.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/076.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/076.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f858e41af7d47aea2c6a8ce7ca6281da4ef31d5b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/076.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/077.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/077.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b9434e28b346c2e76eeb3ffd3d23d3d209382a2d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/077.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/078.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/078.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6549b919af693bcde63e9f84f6409a3e2d03b8ca
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/078.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/079.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/079.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9743f829d6cae7b8a3550ee0530a1817c9d8323a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/079.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/080.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/080.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..71ef5b447b6722c79ff0805e121568cf07486ad0
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/080.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/081.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/081.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b6eb7f1f39ffc0441bcbf76d2da810a39bf1ce67
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/081.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/082.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/082.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0ad48a78cd90ba3b8acc386f1afc6f1a5f778556
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/082.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/083.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/083.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f87365f73d7af61e438b2c8dcc395ca09acb3289
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/083.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/084.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/084.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1b1b1d64967e7964c82a3c96c6d2b852e2acfb03
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/084.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/085.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/085.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..cdc31ad9d91cbd02b403ae7599798c7f575e665f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/085.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/086.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/086.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..423bb03a4fe14cda6bfb9f1be07cd010d9c72be7
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/086.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/087.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/087.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c2242bb5ecc1a32bcd9763f43c91b0948c81b41b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/087.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/088.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/088.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ed1852511004ba7cbfc5ba1dc6e15b579396a9f8
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/088.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/089.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/089.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8191f33d7e42c0879b12027a6ecef9aaa1505353
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/089.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/090.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/090.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..af493904f064c8b24d8f539f06a0de93d6976317
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/090.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/091.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/091.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..979671306d310bd75f5c862adc568ca261150a6d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/091.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/092.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/092.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b1499d6c546bb7e3c0299339073d86a2e9c87bad
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/092.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/093.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/093.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..af106ccad78c840375ac81374b8df9c19f82b343
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/093.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/094.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/094.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b8773dc84c03624fcfb296d961d21a49d3a133b3
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/094.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/095.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/095.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d7d4aeb40444bded86997aa2f8112279ae7f5473
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/095.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/096.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/096.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..da603972c9283efadabf340c1dbc7738968ee889
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/096.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/097.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/097.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..82a7fb90dfc66e97d29bf0e08451a04e8ad1b98d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/097.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/098.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/098.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e3055dbb62357bbd61c1cb3454f024b94695951f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/098.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/099.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/099.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2d4e0c9989595a5cdb5eb0f5734c0f23c7e06566
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/099.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/100.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/100.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..32ada215f15d957bc53a45ea7c070e456e7bc633
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/100.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/101.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/101.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1e5f90793529336d303309de802014857a7b7556
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/101.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/102.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/102.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4c01d4e1440fedb99684c6ee0964d142fe1569be
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/102.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/103.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/103.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3c6d22aad9a9049ee07689659369afaa225fbb39
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/103.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/104.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/104.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..552e3e4f629389b7032045bc58ea2707fa9914e1
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/104.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/105.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/105.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..406966d39d70bca1d671d6fd7afc6e73547da99a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/105.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/106.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/106.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..01310b547b1253c9f0800b7f610d3af33a63d82a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/106.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/107.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/107.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a5e57f34f03b21f140597ad229fea3d8b1cc408c
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/107.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/108.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/108.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a485c88e95310ad0c953c83d01eeae7acf58be38
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/108.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/109.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/109.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1a3356d3139584ed705e8691c9dc99a2cfff25e5
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/109.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/110.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/110.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..86b23c4c43cf7bdd712ffe1776261ada851ffc4c
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/110.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/111.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/111.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d484668cc73c3ef89675bf43d917fae426fcf7a8
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/111.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/112.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/112.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..21bda2f195fb5e0690d003bce6d41158ecfc9a6b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/112.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/113.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/113.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5bb246ac0539eaf2286b826906b5dd1605fe5af7
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/113.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/114.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/114.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..05f3559691cc32bc2a5388c68c8bebc168c91b00
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/114.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/115.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/115.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4c69e9eed1ea9c60c2e21d84de04486c87029e1e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/115.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/116.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/116.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c540c797206dd12e6e71340f9a0f34e7ef7e9fa4
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/116.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/117.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/117.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..33eed4fd34b70ccdb46a1f3c69c3b6cffce48019
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/117.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/118.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/118.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3daad8da23c3325111436845c9da836858847833
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/118.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/119.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/119.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..00862404d602853f20711af984d4827160838f2e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/119.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/120.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/120.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..042f0f41c56e0fc00b81bf1a8762aa41d2c238cf
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/120.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/121.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/121.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..342a52508d67a21245bd26fd4ce6b710c3e717e1
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/121.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/122.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/122.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8b457ad499b4f41dddd6c82dd7a064009d65c8d2
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/122.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/123.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/123.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5c9a6f037330922b961fd9c92304e4fb208ffcee
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/123.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/124.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/124.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1e666c1efd2e30774c5b1c567667c1e47f35e923
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/124.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/125.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/125.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ccea8caf7580253b1719b1a689d23e174aab8bf5
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/125.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/126.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/126.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a4d97de6dc71944dc3cd5a59637d202d8c975842
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/126.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/127.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/127.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..294df9c2e1cdc27ae9973f75312070c05c108396
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/127.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/128.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/128.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..de0ebf55645af6aa81e5f4a6c39f2a8e48c490a3
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/128.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/129.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/129.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4faa3e24f9f7d3390bf6047309e01d24851ad8af
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/129.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/130.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/130.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..73fb3bc7e98e3231a4882c0631a6e1ee955007e5
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/130.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/131.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/131.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..16d11a33b62c5322f12896a094f54b5b27d03625
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/131.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/132.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/132.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9f69251f017a6ec00234c04d2ecff09e327f8644
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/132.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/133.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/133.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0165ace68e0fa5320b49e0824d956678e516667a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/133.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/134.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/134.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..86ef92277dc4100b8902af538eb266909c0094c1
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/134.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/135.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/135.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3f11e21d37d5ff4fc1593e10798f7c6adb112be5
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/135.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/136.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/136.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c835f5c7796afd18917b97cf5f1ccdfa4a906f93
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/136.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/137.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/137.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..22f1d00d59dc60a249837b2c06a68588a1649af1
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/137.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/138.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/138.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6e11c39d2687afb55ad9d724331149593bea32e0
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/138.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/139.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/139.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b062793965c3227058f6672106d0075b4b2d219f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/139.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/140.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/140.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d4b056f29c09b51ba4f9c106d1f9a9beadc6f275
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/140.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/141.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/141.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e378401e5ae1e56741b9d9eab0452758f017821e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/141.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/142.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/142.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..099ca7d011a64ad677377774c3bb9d3a537d9d0e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/142.jpg differ
diff --git a/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/143.jpg b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/143.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..96c5082c9a172addae3bed44418adf8a6ff86858
Binary files /dev/null and b/data/Bird_Speciees_Dataset/AMERICAN GOLDFINCH/143.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/001.jpg b/data/Bird_Speciees_Dataset/BARN OWL/001.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..bb539268fa82682b0275bdfd31a2965edea32471
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/001.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/002.jpg b/data/Bird_Speciees_Dataset/BARN OWL/002.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6305f9a2ea706df784d157d89a8590842ca6cca4
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/002.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/003.jpg b/data/Bird_Speciees_Dataset/BARN OWL/003.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a932b27f15e1f8d4ece3e708be65d1620b2909e1
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/003.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/004.jpg b/data/Bird_Speciees_Dataset/BARN OWL/004.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..181914e34e96116cfde6c3353cf93436d0279c2b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/004.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/005.jpg b/data/Bird_Speciees_Dataset/BARN OWL/005.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6a560d1143f43f1abd512479c279e53caea9f892
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/005.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/006.jpg b/data/Bird_Speciees_Dataset/BARN OWL/006.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..51540a2af6d40432efb453fd6b87cf93719a18f7
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/006.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/007.jpg b/data/Bird_Speciees_Dataset/BARN OWL/007.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f6af2b85c83f85c862f92cd015efe4a58d82f237
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/007.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/008.jpg b/data/Bird_Speciees_Dataset/BARN OWL/008.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..63529fd46d6e031bf8afc472314f378827751afd
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/008.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/009.jpg b/data/Bird_Speciees_Dataset/BARN OWL/009.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2457d77466f0c77406c39a1e2b74c0ce46a05b72
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/009.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/010.jpg b/data/Bird_Speciees_Dataset/BARN OWL/010.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3aeb5369519bc1d836b3af05c76500718b7157c9
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/010.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/011.jpg b/data/Bird_Speciees_Dataset/BARN OWL/011.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a0a8944d53d70a45bda5372a0e4c09fa74b31d10
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/011.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/012.jpg b/data/Bird_Speciees_Dataset/BARN OWL/012.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f2c73855711b61a3708db100a2fbd084abf48dff
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/012.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/013.jpg b/data/Bird_Speciees_Dataset/BARN OWL/013.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b72672810cc1cbb4e03ea491e5b6736771d3a9b7
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/013.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/014.jpg b/data/Bird_Speciees_Dataset/BARN OWL/014.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..004b484f0052174ed127c63ed305838fee5bf68a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/014.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/015.jpg b/data/Bird_Speciees_Dataset/BARN OWL/015.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..104720059d2c87b5b3f95cf3a710b223cdc3da44
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/015.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/016.jpg b/data/Bird_Speciees_Dataset/BARN OWL/016.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0f31464b6168e0240c9914eff251deef2b1e8622
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/016.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/017.jpg b/data/Bird_Speciees_Dataset/BARN OWL/017.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..813ff5913db87725115008624f4a4d7d7ef1e79d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/017.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/018.jpg b/data/Bird_Speciees_Dataset/BARN OWL/018.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ab550c9c452de2a60498a4dca44e1fd5639b99ea
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/018.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/019.jpg b/data/Bird_Speciees_Dataset/BARN OWL/019.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ec5c06bdf10da398563ccc4e2c96308c4c1f829a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/019.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/020.jpg b/data/Bird_Speciees_Dataset/BARN OWL/020.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..930ab857ece20fd2a7242be362e87cd355e6680d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/020.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/021.jpg b/data/Bird_Speciees_Dataset/BARN OWL/021.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0089fa7b7475630af5d4232a52dc52af8b5bed63
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/021.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/022.jpg b/data/Bird_Speciees_Dataset/BARN OWL/022.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a37d93c37d799721266eef49906607b7ecfb275d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/022.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/023.jpg b/data/Bird_Speciees_Dataset/BARN OWL/023.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6c469773e90da0d6ec0ff189fb4862cfef6629bf
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/023.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/024.jpg b/data/Bird_Speciees_Dataset/BARN OWL/024.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7236f196ef83c05edc2c228f5f842a4292dca7e6
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/024.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/025.jpg b/data/Bird_Speciees_Dataset/BARN OWL/025.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..941a2747f0e30a95f4936b267c3ee6a650f33ccb
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/025.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/026.jpg b/data/Bird_Speciees_Dataset/BARN OWL/026.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..47d902849dc36ec05ceb20e6fd80c92fd3cc17ef
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/026.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/027.jpg b/data/Bird_Speciees_Dataset/BARN OWL/027.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..98dc88a0167d959220d22f7b59a8dc7fa8d71fec
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/027.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/028.jpg b/data/Bird_Speciees_Dataset/BARN OWL/028.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f5c60874c5118e1453d136c713ba3a542d70eb79
