{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# [Happywhale - Whale and Dolphin Identification](https://www.kaggle.com/c/happy-whale-and-dolphin)\n> Identify whales and dolphins by unique characteristic\n\n<img src=\"https://storage.googleapis.com/kaggle-competitions/kaggle/22962/logos/header.png?t=2021-03-17-22-44-09\">","metadata":{}},{"cell_type":"markdown","source":"# 📌 Methodology\n* In this notebook, we'll generate **bounding box** using **YOLOv5**.\n* This notebook mostly based on this notebook, [Great-Barrier-Reef: YOLOv5 [train] 🌊](https://www.kaggle.com/awsaf49/great-barrier-reef-yolov5-train).\n* For train and test data we'll use only **Whale Flute**. We have total **1200** samples with **bounding box**.\n* Finally, we'll use **Whale Flute** model to predict on our **Whale and Dolphin** dataset.\n\n> **Caution:** As this is **Out of Distribution (OOD)** problem, there might be some **False Positives (FP)**. It is recommended to use this dataset carefully.","metadata":{}},{"cell_type":"markdown","source":"# ⌛ Tips\n* You can try **large** models such as **YOLOv5x6** with large imge_size such as `640x640` or `768x768`.\n* Bounding Boxes in **Whales Fluke** dataset are **large** whereas **Whales & Dolphin** dataset has both **small** and **large** bounding box. To adjust this issue you can try changing the **scale** parameter in the **hyp.yaml** file. The default value is `0.5`, you can try increasing the value.\n<div align=\"center\"><img src=\"https://i.ibb.co/BZNh4Qt/areaplot.png\" alt=\"areaplot\" border=\"0\" width=500></div>\n* You can also try enlarging the bbox for example `1.5x or 1.7x`. This will make sure you don't crop the **Whale/Dolphin**.\n* You can tune the **confidence** and **iou** parameter in **YOLOv5** to get a better bounding box. Just to you know there are images with **multiple dolphins or whales** so choose the **confidence** and **iou** accordingly.\n* And of course you can try **ensembling** multiple models using **nms** or **wbf**.\n* Finally, to tackle the **OOD** task you can try a few rounds of **Pseudo Training**, which will help the model to adapt for **Whale & Dolphin** dataset.","metadata":{}},{"cell_type":"markdown","source":"# 📒 Notebooks \nHere are some of my notebooks for this competition, **please upvote if you find them useful**\n* [Happywhale: BoundingBox [YOLOv5] 🐋🐬](https://www.kaggle.com/awsaf49/happywhale-boundingbox-yolov5)\n* [Happywhale: Cropped Dataset [YOLOv5] ✂️](https://www.kaggle.com/awsaf49/happywhale-cropped-dataset-yolov5)\n* [Happywhale: Data Distribution 🐋🐬](https://www.kaggle.com/awsaf49/happywhale-data-distribution)","metadata":{}},{"cell_type":"markdown","source":"# 🛠 Install Libraries","metadata":{}},{"cell_type":"code","source":"!pip install -qU wandb\n!pip install -qU bbox-utility # check https://github.com/awsaf49/bbox for source code\n!pip install -q imagesize","metadata":{"_kg_hide-output":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-02-06T03:46:46.363237Z","iopub.execute_input":"2022-02-06T03:46:46.363756Z","iopub.status.idle":"2022-02-06T03:47:15.063473Z","shell.execute_reply.started":"2022-02-06T03:46:46.363663Z","shell.execute_reply":"2022-02-06T03:47:15.062667Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 📚 Import Libraries","metadata":{}},{"cell_type":"code","source":"import numpy as np\nfrom tqdm.notebook import tqdm\n\ntqdm.pandas()\nimport pandas as pd\nimport os\nimport cv2\nimport matplotlib.pyplot as plt\nimport glob\n\nimport imagesize\nimport shutil\n\nfrom joblib import Parallel, delayed\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-02-06T03:47:15.067215Z","iopub.execute_input":"2022-02-06T03:47:15.067449Z","iopub.status.idle":"2022-02-06T03:47:15.441916Z","shell.execute_reply.started":"2022-02-06T03:47:15.067421Z","shell.execute_reply":"2022-02-06T03:47:15.441178Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Please Upvote if you find this Helpful","metadata":{}},{"cell_type":"markdown","source":"# ⭐ WandB\n<img src=\"https://camo.githubusercontent.com/dd842f7b0be57140e68b2ab9cb007992acd131c48284eaf6b1aca758bfea358b/68747470733a2f2f692e696d6775722e636f6d2f52557469567a482e706e67\" width=600>\n\nWeights & Biases (W&B) is MLOps platform for tracking our experiemnts. We can use it to Build better models faster with experiment tracking, dataset versioning, and model management. Some of the cool features of W&B:\n\n* Track, compare, and visualize ML experiments\n* Get live metrics, terminal logs, and system stats streamed to the centralized dashboard.