Submission Made

.gitignore

@@ -7,4 +7,5 @@ bounding_box/
 __pycache__/
 Object-detection-using-Faster-RCNN
 data_4/
-data_rcnn/
+data_rcnn/
+*.zip

Starlight_Analysts_Submission/other_files/YOLOv5.ipynb

@@ -0,0 +1,172 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "\n",
+    "from ultralytics import YOLO"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "LABELS = {\n",
+    "    0: \"aegypti\",\n",
+    "    1: \"albopictus\",\n",
+    "    2: \"anopheles\",\n",
+    "    3: \"culex\",\n",
+    "    4: \"culiseta\",\n",
+    "    5: \"japonicus/koreicus\"\n",
+    "}\n",
+    "\n",
+    "# Bounding Box Format: [x_center, y_center, width, height]\n",
+    "# Output Format: [id, ImageID, LabelName, Conf, xcenter, ycenter, bbx_width, bbx_height] (Indexed from 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Loading Model\n",
+    "model = YOLO(model='yolov5nu.pt', task='detect', verbose=True)\n",
+    "\n",
+    "# Loading Model in GPU\n",
+    "model = model.cuda()\n",
+    "\n",
+    "arguments = {\n",
+    "    \"project\": \"yolo-experiments\",\n",
+    "    \"name\": \"yolov5n-9\",\n",
+    "    \"data\": \"./data.yaml\",\n",
+    "    \"imgsz\": 640,\n",
+    "    \"batch\": 12,\n",
+    "    \"epochs\": 20,\n",
+    "    \"seed\": 69,\n",
+    "    \"plots\": True,\n",
+    "    \"patience\": 3,\n",
+    "    \n",
+    "    # \"box\": 8,\n",
+    "    # \"cls\": 6,\n",
+    "    # \"pose\": 2,\n",
+    "    # \"hsv_s\": 0.05,\n",
+    "    # \"scale\": 0.0,\n",
+    "    # \"translate\": 0.0,\n",
+    "    # \"fliplr\": 0.0,\n",
+    "    # \"mosaic\": 0.0,\n",
+    "    # \"erasing\": 0.0,\n",
+    "    # \"hsv_v\": 0.05,\n",
+    "    # \"crop_fraction\": 0.1\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Model Training\n",
+    "results = model.train(**arguments)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "TRAIN_DIR = \"../data_3/images/train\"\n",
+    "VAL_DIR = \"../data_3/images/val\"\n",
+    "TEST_DIR = \"../data_3/images/test\"\n",
+    "\n",
+    "train_images = os.listdir(TRAIN_DIR)\n",
+    "val_images = os.listdir(VAL_DIR)\n",
+    "test_images = os.listdir(TEST_DIR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_test_dataframe = pd.DataFrame(columns=[\"id\", \"ImageID\", \"LabelName\", \"Conf\", \"xcenter\", \"ycenter\", \"bbx_width\", \"bbx_height\"])\n",
+    "\n",
+    "# model = YOLO(\"./yolo-experiments/yolov8m-2/weights/best.pt\", task=\"detect\", verbose=True)\n",
+    "# model = YOLO(model=\"./yolo-experiments/yolov8n-42/weights/best.pt\", task=\"detect\", verbose=True)\n",
+    "\n",
+    "results = model(TEST_DIR, stream=True, conf=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for id, r in enumerate(results):\n",
+    "    r = r.cpu()\n",
+    "    img_path = r.path\n",
+    "    img_name = img_path.split(\"/\")[-1]\n",
+    "    boxes = r.boxes.numpy()\n",
+    "    label = boxes.cls\n",
+    "    conf = boxes.conf\n",
+    "    xywh = boxes.xywhn\n",
+    "    \n",
+    "    # print(label)\n",
+    "    # print(conf)\n",
+    "    \n",
+    "    new_row = {\"id\": id, \"ImageID\": img_name, \"LabelName\": LABELS[int(label[0])], \"Conf\": conf[0], \"xcenter\": xywh[:, 0][0], \"ycenter\": xywh[:, 1][0], \"bbx_width\": xywh[:, 2][0], \"bbx_height\": xywh[:, 3][0]}\n",
+    "    results_test_dataframe = pd.concat([results_test_dataframe, pd.DataFrame([new_row])], ignore_index=True, copy=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_test_dataframe.to_csv(\"./submissions/results_test_8.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}