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/028.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/029.jpg b/data/Bird_Speciees_Dataset/BARN OWL/029.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4c02b9f554e31380f885329353242e04ef1b0fd6
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/029.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/030.jpg b/data/Bird_Speciees_Dataset/BARN OWL/030.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..08d82399bf4d2e5108e689e432c72283f69b2d06
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/030.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/031.jpg b/data/Bird_Speciees_Dataset/BARN OWL/031.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..41912c45d9db90154a5016b0456262b732dd2b4e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/031.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/032.jpg b/data/Bird_Speciees_Dataset/BARN OWL/032.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8f7abf5b67906bca55e5f90d9a15095f87b1f9da
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/032.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/033.jpg b/data/Bird_Speciees_Dataset/BARN OWL/033.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..cdf6ead2c124e4a4fff55d4a203127aebeab5b4b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/033.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/034.jpg b/data/Bird_Speciees_Dataset/BARN OWL/034.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fae9cc71af982b6c7125d2e8b407bbe3b9865e74
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/034.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/035.jpg b/data/Bird_Speciees_Dataset/BARN OWL/035.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..da659c06c1f54323851cc740d9f123e4a33db6fe
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/035.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/036.jpg b/data/Bird_Speciees_Dataset/BARN OWL/036.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..31e1e12122281115d52f5a42f3c7f074a6f70d65
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/036.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/037.jpg b/data/Bird_Speciees_Dataset/BARN OWL/037.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c49c057715fea71c208353c3d0bb3f5746c863e3
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/037.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/038.jpg b/data/Bird_Speciees_Dataset/BARN OWL/038.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5c943857d96af69be13b3fcf950bdf3740f5de32
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/038.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/039.jpg b/data/Bird_Speciees_Dataset/BARN OWL/039.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8c5197f03cd20eee65b867591c2bb357f868cecf
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/039.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/040.jpg b/data/Bird_Speciees_Dataset/BARN OWL/040.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d135c8f0e4792bd579d1fbc04dfe6abe3b4fccee
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/040.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/041.jpg b/data/Bird_Speciees_Dataset/BARN OWL/041.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1cd4ba4f89f0e6398514c5cb433449b71fff5bdc
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/041.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/042.jpg b/data/Bird_Speciees_Dataset/BARN OWL/042.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..70a8bd615886474cdeca66f9fed1767279694481
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/042.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/043.jpg b/data/Bird_Speciees_Dataset/BARN OWL/043.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..07829ae4e5501d6b975975f0e0cf0f476a95ec18
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/043.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/044.jpg b/data/Bird_Speciees_Dataset/BARN OWL/044.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d86680efbe5262ef6ea4d36c442e50803f286e38
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/044.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/045.jpg b/data/Bird_Speciees_Dataset/BARN OWL/045.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8615155eb0612a016c2bb15984a3f6fdd93c744d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/045.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/046.jpg b/data/Bird_Speciees_Dataset/BARN OWL/046.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ce107fbc90b600fafe27310e2a72e6a6278a1164
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/046.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/047.jpg b/data/Bird_Speciees_Dataset/BARN OWL/047.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d605c0f3a11b93a0b901e21695cf397c364eec8f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/047.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/048.jpg b/data/Bird_Speciees_Dataset/BARN OWL/048.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0ebba8f1f40c89b1c96130167345e4abc0ecdd95
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/048.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/049.jpg b/data/Bird_Speciees_Dataset/BARN OWL/049.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..768b4065e4bb526c423b351faae7eb931eee09df
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/049.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/050.jpg b/data/Bird_Speciees_Dataset/BARN OWL/050.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..98941fde1fce2526804aa32887ff91e8adda5f60
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/050.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/051.jpg b/data/Bird_Speciees_Dataset/BARN OWL/051.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..33565ef75eb980d831fda068bda21982801d8020
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/051.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/052.jpg b/data/Bird_Speciees_Dataset/BARN OWL/052.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7a05a571b4b48ba5f3853bce7275d3bd8af67125
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/052.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/053.jpg b/data/Bird_Speciees_Dataset/BARN OWL/053.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c96fc92db16f6bcb6601ab4d53ea2c19b690c49e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/053.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/054.jpg b/data/Bird_Speciees_Dataset/BARN OWL/054.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c1135c913d3c35d279b63a34b090958c5af09e40
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/054.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/055.jpg b/data/Bird_Speciees_Dataset/BARN OWL/055.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fc235ffec8510f76f84cf0e12046eb923b123290
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/055.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/056.jpg b/data/Bird_Speciees_Dataset/BARN OWL/056.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..008be9d495e8f37791501a2e8c8ff922387d0ce7
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/056.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/057.jpg b/data/Bird_Speciees_Dataset/BARN OWL/057.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..17840b001cc20511dc3c2140cb9253c260cc9c13
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/057.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/058.jpg b/data/Bird_Speciees_Dataset/BARN OWL/058.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..857abaa869cd4a1e8a4ae7d736cfcc9ef40d1713
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/058.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/059.jpg b/data/Bird_Speciees_Dataset/BARN OWL/059.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f9080fc47bbaf07654947f7a40d72d45815946f1
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/059.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/060.jpg b/data/Bird_Speciees_Dataset/BARN OWL/060.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c1995eb81227b24c518a42d7801be92f4d86d44e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/060.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/061.jpg b/data/Bird_Speciees_Dataset/BARN OWL/061.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..adc0b8a2cb2bed6f305473f8a303edfb7523364f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/061.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/062.jpg b/data/Bird_Speciees_Dataset/BARN OWL/062.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..299ac6be20bd76a3f6b2b26f44d1e8831197cf6c
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/062.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/063.jpg b/data/Bird_Speciees_Dataset/BARN OWL/063.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9512b8d0fc1c2dd423f096a174a558c6fc09ebfd
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/063.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/064.jpg b/data/Bird_Speciees_Dataset/BARN OWL/064.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8dc6d78f1b150d9bb02915cd0414d54193eefe9a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/064.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/065.jpg b/data/Bird_Speciees_Dataset/BARN OWL/065.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6d98cf331a45b6d222d2b74dba9f5a2c3b01175c
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/065.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/066.jpg b/data/Bird_Speciees_Dataset/BARN OWL/066.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..692a649fd6a50d96e4d5339ee4be83a966da4a50
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/066.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/067.jpg b/data/Bird_Speciees_Dataset/BARN OWL/067.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c56cb4652aafc694b6df21cff7e7a837f194deb9
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/067.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/068.jpg b/data/Bird_Speciees_Dataset/BARN OWL/068.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..314d2888ef1773632caf37b4ba8b0908ad6fec00
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/068.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/069.jpg b/data/Bird_Speciees_Dataset/BARN OWL/069.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e356a6ff84945b75425fe908d92ecbb361d87e46
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/069.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/070.jpg b/data/Bird_Speciees_Dataset/BARN OWL/070.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ccda90e9e787543a53faf0b5f5cd8ee305171393
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/070.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/071.jpg b/data/Bird_Speciees_Dataset/BARN OWL/071.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4c6bc50916db3b8ece722327311662d4638f2ac8
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/071.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/072.jpg b/data/Bird_Speciees_Dataset/BARN OWL/072.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b960fb36ecd5f731784cd92316d39bcbd2cda130
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/072.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/073.jpg b/data/Bird_Speciees_Dataset/BARN OWL/073.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0074bd357c29dad65bdc0b3f47ed45b6aef46b90
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/073.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/074.jpg b/data/Bird_Speciees_Dataset/BARN OWL/074.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1857c8ed3dd7d64570a9870ed88f95ab147b95c6
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/074.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/075.jpg b/data/Bird_Speciees_Dataset/BARN OWL/075.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1f675dff8bba275326c9aa1389b002e838d14c3e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/075.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/076.jpg b/data/Bird_Speciees_Dataset/BARN OWL/076.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..29bcf0badea52c14575d7516409aa975067162e0
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/076.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/077.jpg b/data/Bird_Speciees_Dataset/BARN OWL/077.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..af274d5ed01a13dc2d6d6c20a9b02c79c9038b6d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/077.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/078.jpg b/data/Bird_Speciees_Dataset/BARN OWL/078.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6d21672995cb07986012114da0f8f80b3148f0e5
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/078.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/079.jpg b/data/Bird_Speciees_Dataset/BARN OWL/079.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ac0b72d5c4c8d3cd791669b3391f82a4d2c0492a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/079.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/080.jpg b/data/Bird_Speciees_Dataset/BARN OWL/080.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..50ff6480a7e2223254def1779000c4a3f49d437d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/080.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/081.jpg b/data/Bird_Speciees_Dataset/BARN OWL/081.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..acc9525c52cda15d3d2180cb59a2bd84401cea95
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/081.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/082.jpg b/data/Bird_Speciees_Dataset/BARN OWL/082.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ba6214973d2cd645c0475e25d63c2cee95d5442b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/082.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/083.jpg b/data/Bird_Speciees_Dataset/BARN OWL/083.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..55ab13f783d24f8a3a197e2829c3197309a194c9
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/083.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/084.jpg b/data/Bird_Speciees_Dataset/BARN OWL/084.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..af55b9649e19604cea30718ca6ed50bf9fe0c0b6
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/084.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/085.jpg b/data/Bird_Speciees_Dataset/BARN OWL/085.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..07582f783f33b79484ec8a39a28e75fc6d5f8805
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/085.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/086.jpg b/data/Bird_Speciees_Dataset/BARN OWL/086.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5be744cefd5bea6984961ff17ee82dcd80e08881
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/086.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/087.jpg b/data/Bird_Speciees_Dataset/BARN OWL/087.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1653bccc9e9e616794625edbc1df1b8b536da9d7
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/087.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/088.jpg b/data/Bird_Speciees_Dataset/BARN OWL/088.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4cdf1d2ea58d021990922bb12669b5c2c22ffe8e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/088.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/089.jpg b/data/Bird_Speciees_Dataset/BARN OWL/089.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8c84d6d26bc06469dbcdcf21c4201a182ecf50b2
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/089.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/090.jpg b/data/Bird_Speciees_Dataset/BARN OWL/090.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fe08cdcc75838ca53ffe6af33eee3c229888bb09
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/090.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/091.jpg b/data/Bird_Speciees_Dataset/BARN OWL/091.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..778e48a834519793ec1aa9618bcf165481ac99e3
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/091.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/092.jpg b/data/Bird_Speciees_Dataset/BARN OWL/092.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e65913978cedb12fa311d45b98116f3f333b0b9f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/092.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/093.jpg b/data/Bird_Speciees_Dataset/BARN OWL/093.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fa14e048fe6afdfef39e2a7ed5031cb84af475ed
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/093.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/094.jpg b/data/Bird_Speciees_Dataset/BARN OWL/094.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..cb56d418d6d080d65b256d57291bb69fdd72ed3f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/094.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/095.jpg b/data/Bird_Speciees_Dataset/BARN OWL/095.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..80901d9e08f8ef72c3c620ebb3ed0547595a41b0
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/095.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/096.jpg b/data/Bird_Speciees_Dataset/BARN OWL/096.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..399157f8ef49f52470a2d14e28c2260b3e2e32cb
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/096.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/097.jpg b/data/Bird_Speciees_Dataset/BARN OWL/097.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a13ce89990f90e2a90c50982ef7639d571b06724
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/097.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/098.jpg b/data/Bird_Speciees_Dataset/BARN OWL/098.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..aca14e12bf8ed9d1411803a7b81faf6a018b7f80
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/098.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/099.jpg b/data/Bird_Speciees_Dataset/BARN OWL/099.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ca7f86ba24236e219179207479666a8e69b82bd5
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/099.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/100.jpg b/data/Bird_Speciees_Dataset/BARN OWL/100.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8bb8b755451cfe764c96b2ab70be75f0c0ea9210
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/100.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/101.jpg b/data/Bird_Speciees_Dataset/BARN OWL/101.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..872e6ae5b46ca1fb75a5ed7f0872036bf5c61849
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/101.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/102.jpg b/data/Bird_Speciees_Dataset/BARN OWL/102.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4c42fe9a087e283d3a5bc5fe8620ba3b9e11e4d3
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/102.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/103.jpg b/data/Bird_Speciees_Dataset/BARN OWL/103.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d902844255951b5d644ed32ba6efa51aeb12ccbd
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/103.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/104.jpg b/data/Bird_Speciees_Dataset/BARN OWL/104.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c3c344665a00be3eaa04f17002d0304b6e6fb186
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/104.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/105.jpg b/data/Bird_Speciees_Dataset/BARN OWL/105.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..acb57e9707e020782d8b8291450858539ef9020d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/105.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/106.jpg b/data/Bird_Speciees_Dataset/BARN OWL/106.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0f7fee3ca1305a69cff524b1e9838af0f131e9af
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/106.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/107.jpg b/data/Bird_Speciees_Dataset/BARN OWL/107.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ba76431b6441b5c7462b34bd1e76ab1a9bf87bb2
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/107.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/108.jpg b/data/Bird_Speciees_Dataset/BARN OWL/108.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3ef5d4b69761e5780fc7c96012edcffa2175ceda
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/108.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/109.jpg b/data/Bird_Speciees_Dataset/BARN OWL/109.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..70f31bcb82187533a815acd495f86b14113f34f4
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/109.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/110.jpg b/data/Bird_Speciees_Dataset/BARN OWL/110.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..76b15b80cbab091d1fb6622b32e3d583ee83bd7f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/110.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/111.jpg b/data/Bird_Speciees_Dataset/BARN OWL/111.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d54497e07067d38c6e7470879f8bb5534e0d4293
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/111.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/112.jpg b/data/Bird_Speciees_Dataset/BARN OWL/112.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9f2ea02958bce453488a26b32e9216da91299d81
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/112.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/113.jpg b/data/Bird_Speciees_Dataset/BARN OWL/113.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..69d8db2fd95c36bc00187fd2b1b1e44f81e9e820
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/113.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/114.jpg b/data/Bird_Speciees_Dataset/BARN OWL/114.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9ecafeff3ec08043ccb48f669b3ec7324523c74a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/114.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/115.jpg b/data/Bird_Speciees_Dataset/BARN OWL/115.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..69569b7c51b701f13f6aeaeeebcd288201bcb633
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/115.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/116.jpg b/data/Bird_Speciees_Dataset/BARN OWL/116.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e014d96e96c9e521d90241373a83babed1600060
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/116.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/117.jpg b/data/Bird_Speciees_Dataset/BARN OWL/117.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d31f58a01420d3cf2dedc568cea1e55b40644a6c
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/117.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/118.jpg b/data/Bird_Speciees_Dataset/BARN OWL/118.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7abe687cfc8a2239c5a87a70496f4132d9f7300d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/118.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/119.jpg b/data/Bird_Speciees_Dataset/BARN OWL/119.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c787bf9ec18b1453c6ec4747751c886ec794d803
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/119.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/120.jpg b/data/Bird_Speciees_Dataset/BARN OWL/120.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6f70261cd4dd65bf93d597927f938d6993ab2347
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/120.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/121.jpg b/data/Bird_Speciees_Dataset/BARN OWL/121.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0657ce7424f77aa7d4b15411b9c6c308c62a41ec
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/121.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/122.jpg b/data/Bird_Speciees_Dataset/BARN OWL/122.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..dbec4e9d7c43bb4e9b92e183da78e9f6fb4a2fa9
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/122.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/123.jpg b/data/Bird_Speciees_Dataset/BARN OWL/123.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..88a714826190310c2f7763251a646e4eb6d037af
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/123.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/124.jpg b/data/Bird_Speciees_Dataset/BARN OWL/124.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f8cc5ad6ca9adef62fc178372a5c5f9478f30398
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/124.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/125.jpg b/data/Bird_Speciees_Dataset/BARN OWL/125.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e436b3881900c41d60c8bca944a2fc17361d60dd
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/125.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/126.jpg b/data/Bird_Speciees_Dataset/BARN OWL/126.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..99f8d671db2c4b17a7a56638da4afe133ac14838
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/126.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/127.jpg b/data/Bird_Speciees_Dataset/BARN OWL/127.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ce577aea9d449ad3c7f8cccf7b7545b2d2aecdf9
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/127.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/128.jpg b/data/Bird_Speciees_Dataset/BARN OWL/128.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c097b2eb2daa2c6623833146b904c5dcaa1615d5