\n* Explain how your model works, show graphs of how model versions improved, discuss bugs, and demonstrate progress towards milestones.\n","metadata":{}},{"cell_type":"code","source":"import wandb\n\ntry:\n    from kaggle_secrets import UserSecretsClient\n\n    user_secrets = UserSecretsClient()\n    api_key = user_secrets.get_secret(\"WANDB\")\n    wandb.login(key=api_key)\n    anonymous = None\nexcept:\n    wandb.login(anonymous=\"must\")\n    print(\n        \"To use your W&B account,\\nGo to Add-ons -> Secrets and provide your W&B access token. Use the Label name as WANDB. \\nGet your W&B access token from here: https://wandb.ai/authorize\"\n    )\n","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:47:15.443061Z","iopub.execute_input":"2022-02-06T03:47:15.443479Z","iopub.status.idle":"2022-02-06T03:47:17.480922Z","shell.execute_reply.started":"2022-02-06T03:47:15.443442Z","shell.execute_reply":"2022-02-06T03:47:17.48006Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 📈 Meta Data\n* `train_images/` - a folder containing the training images\n* `train.csv` - provides the species and the individual_id for each of the training images\n* `test_images/` - a folder containing the test images; for each image, your task is to predict the individual_id; no species information is given for the test data; there are individuals in the test data that are not observed in the training data, which should be predicted as new_individual.\n* `sample_submission.csv` - a sample submission file in the correct format\n\n> Note: We don't have access to `species` column for **test** data. So, we can't direcly use `species` for **train**.","metadata":{}},{"cell_type":"code","source":"FOLD = 0  # which fold to train\nDIM = 640\nMODEL = \"yolov5x\"\nBATCH = 16\nEPOCHS = 15\nOPTMIZER = \"SGD\"\n\nPROJECT = \"happywhale-det-public\"  # w&b in yolov5\nNAME = f\"{MODEL}-dim{DIM}-fold{FOLD}\"  # w&b for yolov5\n\nROOT_DIR = \"/kaggle/input/whale-categorization-playground\"\nIMAGE_DIR = \"/kaggle/data1/images\"  # directory to save images\nLABEL_DIR = \"/kaggle/data1/labels\"  # directory to save labels\n","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:47:17.482652Z","iopub.execute_input":"2022-02-06T03:47:17.482936Z","iopub.status.idle":"2022-02-06T03:47:17.48833Z","shell.execute_reply.started":"2022-02-06T03:47:17.48289Z","shell.execute_reply":"2022-02-06T03:47:17.48766Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Create Directories","metadata":{}},{"cell_type":"code","source":"!mkdir -p {IMAGE_DIR}\n!mkdir -p {LABEL_DIR}","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:47:17.490782Z","iopub.execute_input":"2022-02-06T03:47:17.491214Z","iopub.status.idle":"2022-02-06T03:47:18.792398Z","shell.execute_reply.started":"2022-02-06T03:47:17.491175Z","shell.execute_reply":"2022-02-06T03:47:18.791447Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Get Paths","metadata":{}},{"cell_type":"code","source":"# Train Data\ndf = pd.read_csv(f\"{ROOT_DIR}/train.csv\")\ndf[\"image_id\"] = df[\"Image\"]\ndf[\"old_image_path\"] = f\"{ROOT_DIR}/train/\" + df.image_id.astype(str)\ndf[\"image_path\"] = f\"{IMAGE_DIR}/\" + df.image_id\ndf[\"label_path\"] = f\"{LABEL_DIR}/\" + df.image_id.str.replace(\"jpg\", \"txt\")\ndf.head(2)\n","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:47:18.79409Z","iopub.execute_input":"2022-02-06T03:47:18.794336Z","iopub.status.idle":"2022-02-06T03:47:18.876966Z","shell.execute_reply.started":"2022-02-06T03:47:18.794301Z","shell.execute_reply":"2022-02-06T03:47:18.876278Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ✏️ Write Images\n* We need to copy the Images to Current Directory(`/kaggle/working`) as `/kaggle/input` doesn't have **write access** which is needed for **YOLOv5**.