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/128.jpg differ
diff --git a/data/Bird_Speciees_Dataset/BARN OWL/129.jpg b/data/Bird_Speciees_Dataset/BARN OWL/129.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e6383e7039ddab1557435aed1a91e6979df18968
Binary files /dev/null and b/data/Bird_Speciees_Dataset/BARN OWL/129.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/001.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/001.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8d7538dbf84d22a75d9eec4c2020c473ba45ab35
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/001.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/002.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/002.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..bd13b0c85ed110c80438e9d9053a8b93a30d3614
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/002.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/003.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/003.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c15f08668ba450236fd696a0391c8821dcb77d72
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/003.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/004.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/004.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e97e906157daaac94b659de13fab5ea882d06a31
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/004.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/005.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/005.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..55f6e41d93c667a0238c55b0d10ad937d01cf38a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/005.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/006.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/006.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..55afe83db23e026dbe1b6d21d6c3459c28c66f1f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/006.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/007.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/007.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..520d0acdfcd574d8dd0ffcd2a4fdc74536c76152
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/007.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/008.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/008.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d3960986e6621757f6ed28eb9e05b903dee616f9
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/008.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/009.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/009.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..124d984aa2eed7fea3574117bb461d3dffdf10c3
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/009.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/010.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/010.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f3662e4bed13571c0202da9c106d91c3d4c04fd1
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/010.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/011.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/011.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7f17637b03b6f3a8ac324c1aeb8da57ec876790e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/011.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/012.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/012.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3cd626e3545b2b46677584df591c591df9ac0b5d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/012.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/013.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/013.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0a00dfc25e07ab3d5d2a199ba087b4384f336121
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/013.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/014.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/014.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5b7c0a087aa5604c9a700c25e83c642349a848e5
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/014.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/015.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/015.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9acd4a4cee5e929c63c6a2109b00957bf0bdb260
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/015.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/016.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/016.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..30cb1ac016506e4bda83dc603585cd8095268924
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/016.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/017.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/017.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b153b1fce4ef86259a43e1eebd96bd48519eebb2
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/017.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/018.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/018.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c3905a5d28dea765a716368ee8ca592e1fb4c097
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/018.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/019.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/019.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d8a130462eb939450450edb3f2425e15946f2e8b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/019.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/020.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/020.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1147779288395e5f255842d33e29ba60204aab0d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/020.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/021.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/021.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7b5adcafeca80e3f4e9bc00c606a0b63fc7f3b8d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/021.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/022.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/022.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..565a14b5d4c7dc93c5bb1161392f1fffbbcd451d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/022.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/023.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/023.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b1d1bf3ca42442551d8ea89a83268661b55fd6ec
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/023.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/024.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/024.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c1823049ca63bc53b197a8c01167f2ae0533f675
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/024.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/025.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/025.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6838aaf47610685998cd71b8db8f80700bcf73bf
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/025.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/026.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/026.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9441513214b7621691063fb5338e7c5e63ebe689
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/026.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/027.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/027.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a4c31a2e9406bb47f0cd5252cb337ca2d95afe17
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/027.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/028.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/028.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f425c354526718a51cf0ba2936785d2c6c59e145
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/028.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/029.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/029.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e090e2029eaaa19ae2d7a6b3a040186aa977f7be
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/029.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/030.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/030.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..66d1ae27e744f4812f5e2c0ec429ee54d73af798
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/030.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/031.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/031.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..586a2c376f027b35d7f2a9f687241d51574ccff1
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/031.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/032.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/032.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d05a10ec7890f3df0d2224a550767ce7d7341c88
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/032.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/033.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/033.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1f7ddc0d5fa7e99d7a51013786e7c9d4a7455551
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/033.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/034.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/034.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2de165eee47b0f02e9cc7a1316d1d4bd1c4379de
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/034.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/035.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/035.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2be06c47524845ddb777babdb31a7e29e24690ab
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/035.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/036.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/036.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d29c0ad33498945ba0d919655ff281b7ff9ab0a7
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/036.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/037.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/037.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..80b66ba111a5b48630e3d9542b2319c98dc0bcd6
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/037.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/038.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/038.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..59f475dea41e2fc4d21b74111473d3f0d1024124
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/038.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/039.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/039.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..19def9d1640b96529edb634141451d77249b62ca
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/039.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/040.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/040.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a3944dd8d3d507791ba3230be4c7fd9ccb0cc979
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/040.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/041.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/041.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..03c02e11442b60616da088a1908239afdd1bdf98
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/041.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/042.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/042.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fbe35c37736ef064ad0bebfe53f9c93c0acdb6e1
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/042.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/043.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/043.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fdea01b8efd6746887ea69f16f354a6a9716ceef
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/043.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/044.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/044.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..92d6e2733bead5a772c938eafd4828b8a0dbf7dd
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/044.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/045.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/045.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..72c6fd20615d2107730c16da75ebae42efca96f4
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/045.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/046.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/046.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..68fcdb0d9f0497e7a85dd30c3195e85248403ac9
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/046.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/047.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/047.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5662661fdaf1931887eb87dd3bb849dae2b6beb4
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/047.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/048.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/048.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f0c3e46ae83aef453176251685debe814fe7f017
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/048.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/049.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/049.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4ce5c4c61d0c5e8cc9710eb5572fdaa379d98941
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/049.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/050.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/050.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..212cc26a20e23a50f894f5eafbed6589a28a7aa0
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/050.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/051.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/051.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..dcdf1bd4d4cfcd25183f6527b643a6f158b51754
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/051.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/052.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/052.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d5f6c9cee3f2fda7f75b44c08d230fefd2db92cf
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/052.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/053.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/053.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..61b49b18594f8de891a8a1c3eaef721b77012b73
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/053.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/054.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/054.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1979a4dc3e548b8ff40954a6d9861759063466ae
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/054.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/055.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/055.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..01ab87ccb669f422718a5670887ef9f39631d804
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/055.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/056.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/056.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e1a6c7fc0ac10932921cf26f5660c944fd50da38
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/056.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/057.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/057.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..cdfb6bda7243bf0a2aefe3d7b03c3ade3d02b338
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/057.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/058.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/058.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..06f4a2c3a3e39b89c8259e2bd3b5e9a40d05b8e8
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/058.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/059.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/059.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..250f23d51293c53308761662148cd7c3f2a02e78
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/059.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/060.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/060.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..dcd45b1d560a14f5c53a424ef82a7f6f5823f3af
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/060.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/061.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/061.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..47babb80e0661d2b08e2a27e373fd5ff5b560e51
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/061.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/062.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/062.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..586fc898e7dd6d9eeb050d55742baf53e1ff1926
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/062.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/063.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/063.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d52c330fca47bc7aa1b474c63d73fd4cbafca321
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/063.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/064.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/064.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a9bbc8a970d373716ddefaf23bfbe76ca60144ce
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/064.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/065.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/065.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1bdfef9956e18f93814e412d16dbe6ae578340e1
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/065.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/066.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/066.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..03bc9bf9d3cfbe044086b4e1cb4ae91d8b991cbf
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/066.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/067.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/067.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5e4a64ca13104b3b134a5cfa98ecc51d58062148
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/067.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/068.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/068.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..adeedee5b748486825e4be00e8521dc2c00ee357
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/068.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/069.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/069.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..409fe27561ada0142d5ae20484fd1d7491b6befc
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/069.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/070.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/070.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f3f78d6e9629a7b2b8b50bcebc541cb529235f36
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/070.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/071.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/071.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..11021e15e5ed5f1decb1ffce22a5743fc29fe960
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/071.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/072.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/072.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e8a84c9c9ca29035c20948307046cb15406d3fd7
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/072.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/073.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/073.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3a9504766f272ae0d2a9f5373d7aa364a82b6272
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/073.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/074.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/074.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9b45885070a24e8dadf72658d31ecdcbb18536d9
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/074.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/075.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/075.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..949eba38677bbc528169af9f9953d981123860d2
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/075.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/076.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/076.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..158e591bcee4eed197903a15e71f67cb5d567761
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/076.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/077.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/077.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2969c0e7cad28c102bdf5ead2dc5ee99fae6545a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/077.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/078.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/078.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ba02633fe82299d0d6e01f189d8da0744779372e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/078.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/079.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/079.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..457ed0a715266eae64badb49a65aae2415e56b16
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/079.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/080.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/080.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f350b79ac83f11e80103d82d013c1ae377e5181e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/080.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/081.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/081.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..70d1057c10fb1631bcfbc1fe8cf2612b6fafeb4e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/081.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/082.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/082.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e93a2573e9bd1b868e747730240377e0f8e5abc9
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/082.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/083.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/083.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..276c1f842002eac508abe371e99420403661c44c
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/083.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/084.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/084.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5c810bcf8cf1209897fd092577414b43971a04b4
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/084.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/085.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/085.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5ce0c2fd013201c27989b8ac059b9e3f009d334f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/085.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/086.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/086.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a988cd9c84507714370e14991530d2d63a9f1274
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/086.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/087.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/087.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3d3db4dca62d7ea7d8bca66c85068a893b5cb943
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/087.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/088.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/088.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3a7b7074de213246867e2a4f4ddb644021fc792d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/088.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/089.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/089.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ce0b4eb4e066829cc9c264e718b1ff17dd239004
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/089.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/090.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/090.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4c23f82c3fa0b762d6f1faaa7f5f6b0c153824b8
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/090.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/091.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/091.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5831960509f842da5ece83b671ae4b564a4f7f03
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/091.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/092.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/092.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..45fac256989eebf82efc043808b2eb821c631547
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/092.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/093.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/093.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..15b2643f20f1766ba0a2694f3ab2e05efb93e99d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/093.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/094.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/094.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..86a679ada11e6fad3adc81e37664cfd839f45607
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/094.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/095.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/095.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8f99e61b11cd44e89db557fd15dd3e2397fc9c5f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/095.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/096.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/096.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..322a1c863e813445a4464f476e21b9b89f036e08
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/096.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/097.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/097.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c5c9dc4c7bf46eae48216514c4ad82a7e34456c8
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/097.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/098.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/098.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..bffa01d787f176082f590bef7c4122524948e5e2
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/098.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/099.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/099.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..bc2f58792f9796d28375ec28ada955372fa04797
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/099.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/100.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/100.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e971aa02962b045ffc07ed872402d82d3e5a4125
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/100.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/101.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/101.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ef16ce82679ea8f346aec65c19a51a6700df3f9c
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/101.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/102.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/102.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..75ccc7109aaf5bf6f9744d241466ac8208e556b4
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/102.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/103.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/103.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d4a23a8d6f3725d53d51888fa5e8ac99930aeda2
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/103.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/104.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/104.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e989a29f933d04dbc57955b5d2d407b3ff0a7ea8
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/104.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/105.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/105.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e533d8c86507ebe3d10ca921e4ee87d618bf1c4e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/105.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/106.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/106.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b39c7e74be10bf25298ec5f6e702440c5821cca1
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/106.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/107.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/107.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..55012371c67e80595f466e187166c57d340fcdcb
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/107.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/108.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/108.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..34185081bd7e4bd9641eae122ceb784ea1e0ce69
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/108.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/109.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/109.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3b13687e4262d917ba3baa2bb189300cb4015d43
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/109.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/110.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/110.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a0366ad0c7d50a862cea7802823298fd74b04caa
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/110.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/111.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/111.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9186fc5c6307524ab5341a1b24daee9ba887537d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/111.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/112.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/112.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b88c209005c4d4af41a101a754829c18dc73483d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/112.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/113.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/113.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..de48551931953a1a03ec35e156358fda950cff59
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/113.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/114.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/114.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5cbce4bbf0f520a46a48925a70115556b6815684
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/114.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/115.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/115.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..31f959718f14fd25dde7ffda922e0c92832eb61b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/115.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/116.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/116.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1acbd7e0aee75916b001a729d64b7933fd4920b7
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/116.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/117.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/117.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..213291c8ced7b0530283724f58b51b2b1f64f1f1
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/117.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/118.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/118.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..be4a6bfde3248d75e3e8f8b5fb2f8aacea0f13c6
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/118.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/119.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/119.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..26b8736bd1408f28bd1624235699cddfa8ca56c3
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/119.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/120.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/120.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fb129335069403245f137997217fb0a04b57be61
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/120.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/121.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/121.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..91ddb1d319e59378754fc3e104dab94fd50e917b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/121.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/122.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/122.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..08efa1c4781b1b2dc2d369c4868bb116306af19b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/122.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/123.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/123.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c8909b55bfd86cbcef2fc17093f0d567fa334b49
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/123.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/124.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/124.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c34b85425813c30b2b4c54c255e8ee64a3ec46e8
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/124.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/125.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/125.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2e24e099a06cfee828805e4e3b6e55f7b697b79e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/125.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/126.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/126.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c76235195a538fe025366f841c3aedb9f36082ea
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/126.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/127.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/127.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..14d86b99c59fe2784840119393f7125e4592b4f0
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/127.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/128.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/128.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0be5cd23158311c669e2268ba5df11c9bbd2da32
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/128.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/129.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/129.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..38a844ca240ebe2cd9c25c184af2c14650baf74b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/129.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/130.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/130.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c1680b000642bd56024f21602927fbbae827f366
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/130.jpg differ
diff --git a/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/131.jpg b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/131.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..300b1eec4dec58bdb4f92191d29046a15967fbf4
Binary files /dev/null and b/data/Bird_Speciees_Dataset/CARMINE BEE-EATER/131.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/001.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/001.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4acb6d0f4a389e82bce7bec94f6273f98efc05ba
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/001.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/002.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/002.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..44207cc4b9120a4bb0d654f1e2119a1fc059b1af
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/002.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/003.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/003.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e324a9ade565a467c04bd10645ecb6f7e4375b5f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/003.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/004.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/004.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f85b703d8ec5ea5c9a499d2da5b790f8db6f4c61
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/004.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/005.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/005.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..849eae4eeaa296367c5d73cf2179dc852285d87d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/005.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/006.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/006.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..59bc6a2d86beccc6b9820f01516e44ba601319a9
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/006.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/007.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/007.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7925ed4a0c8b5d059c41bda5a35b7f2385f4e649
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/007.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/008.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/008.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b9f01091a005f118c79d311ad948b128653e0d18
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/008.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/009.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/009.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7b961cc19907d8f857a7d103157d3a1565f966e6
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/009.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/010.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/010.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0ba134f55fbb0215e63f8c57e9d208a7b575c919
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/010.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/011.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/011.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c55b44f84ab4b42ad571a4efbbc152a31fa19fa6
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/011.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/012.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/012.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d51c6a1e3ef978115174f31d94057883a18a2ec8
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/012.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/013.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/013.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ac60ec135c395cb48bad538ee67adda5c765271c
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/013.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/014.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/014.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..03c86b4d3ba85d401d487a5d9497ead0f7d330c2
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/014.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/015.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/015.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1a96b90cedd76f210ec181e3b6e3cf67c02f717f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/015.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/016.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/016.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..aa146b6adbbddff1f1f2e4b501f75563041adb0a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/016.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/017.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/017.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4aaa90a62bdbde3bee1fe71ac8c91a7c680e104b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/017.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/018.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/018.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..13f1ba1581b84fcba2dbbd00a71194b15d0a056c
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/018.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/019.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/019.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6ceef7bda8702938027339c5868c41934cc26139
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/019.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/020.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/020.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1a895d5833691fa46767f3fa77eadc629930ecd8
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/020.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/021.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/021.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e809abd69ef282bec2ac84a0e412b3872944478d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/021.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/022.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/022.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6e79bbed121ee9d75042e432ec9c61a09548184e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/022.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/023.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/023.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..390e3bb29a7e771d281daa88d6ce5a585339e5d6
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/023.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/024.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/024.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..72bde28fe49b50dc7e7f83065372c5e875b9fa7d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/024.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/025.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/025.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1486cd1543043f96e5433eeff5e70d284408a6a4
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/025.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/026.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/026.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4e71e18038cf97790b67e838f8555d52f6399eba
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/026.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/027.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/027.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9357ed0fc534d808c853d2c4f9d5132b9cd3e39a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/027.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/028.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/028.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..bded61806560b80fc81b3c024db5c16696b1cefc
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/028.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/029.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/029.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..df3964cb7233e43262c44e6cd55c11ab70656562
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/029.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/030.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/030.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..340bb7a49136eb85a0701cd786859bde029e544d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/030.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/031.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/031.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0c7223fb6ffe4d54ecde7f4dd768759c29c15dd5
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/031.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/032.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/032.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b84ab5716bed3b3cb7b208c8185c55be4ed31c59
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/032.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/033.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/033.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e35c01783e5ee8be35c649a6e614cfd720148712
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/033.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/034.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/034.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..bb1285782f7dfebebd38aaa7af7f41833a5fc7f9
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/034.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/035.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/035.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..95284aa6e6eba32dd1c31c2428d4686ff476f688
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/035.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/036.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/036.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6497e7aa84ed9be297d4812ff61319bd70869080
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/036.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/037.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/037.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0d29dd473ca93f8ca3890f0a6ba851c86a1e172e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/037.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/038.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/038.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6284d0857c4a514c625d822e57d50a4d07f11a21
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/038.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/039.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/039.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e99430ff5ad3880df4abb212f9ae9e70c6d3b6f8
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/039.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/040.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/040.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..dc6ce67d15833c8cf231fd240d1f96593198d077
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/040.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/041.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/041.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fd5e8530811164e5deed5aaafb22285fdcdddaa8
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/041.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/042.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/042.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5cd5f0bf159bc936207147d782e566744395c331
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/042.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/043.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/043.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7f6fa5c87443393e371d5cc5c24a722cc4996bec
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/043.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/044.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/044.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d7e860c4434c67cec8da309fe1671d09e1ff8ce5
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/044.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/045.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/045.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..880f5505b27e7b66bc790619f90d4b680d3d8507
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/045.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/046.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/046.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..aa44752c2899cbde5e3ea5bed63bf65393f841b3
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/046.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/047.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/047.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3435e7b7e5f346991c939d6f4e16e29b6c96c953
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/047.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/048.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/048.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..458c899fb4918d745fa4d00cef9576cde7ca0116
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/048.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/049.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/049.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b392bdb84b3a34a1b44298cddb582cf09158fdae
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/049.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/050.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/050.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5c4a78cfc9fdac854bdae407f73f44a1efbb5de4
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/050.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/051.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/051.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..90c30f85fcac06ee1e0efac3a17fab90719fd9e9
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/051.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/052.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/052.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a24fe33a15c49f3c58b008d299591142620a4104
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/052.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/053.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/053.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..85a1b48fdd0187a4a64dffc20637894aa8db9f66
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/053.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/054.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/054.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..53900da3048b82a6f2ad2299b8f4cf678c63b8ef
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/054.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/055.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/055.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d77cb22caaf74e26a9e576edbe99b60538a55c53
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/055.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/056.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/056.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0f168b67cdd450dd258bdc25ef0c337f90f266a5