\n* We can make this process faster using **Joblib** which uses **Parallel** computing.","metadata":{}},{"cell_type":"code","source":"def make_copy(row):\n    shutil.copyfile(row.old_image_path, row.image_path)\n    return","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:47:18.87817Z","iopub.execute_input":"2022-02-06T03:47:18.878428Z","iopub.status.idle":"2022-02-06T03:47:18.883183Z","shell.execute_reply.started":"2022-02-06T03:47:18.878395Z","shell.execute_reply":"2022-02-06T03:47:18.88231Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"image_paths = df.old_image_path.tolist()\n_ = Parallel(n_jobs=-1, backend='threading')(delayed(make_copy)(row) for _, row in tqdm(df.iterrows(), total=len(df)))","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:47:18.884631Z","iopub.execute_input":"2022-02-06T03:47:18.88515Z","iopub.status.idle":"2022-02-06T03:47:52.488411Z","shell.execute_reply.started":"2022-02-06T03:47:18.885112Z","shell.execute_reply":"2022-02-06T03:47:52.487777Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🔨 Helper","metadata":{}},{"cell_type":"code","source":"# check https://github.com/awsaf49/bbox for source code of following utility functions\nfrom bbox.utils import coco2yolo, coco2voc, voc2yolo, yolo2voc\nfrom bbox.utils import draw_bboxes, load_image\nfrom bbox.utils import clip_bbox, str2annot, annot2str\n\ndef get_bbox(annots):\n    bboxes = [list(annot.values()) for annot in annots]\n    return bboxes\n\ndef get_imgsize(row):\n    row['width'], row['height'] = imagesize.get(row['image_path'])\n    return row\n\nnp.random.seed(32)\ncolors = [(np.random.randint(255), np.random.randint(255), np.random.randint(255))\\\n          for idx in range(1)]","metadata":{"_kg_hide-input":false,"execution":{"iopub.status.busy":"2022-02-06T03:47:52.489672Z","iopub.execute_input":"2022-02-06T03:47:52.489953Z","iopub.status.idle":"2022-02-06T03:47:53.124247Z","shell.execute_reply.started":"2022-02-06T03:47:52.489916Z","shell.execute_reply":"2022-02-06T03:47:53.123533Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Create BBox","metadata":{}},{"cell_type":"code","source":"def point2bbox(points):\n    points = np.array(points)\n    points = points.astype('int') # str -> int\n    points = points.reshape(-1, 2) # shape: (None, ) -> shape: (None, 2) => (x, y) format\n    xmin, ymin, xmax, ymax = points[:, 0].min(), points[:, 1].min(), points[:, 0].max(), points[:, 1].max()\n    return [[xmin, ymin, xmax, ymax]]","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:47:53.125548Z","iopub.execute_input":"2022-02-06T03:47:53.125808Z","iopub.status.idle":"2022-02-06T03:47:53.130789Z","shell.execute_reply.started":"2022-02-06T03:47:53.125774Z","shell.execute_reply":"2022-02-06T03:47:53.130124Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"f = open('/kaggle/input/humpback-whale-identification-fluke-location/cropping.txt','rt').read()\nid2point = {x.split(',')[0]:x.split(',')[1:] for x in f.split('\\n')}\ndf['point'] = df['image_id'].map(id2point)\ndf = df[~df.point.isna()]\ndf['bbox'] = df.point.map(point2bbox)","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:47:53.132105Z","iopub.execute_input":"2022-02-06T03:47:53.13244Z","iopub.status.idle":"2022-02-06T03:47:53.43224Z","shell.execute_reply.started":"2022-02-06T03:47:53.132406Z","shell.execute_reply":"2022-02-06T03:47:53.431535Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Get Image-Size","metadata":{}},{"cell_type":"code","source":"df = df.progress_apply(get_imgsize, axis=1)\ndisplay(df.head(2))","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:47:53.433635Z","iopub.execute_input":"2022-02-06T03:47:53.433897Z","iopub.status.idle":"2022-02-06T03:47:54.79363Z","shell.execute_reply.started":"2022-02-06T03:47:53.433864Z","shell.execute_reply":"2022-02-06T03:47:54.792815Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🏷️ Create Labels\nWe need to export our labels to **YOLO** format, with one `*.txt` file per image (if no objects in image, no `*.txt` file is required). The *.txt file specifications are:\n\n* One row per object\n* Each row is class `[x_center, y_center, width, height]` format.