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/056.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/057.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/057.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..05a3afafb26cfb4337f34e3f172ea612c18dbf3d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/057.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/058.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/058.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0c66dcf3f783348c4a6daa848c7660e554edb95e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/058.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/059.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/059.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..37d83be85cf4bb70fdeea5d1d2f6707dbac54ac8
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/059.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/060.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/060.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3e6a9fd175128eb0247b891f8b3e552caa5a46ca
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/060.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/061.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/061.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..38c57428fba7d8c39089e5ef2682bcb67a20f7c6
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/061.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/062.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/062.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2540631c5bf00d23c5824cacf4420aad88d4db7c
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/062.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/063.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/063.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..56ea14deccf6bdf389310bf4a85aaeb1e1177c67
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/063.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/064.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/064.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0f32e5b6a233e46e880dbdbf5713fe20ab039fda
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/064.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/065.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/065.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a2ec0c592addf2e467cf5de8b1963a496e51d90d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/065.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/066.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/066.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d913d0791a2a941f7bcb8b9b98b9af4e06642ab6
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/066.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/067.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/067.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..733436727a423b6b98b1c2f934a4131cecf0296f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/067.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/068.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/068.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b2885824ba004431425b573a2bfb027d6dbac6d8
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/068.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/069.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/069.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5d10b9f5a7cc3cb0dc92cd43acc9a6d0a5fe86a2
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/069.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/070.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/070.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4c076569b9c372b0ff383dcecfe34390200ee217
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/070.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/071.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/071.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b5d3171aa980c6ed90da79f9c8368d174cb9d611
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/071.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/072.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/072.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1ae6a8694c3596202935986c8317694b2ceeb0c5
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/072.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/073.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/073.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..00af6a1a2b2ea2e10dea09549f7b41ff5e005849
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/073.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/074.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/074.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..af744adbb82d7c6988b2fa6740728dff8938ac1a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/074.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/075.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/075.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a0bedd864f1e4f672ef464cee3d2471dc5b95436
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/075.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/076.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/076.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f9f865f49cbde4768db524458137b16d38b104d6
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/076.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/077.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/077.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a6b7cd6c9b488696b3eabe9a92a05f6edc5f2fd3
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/077.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/078.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/078.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..15142fb90b615a8d809a2318430f9d3cf9676b7f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/078.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/079.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/079.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ad7bc4bc2fb5f46f9611def5c30aab1617803617
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/079.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/080.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/080.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..afed67e2d10378a794c78018b6e270ea16378b3f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/080.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/081.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/081.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4f44836915c97e3c24a9dc2941f2ac6ae2566475
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/081.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/082.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/082.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ab2136139af60a9ae9133df41ef2d3bb968bcad8
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/082.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/083.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/083.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..86b1760fe3db34f22376f87d65157f7bf5a93660
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/083.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/084.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/084.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..806cb1cb13955792b7f723693fe866e63dc7b546
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/084.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/085.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/085.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..579eca88403f0c256ad6254f3c460bdcdc696f06
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/085.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/086.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/086.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2debd4e2a1429a263c17b3b76f65f1101300b453
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/086.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/087.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/087.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..46f018129d75eb6dece143d4518940c16da8efb2
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/087.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/088.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/088.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0e35e2b17f72cddb0ed4d432eeb306127e61abf2
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/088.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/089.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/089.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8946a3c0a58eebb316674bcd6b0a2a5ab4c02f53
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/089.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/090.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/090.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f1eb1a44aa8381e85f261bbcdd88c8c621eab3a3
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/090.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/091.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/091.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5c6b3b291cc068af130302d66a2353a89870e6a0
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/091.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/092.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/092.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..307e9616195ab88e5b6ba00bc2b754eff81caadf
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/092.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/093.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/093.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..24c1f00096416c56a6583daeb43803becde817a2
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/093.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/094.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/094.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..39e9fab32eab174d82f1fbf13629568771ddca41
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/094.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/095.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/095.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fc6a61696c375eebc4a3c010cd3ee66702b76f8d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/095.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/096.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/096.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5ab2db4280bbf6bb36cf1509e9adce035f2166f7
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/096.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/097.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/097.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fbcd11e8ec92e96c88d2966ce4d9726e676f8423
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/097.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/098.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/098.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f2ab6b2bdbcebbbeeed0286013a7a5b831cd3331
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/098.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/099.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/099.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ed1aec7d951a029372af5d743c9bfc93a435536e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/099.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/100.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/100.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a2141bab0dfe4aad14c4382b55026ad8b417d27f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/100.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/101.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/101.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c50ca0befbbc415c342897d8dd74485beb535691
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/101.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/102.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/102.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c4b192ba53cc4c50b018b0b7ed3eef51976c4afe
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/102.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/103.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/103.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..54a99192b79d74c3b44073302f37fa324f221849
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/103.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/104.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/104.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..342c059428648083a7e040000005c8d7222d45f8
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/104.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/105.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/105.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6081ae7596e63bf1df71832f432e9aa42472c24a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/105.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/106.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/106.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..48006dda9cc3251d1ea19becd535d1054afa5dc9
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/106.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/107.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/107.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4d225f5272f94b8e715d0e981c5b2d8330a51d67
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/107.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/108.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/108.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8714d01b7e0d1dc4709e44d5da612f95cca6f62e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/108.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/109.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/109.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..05ce58de44c57086aa6e33cecd47be780005fd84
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/109.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/110.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/110.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8d8c0f27245a8d03f6ca363bb83c7fba64a54f88
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/110.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/111.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/111.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7655ae75afb8017ff17bf51c106cfe6537074ab1
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/111.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/112.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/112.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a2845fb15f33f64d924c605b1a0aa2c5ae3a9127
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/112.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/113.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/113.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..70353d2d2ec515df08df67422140c1417e1d7a8f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/113.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/114.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/114.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f2642a6c75d921931cf0707108ca05e4604d46b0
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/114.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/115.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/115.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7018c4bd415c72d0ede6d1ab35eb5d4c85723fa6
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/115.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/116.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/116.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1d4c1e058e0fe1e4ab392a11ebd0c8a9201ef808
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/116.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/117.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/117.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f3fe66d6ada3b512189ddd170074ca595923f57b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/117.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/118.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/118.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5fb397953c4a46b4c4379726b7ef0f66d09151ef
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/118.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/119.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/119.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c204cb01f8469214a95a9f37d26d116dcb527e6f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/119.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/120.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/120.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d646f950a9a6ba59f0766ac08ecd44dfbef7e17f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/120.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/121.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/121.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f9a4a6bafde551e47ab412bf5bf7e93be8fa49d0
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/121.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/122.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/122.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..49f26e3c8224e46b4dd23504ec8a166e821e6d8c
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/122.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/123.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/123.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..09fb2f68fe30062dfafc4a83acc330ea55dd4758
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/123.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/124.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/124.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2aa7900ae810d896348e745fdc677ccf10f01f20
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/124.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/125.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/125.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5f11d01ad4804cae426b483e90c079b4cfbad1aa
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/125.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/126.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/126.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7cff563eb36f6064123c623402e7805c4f29079e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/126.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/127.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/127.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d665aa91f836f754e63d6edb7aad57e84df502e1
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/127.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/128.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/128.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4f41f96b0048a878fe2e306a044264f101f4c685
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/128.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/129.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/129.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..781c68a728965223f11c1915fcdb686e07e6b0aa
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/129.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/130.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/130.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c87bb05a3f0dc63eae5d74769c39fab68ae2b86a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/130.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/131.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/131.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..019efa1877745485711f4aea9b531ca7f8097bf9
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/131.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/132.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/132.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..00dbf7a5d573f8cdc978b3b0ef8a279491b44de1
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/132.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/133.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/133.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..072c56312b17cbf84815f7e3220db6d7f0aa1373
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/133.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/134.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/134.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a1a86c2cfeaea66064fb11187a08b98d13aa526e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/134.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/135.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/135.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0c856d6b2fbcca6917409707c69b23c7e5d60827
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/135.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/136.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/136.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..aade6cc916ca05483d620aa9f0868b046c574f60
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/136.jpg differ
diff --git a/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/137.jpg b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/137.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7a776e34ad9e17ba322957842c42f17379770c81
Binary files /dev/null and b/data/Bird_Speciees_Dataset/DOWNY WOODPECKER/137.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/001.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/001.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d610a20c8ffae01276aeb094130562ca165aef19
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/001.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/002.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/002.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9876871a21706a40fcecbfb5bcea408fbe21e5c7
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/002.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/003.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/003.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1f1b3a58413c141cb38f6c98558513f2dbe618f9
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/003.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/004.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/004.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2d7d356d99071ab97311b106bb6162160ac91b8a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/004.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/005.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/005.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..21e58f378cabde5bf05cad301d4f61500c1579cf
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/005.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/006.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/006.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..dba32a7219c47add6077780f43d8b0c52fd8f138
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/006.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/007.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/007.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b5696dc72b39f79b156538f75f01848273dec909
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/007.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/008.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/008.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..132a2258d7fba3e5be6d9e5ff837f646661e4452
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/008.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/009.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/009.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..524fffb4b09627ac8b4315cda0f4aca39823c449
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/009.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/010.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/010.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..cd2b1b32b78600297a212f56cc97cd15bb814b39
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/010.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/011.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/011.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ea4c772eafef16a0a8aeaf800c415e551ec5d46b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/011.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/012.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/012.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e57a90aab4b777eb81868186a37e4cf78d32008f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/012.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/013.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/013.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c45a2c52ea118d327fa1ddf8eae96d04f896e737
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/013.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/014.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/014.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..153fd241fc26a439ffc1d4e01e6eba561236b140
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/014.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/015.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/015.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7945eb108e0bb59b8adb38fb7720bbf8094e2d49
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/015.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/016.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/016.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3ed626091ac92a0e8bd617560b954ce3b6d2509b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/016.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/017.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/017.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2d4a9cc3c648e696a5a751571908d761e9de2750
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/017.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/018.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/018.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..af9f5e9b5ab79fdb7be120716fe8d7219e3ae12f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/018.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/019.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/019.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..577310b5588d43d06c899655366d0006a7966eaa
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/019.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/020.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/020.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3cfab761cd54061dcc22329c96e0606e98b49380
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/020.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/021.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/021.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5799ed59554a5689783b294a855f9a1c80dd50dc
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/021.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/022.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/022.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d6431564ae39ee7a1e2b1e570c3392dd79ecdd3a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/022.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/023.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/023.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d7b094daac3b2fea10758d003d82a32dc8814982
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/023.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/024.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/024.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b2993461bdeea03378a8443c67d1b721080335d1
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/024.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/025.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/025.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6529c4b140d5e095dd8023a8b8e5f5a38204687d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/025.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/026.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/026.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..95d5c0825c9e9d76bb5fce1fa0c7b6295c330cfd
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/026.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/027.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/027.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8581f890d6d47e32037831682fed661608c6bd59
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/027.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/028.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/028.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ee44df42118a90a175ecb0a333d2105459a2bdc7
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/028.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/029.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/029.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..bf2d0a065f1c70d8c4b385ca508a81a08885112d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/029.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/030.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/030.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4c493bbddf0b92fb14d67748696fa5e822853467
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/030.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/031.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/031.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ea78e96f6a55a3f4104290a61d4c8a022917d97d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/031.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/032.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/032.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0aa58085c0f48d5d8a8493388724043688bea378
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/032.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/033.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/033.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..347947e5bdca8e206e54bb1afd6f23269e6cde69
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/033.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/034.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/034.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..677830483374026fdcbe26c19d8c0bd0868c11d9
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/034.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/035.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/035.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..594cb1da8580f1294337fc4dd3d349601f46be44
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/035.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/036.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/036.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..609e833007dc6dbfc5893844f0d2c37b6b2112ae
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/036.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/037.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/037.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a27520478828e74722b218a23d82d5167364bebd
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/037.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/038.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/038.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..cf89b486fe73aadf1b7574910a10fb4b72de3d16
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/038.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/039.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/039.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..01d35c64da16d081ab1c57126ad2c5746f434e7e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/039.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/040.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/040.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..94629aa6bece6667c8f957918bf35277a59c8f03
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/040.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/041.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/041.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..92cbf2cec59a4a893cd81a6fc580b742c537d006
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/041.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/042.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/042.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..acad92da902832aa890a1469d3116c90f8e9cd03
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/042.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/043.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/043.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a58c6a5ca42fd1df9c00aba07d1ade76649d772c
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/043.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/044.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/044.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..bb6c59c8106db39e6854fad904847b03f94ddccb
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/044.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/045.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/045.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a5a31da92a69b5f640eb161ee556b9b0d4bf1d9d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/045.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/046.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/046.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b6f6ff7b7ba79bde8fa350bc7ff497ea03b6760b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/046.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/047.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/047.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e2afe4a9fcdda7e4d8e41a526d3c991c7adc1aec
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/047.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/048.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/048.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a00989fab8d270adb40fc44c02c797c9d47d1513
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/048.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/049.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/049.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3be92de33ff7cdd0c5270f14af5059df7983d588
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/049.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/050.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/050.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b834ab2410910614764a195c63ec420c281918c9
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/050.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/051.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/051.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8c92c82e31c061994a8ea48188589d21ce7c2332
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/051.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/052.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/052.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ef8aacf3913889f2225986013e39759fcb47b03d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/052.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/053.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/053.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5d4203254f38d06bed45c593eadb07aec95fc8fc
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/053.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/054.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/054.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b10b2b530bfffae11dc489a791ed5df227424fa2
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/054.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/055.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/055.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..aaef8ef73d5c8a9938ba178eacce69089b6ce746
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/055.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/056.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/056.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..17974ac2ea58188ef1848de78829699a6fee3bb6
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/056.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/057.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/057.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..df7c50a8cb891463f565f84f0ca873aa4f903fe2
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/057.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/058.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/058.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d841ae4ec486e54af34e53755f97a79eab3cb1ee
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/058.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/059.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/059.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1fa4ed0acaeed5833f4451806f0f218b474ed1b6
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/059.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/060.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/060.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fc4a27fe781fc5483315b218dd08a5949d34609f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/060.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/061.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/061.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..82e0d155c429b1ace96dcc24a51ddc7e31568970
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/061.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/062.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/062.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a611738aa66a9857d07549ab7070d96136d6222f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/062.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/063.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/063.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..40a182cb1d90ad0f0a16a9a62d86f6399d842fdf
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/063.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/064.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/064.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fa8e8af04312e1bc6b9ad4317737e868a075a065
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/064.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/065.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/065.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a246d84d94784da3cd8e4e55c98caf598d6f73b3
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/065.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/066.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/066.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d3e81b93622fd894d1d9a407af31ad61988fa3d1
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/066.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/067.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/067.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..12b8651bc71672136ea65877259995c1c70a5663
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/067.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/068.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/068.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6c10a09010fe42255865f3ede334d64dbe242a56
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/068.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/069.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/069.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ebf37062fa2f6d25dc7d635447a82a47365a4447
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/069.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/070.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/070.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d17b6d04fcd686df610f559bd6211cdac6b094e8
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/070.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/071.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/071.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fc046931f3e884527424ad61024310a87244ce91
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/071.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/072.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/072.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3e5f1ea800f926ddeef087fcf54f884ca31b47ef
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/072.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/073.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/073.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8cb00b041de72c9d7cfe3a39f1fe79e9f31450a5
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/073.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/074.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/074.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..27998d87a5be816c238709243ae20e659ae79b18
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/074.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/075.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/075.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9f59d945e71b85096e50f77d5965d7be10966769
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/075.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/076.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/076.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..50d3fa358f89c11b5c1b8555d101b6e05ca69ecc
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/076.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/077.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/077.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c0630a90189c8df18e23570e9771c47d985c6f5a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/077.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/078.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/078.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..47139e57d240c0f9ed768b8f5a6e01ce036c5753
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/078.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/079.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/079.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..26119707e7526509504b31021481cf658e21bbc7
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/079.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/080.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/080.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..54ec6cb7134cc615e204e6be77b5b2e734152160
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/080.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/081.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/081.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e5c67aa011ff36a54d5118dbe08e5bd953d79411
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/081.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/082.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/082.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..12969139aef7ef60d1e412b7cd81708d563512be
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/082.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/083.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/083.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..476c0dd75fc92500a61419845bb98586ffcfcdef
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/083.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/084.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/084.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..35f38dcce8ddfdb3257143d4915df6b45354587a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/084.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/085.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/085.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..234ecf2e3da5204403ce9e00e1e012cb7ab783d6
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/085.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/086.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/086.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..dfa52ba0f48b15a4c7c298d09733308d33a12c2b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/086.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/087.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/087.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5da301c73bbfaeeb19e205dd0dd81d6357488ac4
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/087.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/088.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/088.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5740e23a5649fcfca955682a037e6216a4f30e99
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/088.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/089.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/089.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..688cefebe436cbb5b06307735c5e65d5a6491094
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/089.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/090.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/090.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..caeab0a41923acd097db6e7024b06094c3e77cc4
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/090.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/091.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/091.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5e0a849e2dbdb08678ac634cebf0ee3f29fbdd19
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/091.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/092.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/092.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c341c420e7d5670b1ba633f3eecf47329e48c94c
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/092.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/093.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/093.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d6c17dc5bdfd57cd7c5e19fdede57810d84a71f4
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/093.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/094.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/094.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2beb163c43dc1237de69953ced0acee5fc1a32b0
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/094.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/095.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/095.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a3232a39f3d54aacdd2d55960b1fa33a8e05acf4
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/095.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/096.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/096.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fe55fe1e41e727baedc55c4e8dda8e77b6be9844
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/096.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/097.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/097.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..32d6c31de42b05f7f4e784131d0f1272c7071885
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/097.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/098.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/098.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1137d4b13c18e4dee8314ce78309e969b87dfdc1
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/098.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/099.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/099.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..536727108094f75ace980b6261785a8c4ca2e071
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/099.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/100.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/100.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6f446fb6670f15c62a3b221333dc1982c8da8cd6
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/100.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/101.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/101.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6096396753fd5aa92d46490fa5d32aa4d2ccc385
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/101.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/102.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/102.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..cb3b289134cf9661af39af5abaecee8c9123cd58
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/102.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/103.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/103.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d2e136e3cae5bae2385b5b3cc02f5438a9cf8110
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/103.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/104.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/104.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..dcbbe50e576ede906086573b6e372116c8768c32
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/104.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/105.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/105.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..98b1e40b4c3e2fe371143487104bd4579e119bfe
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/105.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/106.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/106.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ba8835aa62b456b8c0d0e9e2631534a9af392f5b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/106.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/107.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/107.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d619904385f80cabc84c784d089533681cc7316c
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/107.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/108.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/108.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7b0d8c64de040939d56eddbeee606d7c9de24995
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/108.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/109.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/109.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..49b560315232e3fd47b2f21e9f7358c366b687d0
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/109.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/110.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/110.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..52a316494af39c06793ab02fc6ef0faebfcd7810
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/110.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/111.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/111.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..afc30177aee8f773765b09f975979cc71715065e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/111.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/112.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/112.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..70c1d28c927cc6967d81f003d80aadee75875608
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/112.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/113.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/113.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fa67957d082bf0889512d74d5334a372084066fb
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/113.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/114.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/114.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..01efd664bfaecca238141d05ec22532b1c828f4e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/114.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/115.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/115.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b3b678ba272aebbbca9dc74d1cdba592a0d2ef4d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/115.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/116.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/116.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..443bf3efea6f1b80ba720d678fd773d8f368c1c0
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/116.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/117.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/117.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..341f86d8859becf44ecaa90848777aab6bbd7b9f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/117.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/118.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/118.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..021a8b2369f703fa26e3e477b77af610778607e3
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/118.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/119.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/119.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f982b3bc9507de1150866a00cd350e7f446b65fc
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/119.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/120.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/120.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..06f3efcfa40390c65ae763c19d7c3b3a274a5de9
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/120.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/121.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/121.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..119a388562f9efb77e233e11f32b4418935f49b5
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/121.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/122.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/122.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e8c1cae77031ff33951162ed24c237092f2fbe48
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/122.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/123.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/123.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2e6bab5d9302c670b6b042008b56fe6eaf818ffa
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/123.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/124.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/124.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c9df31d4b550425d64094d9deb6dfa266da7a48a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/124.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/125.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/125.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b1436dfe1803d46787c5193098c213399a8fa29d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/125.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/126.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/126.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0b47a0f19fcf98dc8cd68b95ae5a57303bae60d8
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/126.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/127.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/127.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a2bea296332d78477f9ad11d681f009eedd880da
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/127.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/128.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/128.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..64c1626af65d333f95e099058b0f39cbb8f61cd1
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/128.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/129.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/129.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8532d5052e5ba12200394c350a8b006a87770a34
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/129.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/130.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/130.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..591caf77323e8b2a26ef0bd10a2c571bb5d13aa3
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/130.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/131.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/131.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..20618967e0e63f2795b4d88828892084eb86c759
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/131.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/132.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/132.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..cf4a6565b4a7b5d242ca4b538483b60ab3342abb
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/132.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/133.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/133.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..152a3cf30d0c757f552890e815b357d93e1abbb1
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/133.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/134.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/134.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a2815d85ba784859d82f41892adcb61a227170f7
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/134.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/135.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/135.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..029ab71106fac31f274f0c2cee2101134b5d73e0
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/135.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/136.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/136.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2f7f790f3524ba0a25945a9aed4779be0092f8df
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/136.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/137.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/137.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..166ca00c166726e1fc75d49a2fc72312731d4052
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/137.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/138.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/138.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d3ff578f75a74a41b2d027bb5fc201b4d66e8038
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/138.jpg differ
diff --git a/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/139.jpg b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/139.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b3cacb6ed73c59bd3d41c155de5eac6ce3a9f035
Binary files /dev/null and b/data/Bird_Speciees_Dataset/EMPEROR PENGUIN/139.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/001.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/001.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ace83f1d0b13c5984f717fd3e30112e4e9700cb7
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/001.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/002.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/002.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..bc4ec5d60d833b8acf19140365e2f7c0867afecf
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/002.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/003.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/003.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8dbc5e2a8f7b8bb367102c05dc3fdb36bdf8dcbf
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/003.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/004.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/004.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..63eca5c8dc5f362822ad74a0a9ca978e5088b795
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/004.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/005.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/005.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c2de5c03cda98e065866c052aba162520f6eab73
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/005.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/006.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/006.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..872855c4f7ff6d9ce07b20b5cb50ca153f305ced
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/006.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/007.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/007.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..39919964a529d1fc2f1fef2280fdccb1248f2e72
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/007.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/008.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/008.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..cc1dc7e2586ec20bc0820d43910404c1aa254af0
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/008.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/009.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/009.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..14f98d48dcd2d8db2fc1f500327a3329a9d28c22
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/009.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/010.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/010.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..98cdd1f1d57ac04ac34fd94fde2215a81a4891bc
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/010.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/011.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/011.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0ff2bda125acb6bff7777b54608d5cd6e86e5c93
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/011.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/012.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/012.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a707c6b9f6a08f0ff7d8500ed4f71600cd0f08bf
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/012.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/013.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/013.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..84b9eef63cb3441298181ebf8222398e580131fa
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/013.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/014.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/014.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6ce5c355e06de491fe7d45ae653e279a6b45a30a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/014.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/015.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/015.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0f153047f2f5876d18f7263556cc4d06ebc7fb8b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/015.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/016.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/016.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e6279346385bf2aa6d9464456720fdcfe41e0e35
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/016.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/017.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/017.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..26ae1279f30bb22103cb60f7be3cff01fd48e8ff
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/017.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/018.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/018.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2b461e6ee4d8177ecb344c580cd78f0c7cdb0083
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/018.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/019.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/019.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3bcefd6ada57e0a307e69ab9655e6ef94f05664e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/019.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/020.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/020.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3b9c89c954c2cc365164d2b662cd9bc8dfb9b007
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/020.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/021.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/021.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..53166b3e51154cd66d18fa60d42bdaaa9cef2437
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/021.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/022.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/022.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a3aca4bb756045e5187a747d15a795851de9328c
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/022.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/023.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/023.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5174bfcb57154777548d3c16c7b63ca89f3552ad
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/023.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/024.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/024.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d3e874268b4e819f49584db989237874af5f2f48
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/024.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/025.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/025.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fd3dd8d4279c4c901b01ca3907956b5a03fedb48
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/025.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/026.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/026.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8d19e23ad10a13dab17d56917d17c965eb2fac3c
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/026.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/027.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/027.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6afcee1c03c419302033c511afdbab83b7a5e909
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/027.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/028.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/028.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..fbd6d260757eaf895ce0e8b8e5f78c84a78b9647
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/028.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/029.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/029.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4fdd3857b396f717b0f5c6dd3aa0e1399f647e34
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/029.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/030.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/030.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..de32cb5d277436e9a453d1a7e1f5d5f38ee8509c
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/030.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/031.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/031.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..68a8242e8ad7805715c74bebe26c61da2674650d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/031.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/032.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/032.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b728d59737cc370e3b08b6dcdaf958683737f7c5
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/032.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/033.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/033.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..02e7038c370ca3e60bf163753cedb45e42fa1854
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/033.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/034.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/034.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..08279c04989a0394e9ae9db2c06f03463cee6cef
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/034.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/035.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/035.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..138e96f641b9ce4b5cae42ce1af733d47dce9bf9
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/035.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/036.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/036.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3a03076f2655a6b2ad7b1c541a5600c73bb72d77
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/036.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/037.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/037.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5c28d02c1cc0ce64eabd0d9597ecefe6ec610130
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/037.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/038.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/038.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b46fe547990b0804700635898ec2c1eb04478eb9
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/038.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/039.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/039.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..cb37e6a1bf6ac76a4ba4b687a5bf465f43b1e4e0
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/039.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/040.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/040.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..76e684733de15fa1f94ba0e19b2a442fd2b614d7
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/040.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/041.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/041.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9b9b4ad19f6f27b3d1d7a8b2c6ba2a557986a0ed
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/041.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/042.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/042.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5cf6b3643deb62896e95592ae9f24aa67b451171
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/042.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/043.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/043.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..804759ddeefebcd673f5b4ed62b0e893bfba6e53
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/043.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/044.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/044.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..59aa6b666c315b80b6c7325111131cf432c08bb5
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/044.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/045.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/045.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1032ed7511584c96a61403f3b98b1d50ebfdea25
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/045.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/046.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/046.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..248540f0f1c889cf6638e9ad45e854a7d71701ed
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/046.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/047.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/047.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..54d2b6e046f0951145d8b570090f6fdc64fd6ba6
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/047.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/048.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/048.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..dcebef8514241180d5a53fa20249705202f7c4c8
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/048.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/049.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/049.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a2e7240c0117c366b323125d117708496905eba5
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/049.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/050.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/050.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..814b4b08bcec87b561a7abb03b386179e602868f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/050.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/051.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/051.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..da2216227bb4b7ad56bf87d64edc796fdcb4e725
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/051.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/052.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/052.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..0c072332964ff8cdaa24287f722a8efd175b9e94
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/052.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/053.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/053.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9ccfe9029312957e410292b6b78d9321243350c9
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/053.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/054.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/054.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3f89990d753c842b174537ec18a8cef7941444c4
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/054.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/055.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/055.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c19042c5997f3056b24a624f6fd59e3f3d9e6981
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/055.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/056.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/056.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..97414c5b435f9aaed666d39aa8f0c7f21fe8b9c0
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/056.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/057.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/057.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9aac8410e6709effe211291a8f7baf63b3463daa
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/057.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/058.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/058.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7e2432d866143c70bb2e2af14369371df35b9ccd
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/058.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/059.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/059.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5bdaeaad220ba38d973d1da353fc92f91d419c43
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/059.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/060.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/060.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d02387b035c2164309f40751b6a226eedaf576af
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/060.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/061.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/061.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..391ee8952804925a46a9ffeac4500e8e8116b2b6
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/061.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/062.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/062.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..65da4abcd71fb4df591159e56f0f6feea1b1cf02
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/062.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/063.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/063.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..13216d6113ed1f8be9d1f5bf02f07526a10405f8
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/063.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/064.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/064.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f0299c81d69fc9fac3ec4c5c57748049a1e9bf63
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/064.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/065.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/065.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7b6b4745487c972bd1a3f12532f09e024ab86a8f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/065.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/066.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/066.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b0b104f640467aff9ef55f7ae9486a6f5f91f652
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/066.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/067.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/067.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..18d9a96604f0195f1b92ae36d060074ef4b75a00
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/067.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/068.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/068.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1e7d816bbe65e932ccb5009e5ce1406a635a6f40
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/068.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/069.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/069.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e32b529ac4ef3205d013ff5180f154d1cce77c08
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/069.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/070.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/070.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e65e008c187363d815769a914682eb96d128b45a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/070.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/071.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/071.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..74af8bc5e3840e0ab56c7008ae787fbddc6bfa18
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/071.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/072.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/072.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..eef5ac60bfc4a88ae07d182bd49ed6a411cfa6fd
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/072.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/073.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/073.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..83d10b40301b736b3459bd97a1903bd687b1e911
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/073.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/074.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/074.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7f54aae04379ef26ab6cd25ee78fa62945c2a3eb
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/074.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/075.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/075.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9008265bed164e993b5a99ca184a046a472f1a76
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/075.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/076.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/076.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..cc624c931ac624a82451571800078a2c4c05fac5
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/076.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/077.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/077.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b3ee9721ff0fff69b4e91f723a97bc866ded3185
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/077.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/078.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/078.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1c2a9fa63d1502d03bb409f2d3bec8790dc67e4f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/078.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/079.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/079.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..bfe6ac979a700c3d6395160c1b6f5e1c1b807dc3
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/079.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/080.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/080.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..975e776ec135936aee8bd7b58706aae10cfe1195
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/080.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/081.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/081.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..83cf10aa966ed35a1e662329abbb9fa3ad6eb55c
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/081.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/082.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/082.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..769c209c211fd76e3feeb9728ecda9b8f4bcad32
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/082.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/083.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/083.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3a507b402b3965fe7083f2c398903bae19d7e701
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/083.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/084.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/084.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8134334edf9c3a289c071b48ac35c1aaf51cc679
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/084.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/085.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/085.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..505387b49b0fe540a0327d1ce6791613b8ff8a2b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/085.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/086.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/086.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..84ca821c906e55e33c51ae050783913364e7f1bb
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/086.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/087.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/087.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..10734db6adf61df0eacff46c9bb78ca499e88fd0
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/087.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/088.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/088.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e935d1fec5deab56834aa1999dffa070384b310d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/088.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/089.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/089.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b71f2219e83b3ef8f4a6b1dc7d1b8f20d6382a86
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/089.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/090.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/090.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..814adbe9285e18750f2e0a60e3be78af2a3d714c
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/090.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/091.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/091.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6d21c57c2a2c5fba3f9d8e931d103b1b2d11ffdf
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/091.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/092.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/092.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ded067ba5c65bc2664669d1086ab284892d18c9b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/092.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/093.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/093.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..57d09e53ecb45dbad83a16aba56cdc14c295270b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/093.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/094.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/094.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1d7efce59fd70bd4ec07337d9b2ffc68f46e7144
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/094.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/095.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/095.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a1d5371a79e84fc64dc375266c86f20e3de533d0
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/095.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/096.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/096.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..631f2ba23416879e79eee34d1b704af50f0a74ff
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/096.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/097.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/097.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..eebc14e1a75935111110b4f893ed5f7132591455
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/097.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/098.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/098.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7e5f44af9bcd1ef516b74da6a897fbd292149e5a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/098.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/099.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/099.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..28abfac67fa19513b57b56ee6202da9aee92f5ca
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/099.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/100.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/100.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..4c808484114c4fce8df02254056b65800c5c455b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/100.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/101.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/101.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ca57dab507d07d658fd1cc593dce8c3cfaf16527
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/101.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/102.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/102.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..38768bc18a919259e9cfc4fd893e0c39699dd1a2
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/102.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/103.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/103.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..df3f6c51ce59596fd4494334915bea776a2eff31
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/103.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/104.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/104.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e00c201884f5f34ce54bd96c9734fa1e0c9906d5
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/104.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/105.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/105.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..cbb0214743c14c084186ed2dafead11d4b6d9bfc
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/105.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/106.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/106.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..22dcd8b54a6172c1904ce70f819c7dda20cfae8a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/106.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/107.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/107.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ebc4fb365c7be8745c6883be7cbe3c67f4c91823
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/107.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/108.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/108.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2b8b54b022912b21ba40236cf189a708a812ffb3
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/108.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/109.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/109.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d3eb4e458dc6e8f788b60cf7e6632771b5ae255b
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/109.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/110.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/110.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..5bc6a42f3936d8f862567597f8fd66bc762dbbeb
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/110.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/111.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/111.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..2bc80c2fccc0b57b5376f31c3a9910e33bbd5893
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/111.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/112.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/112.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1a7f6ebb0aea0def83e5bbdbf5ae623ab6723b82
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/112.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/113.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/113.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..24bd0e72354fafd9d297a30c10fff1a87949e5e6
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/113.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/114.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/114.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..9b5082951a52c69b81bfb4709632425bc201ba05
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/114.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/115.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/115.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3ee89ef0d3b1559b31f89c7d7ca5ae4d49dbfd43
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/115.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/116.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/116.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..cf6f62f08fc3452db764a150b9f7508cbbe9164f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/116.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/117.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/117.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..300499892440c4c3fd6ae594ba5b1884a8294802
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/117.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/118.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/118.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..79583bcc0cedb0c78bbb0ebc23bb1901661f1586
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/118.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/119.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/119.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8c5bf63d5e9bc2cd1193533f7f2710e471951b4f
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/119.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/120.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/120.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..368b5e2bc0abc9a43c11409312abbcb02106d9d5
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/120.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/121.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/121.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7208b1f6a237ab47b38d0ca91f3c411f086c14e1
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/121.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/122.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/122.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..17881bee14e2ea42d95478f54ec049e32a72d3ea
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/122.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/123.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/123.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e171c935eb232f843865fbfd752d1d7512db31a6
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/123.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/124.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/124.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a10c1fbedeeeb553cc77a85bd781446831069b2a
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/124.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/125.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/125.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..3328167c081ce5fae9182bab4653bee05cbbc494
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/125.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/126.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/126.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7f435790ad75ae5d02f32adb160cb4a377ab1557
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/126.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/127.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/127.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d28833351d403add0db54c1e33378aa7322b32b2
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/127.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/128.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/128.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b1809ac77dddf6638034eb23535673f78571f28e
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/128.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/129.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/129.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..74fd20a6dc189a26e97846cf2971df085d28da68
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/129.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/130.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/130.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8e3acce28164ef0b96d89a26ba6ae465e59ac927
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/130.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/131.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/131.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..417634048660cf51f91bc5720e021fd857707911
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/131.jpg differ
diff --git a/data/Bird_Speciees_Dataset/FLAMINGO/132.jpg b/data/Bird_Speciees_Dataset/FLAMINGO/132.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1ecf8ddc80468100771cb65caf2d21cf9431578d
Binary files /dev/null and b/data/Bird_Speciees_Dataset/FLAMINGO/132.jpg differ
diff --git a/gitignore.txt b/gitignore.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2feee6dd8171562d81218ce36f5e8a9e9d9eb0a0
--- /dev/null
+++ b/gitignore.txt
@@ -0,0 +1,3 @@
+flagged/
+.ipynb_checkpoints/
+
diff --git a/model.pkl b/model.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..2b14175e2e87ab5516d790f686b13de72ec164fa
--- /dev/null
+++ b/model.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:abdf8446055a9d04beed3cf2270c7a0fe6168b2c4208c5439f9bafc5c93f3313
+size 114630881
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..719437b2846e7be83a9a95aa1174b28c4527a686
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,5 @@
+fastai==2.7.12
+gradio==3.34.0
+timm==0.9.2
+torch==2.0.1
+torchvision==0.15.2
diff --git a/train.ipynb b/train.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..847ea7a290e6f933d4c3765e49a2bb4fc9b73df1
--- /dev/null
+++ b/train.ipynb
@@ -0,0 +1,445 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7195af18",
+   "metadata": {},
+   "source": [
+    "## Gradio Pets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "370354ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install timm\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44eb0ad3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastai.vision.all import *\n",
+    "import timm\n",
+    "import os\n",
+    "import zipfile\n",
+    "import urllib.request\n",
+    "import os\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from fastai.vision.all import *\n",
+    "\n",
+    "data_dir = \"./data\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "085350d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = \"https://github.com/ddthang86/Bird-dataset/raw/main/Bird_Speciees_Dataset.zip\"\n",
+    "save_path = os.path.join(data_dir, \"Bird_Speciees_Dataset.rar\")\n",
+    "\n",
+    "if not os.path.exists(save_path):\n",
+    "    urllib.request.urlretrieve(url, save_path)\n",
+    "    \n",
+    "    # read by zipfile\n",
+    "    zip = zipfile.ZipFile(save_path)\n",
+    "    zip.extractall(data_dir)\n",
+    "    zip.close()\n",
+    "    \n",
+    "    os.remove(save_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "673d61c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set the path to the data folder\n",
+    "path = Path(\"./data/Bird_Speciees_Dataset\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c50bb35d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Path('data/Bird_Speciees_Dataset')"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73867faf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dls = ImageDataLoaders.from_folder (path, valid_pct=0.2, seed=42, vocab=None,\n",
+    "                               item_tfms=None, batch_tfms=None,\n",
+    "                               shuffle=True,\n",
+    "                               )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a362acc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x600 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dls.show_batch(max_n=8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be8ae242",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+      "  warnings.warn(\n",
+      "C:\\Users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet34_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet34_Weights.DEFAULT` to get the most up-to-date weights.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: left;\">\n",
+       "      <th>epoch</th>\n",
+       "      <th>train_loss</th>\n",
+       "      <th>valid_loss</th>\n",
+       "      <th>error_rate</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>1.384979</td>\n",
+       "      <td>0.044042</td>\n",
+       "      <td>0.006173</td>\n",
+       "      <td>00:24</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: left;\">\n",
+       "      <th>epoch</th>\n",
+       "      <th>train_loss</th>\n",
+       "      <th>valid_loss</th>\n",
+       "      <th>error_rate</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>0.045996</td>\n",
+       "      <td>0.034587</td>\n",
+       "      <td>0.006173</td>\n",
+       "      <td>00:24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>0.025280</td>\n",
+       "      <td>0.018724</td>\n",
+       "      <td>0.006173</td>\n",
+       "      <td>00:21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>0.015530</td>\n",
+       "      <td>0.016226</td>\n",
+       "      <td>0.006173</td>\n",
+       "      <td>00:21</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "learn = vision_learner(dls, resnet34, metrics=error_rate)\n",
+    "learn.fine_tune(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "477ef53a-4b5c-4a07-81c2-95b8e7397cac",
+   "metadata": {},
+   "source": [
+    "We could try a better model, based on [this analysis](https://www.kaggle.com/code/jhoward/which-image-models-are-best/). The convnext models work great!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ee4197a-25be-48ac-a167-903bec5186b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['convnext_atto',\n",
+       " 'convnext_atto_ols',\n",
+       " 'convnext_base',\n",
+       " 'convnext_femto',\n",
+       " 'convnext_femto_ols',\n",
+       " 'convnext_large',\n",
+       " 'convnext_large_mlp',\n",
+       " 'convnext_nano',\n",
+       " 'convnext_nano_ols',\n",
+       " 'convnext_pico',\n",
+       " 'convnext_pico_ols',\n",
+       " 'convnext_small',\n",
+       " 'convnext_tiny',\n",
+       " 'convnext_tiny_hnf',\n",
+       " 'convnext_xlarge',\n",
+       " 'convnext_xxlarge',\n",
+       " 'convnextv2_atto',\n",
+       " 'convnextv2_base',\n",
+       " 'convnextv2_femto',\n",
+       " 'convnextv2_huge',\n",
+       " 'convnextv2_large',\n",
+       " 'convnextv2_nano',\n",
+       " 'convnextv2_pico',\n",
+       " 'convnextv2_small',\n",
+       " 'convnextv2_tiny']"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "timm.list_models('convnext*')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67d34f88-a580-48b9-9b42-e9d0c7e3e870",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading model.safetensors: 100%|██████████| 114M/114M [00:15<00:00, 7.33MB/s] \n",
+      "C:\\Users\\hh\\anaconda3\\envs\\cuda118\\lib\\site-packages\\huggingface_hub\\file_download.py:133: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\hh\\.cache\\huggingface\\hub. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
+      "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to see activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
+      "  warnings.warn(message)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      <progress value='0' class='' max='1' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      0.00% [0/1 00:00&lt;?]\n",
+       "    </div>\n",
+       "    \n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: left;\">\n",
+       "      <th>epoch</th>\n",
+       "      <th>train_loss</th>\n",
+       "      <th>valid_loss</th>\n",
+       "      <th>error_rate</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table><p>\n",
+       "\n",
+       "    <div>\n",
+       "      <progress value='1' class='' max='10' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      10.00% [1/10 00:04&lt;00:44]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "learn = vision_learner(dls, 'convnext_tiny', metrics=error_rate).to_fp16()\n",
+    "learn.fine_tune(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5171c7fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn.export('model.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33db2772-5334-4d7c-92b8-513b94de17e2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}