\n* Box coordinates must be in **normalized** `xywh` format (from `0 - 1`). If your boxes are in pixels, divide `x_center` and `width` by `image width`, and `y_center` and `height` by `image height`.\n* Class numbers are **zero-indexed** (start from `0`).\n\n> Dataset bbox format is **VOC-PASCAL** hence `[x_min, y_min, x_max, y_max]`. So, we need to convert form **VOC-PASCAL** to **YOLO** format.\n","metadata":{}},{"cell_type":"code","source":"cnt = 0\nall_bboxes = []\nbboxes_info = []\nfor row_idx in tqdm(range(df.shape[0])):\n    row = df.iloc[row_idx]\n    image_height = row.height\n    image_width = row.width\n    bboxes_voc = np.array(row.bbox).astype(np.float32).copy()\n    num_bbox = len(bboxes_voc)\n    names = [\"whale\"] * num_bbox\n    labels = np.array([0] * num_bbox)[..., None].astype(str)\n    ## Create Annotation(YOLO)\n    with open(row.label_path, \"w\") as f:\n        if num_bbox < 1:\n            annot = \"\"\n            f.write(annot)\n            cnt += 1\n            continue\n        #         bboxes_voc  = coco2voc(bboxes_coco, image_height, image_width)\n        bboxes_voc = clip_bbox(bboxes_voc, image_height, image_width)\n        bboxes_yolo = voc2yolo(bboxes_voc, image_height, image_width).astype(str)\n        all_bboxes.extend(bboxes_yolo.astype(float))\n        bboxes_info.extend([[row.image_id]] * len(bboxes_yolo))\n        annots = np.concatenate([labels, bboxes_yolo], axis=1)\n        string = annot2str(annots)\n        f.write(string)\nprint(\"Missing:\", cnt)\n","metadata":{"_kg_hide-input":false,"execution":{"iopub.status.busy":"2022-02-06T03:47:54.795032Z","iopub.execute_input":"2022-02-06T03:47:54.795289Z","iopub.status.idle":"2022-02-06T03:47:57.041035Z","shell.execute_reply.started":"2022-02-06T03:47:54.795251Z","shell.execute_reply":"2022-02-06T03:47:57.040304Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 📁 Create Folds\n> Number of samples aren't same in each fold which can create large variance in **Cross-Validation**.","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import KFold\nkf = KFold(n_splits=6, random_state=42, shuffle=True)\ndf = df.reset_index(drop=True)\ndf['fold'] = -1\nfor fold, (train_idx, val_idx) in enumerate(kf.split(df)):\n    df.loc[val_idx, 'fold'] = fold\ndf.fold.value_counts()","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:47:57.044972Z","iopub.execute_input":"2022-02-06T03:47:57.045316Z","iopub.status.idle":"2022-02-06T03:47:57.643705Z","shell.execute_reply.started":"2022-02-06T03:47:57.045285Z","shell.execute_reply":"2022-02-06T03:47:57.642999Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ⭕ BBox Distribution","metadata":{}},{"cell_type":"code","source":"bbox_df = pd.DataFrame(np.concatenate([bboxes_info, all_bboxes], axis=1),\n             columns=['image_id','xmid','ymid','w','h'])\nbbox_df[['xmid','ymid','w','h']] = bbox_df[['xmid','ymid','w','h']].astype(float)\nbbox_df['area'] = bbox_df.w * bbox_df.h\nbbox_df = bbox_df.merge(df[['image_id','fold']], on='image_id', how='left')\nbbox_df.head(2)","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:47:57.647549Z","iopub.execute_input":"2022-02-06T03:47:57.648046Z","iopub.status.idle":"2022-02-06T03:47:57.696541Z","shell.execute_reply.started":"2022-02-06T03:47:57.648008Z","shell.execute_reply":"2022-02-06T03:47:57.695791Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## `x_center` Vs `y_center`","metadata":{}},{"cell_type":"code","source":"from scipy.stats import gaussian_kde\n\nall_bboxes = np.array(all_bboxes)\n\nx_val = all_bboxes[...,0]\ny_val = all_bboxes[...,1]\n\n# Calculate the point density\nxy = np.vstack([x_val,y_val])\nz = gaussian_kde(xy)(xy)\n\nfig, ax = plt.subplots(figsize = (10, 10))\n# ax.axis('off')\nax.scatter(x_val, y_val, c=z, s=50, cmap='viridis')\n# ax.set_xlabel('x_mid')\n# ax.set_ylabel('y_mid')\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-02-06T03:47:57.700179Z","iopub.execute_input":"2022-02-06T03:47:57.702518Z","iopub.status.idle":"2022-02-06T03:47:58.033895Z","shell.execute_reply.started":"2022-02-06T03:47:57.702471Z","shell.execute_reply":"2022-02-06T03:47:58.033203Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## `width` Vs `height`","metadata":{}},{"cell_type":"code","source":"x_val = all_bboxes[...,2]\ny_val = all_bboxes[...,3]\n\n# Calculate the point density\nxy = np.vstack([x_val,y_val])\nz = gaussian_kde(xy)(xy)\n\nfig, ax = plt.subplots(figsize = (10, 10))\n# ax.axis('off')\nax.scatter(x_val, y_val, c=z, s=50, cmap='viridis')\n# ax.set_xlabel('bbox_width')\n# ax.set_ylabel('bbox_height')\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-02-06T03:47:58.03483Z","iopub.execute_input":"2022-02-06T03:47:58.035097Z","iopub.status.idle":"2022-02-06T03:47:58.351687Z","shell.execute_reply.started":"2022-02-06T03:47:58.035051Z","shell.execute_reply":"2022-02-06T03:47:58.350998Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Area","metadata":{}},{"cell_type":"code","source":"import matplotlib as mpl\nimport seaborn as sns\n\nf, ax = plt.subplots(figsize=(12, 6))\nsns.despine(f)\n\nsns.histplot(\n    bbox_df,\n    x=\"area\", hue=\"fold\",\n    multiple=\"stack\",\n    palette=\"viridis\",\n    edgecolor=\".3\",\n    linewidth=.5,\n    log_scale=True,\n)\nax.xaxis.set_major_formatter(mpl.ticker.ScalarFormatter())","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-02-06T03:47:58.353463Z","iopub.execute_input":"2022-02-06T03:47:58.35394Z","iopub.status.idle":"2022-02-06T03:47:59.445503Z","shell.execute_reply.started":"2022-02-06T03:47:58.353902Z","shell.execute_reply":"2022-02-06T03:47:59.444805Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🌈 Visualization","metadata":{}},{"cell_type":"code","source":"df2 = df.sample(100) # takes samples with bbox\ny = 2\nx = 5\nplt.figure(figsize=(4 * x, 4 * y))\nfor idx in range(x*y):\n    row = df2.iloc[idx]\n    img           = load_image(row.image_path)\n    img           = cv2.resize(img, (512, 512))\n    image_height  = row.height\n    image_width   = row.width\n    with open(row.label_path) as f:\n        annot = str2annot(f.read())\n    bboxes_yolo = annot[...,1:]\n    labels      = annot[..., 0].astype(int).tolist()\n    names         = ['whale']*len(bboxes_yolo)\n    plt.subplot(y, x, idx+1)\n    plt.imshow(draw_bboxes(img = img,\n                           bboxes = bboxes_yolo, \n                           classes = names,\n                           class_ids = labels,\n                           class_name = True, \n                           colors = colors, \n                           bbox_format = 'yolo',\n                           line_thickness = 2))\n    plt.axis('OFF')\nplt.tight_layout()\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-02-06T03:47:59.446654Z","iopub.execute_input":"2022-02-06T03:47:59.44724Z","iopub.status.idle":"2022-02-06T03:48:00.430977Z","shell.execute_reply.started":"2022-02-06T03:47:59.447189Z","shell.execute_reply":"2022-02-06T03:48:00.429652Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🍚 Dataset","metadata":{}},{"cell_type":"code","source":"train_files = []\nval_files   = []\ntrain_df = df.query(\"fold!=@FOLD\")\nvalid_df = df.query(\"fold==@FOLD\")\ntrain_files += list(train_df.image_path.unique())\nval_files += list(valid_df.image_path.unique())\nlen(train_files), len(val_files)","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:48:00.432012Z","iopub.execute_input":"2022-02-06T03:48:00.432357Z","iopub.status.idle":"2022-02-06T03:48:00.451127Z","shell.execute_reply.started":"2022-02-06T03:48:00.432326Z","shell.execute_reply":"2022-02-06T03:48:00.450414Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ⚙️ Configuration\nThe dataset config file requires\n1. The dataset root directory path and relative paths to `train / val / test` image directories (or *.txt files with image paths)\n2. The number of classes `nc` and \n3. A list of class `names`:`['cots']`","metadata":{}},{"cell_type":"code","source":"import yaml\n\ncwd = '/kaggle/working/'\n\nwith open(os.path.join( cwd , 'train.txt'), 'w') as f:\n    for path in train_df.image_path.tolist():\n        f.write(path+'\\n')\n            \nwith open(os.path.join(cwd , 'val.txt'), 'w') as f:\n    for path in valid_df.image_path.tolist():\n        f.write(path+'\\n')\n\ndata = dict(\n    path  = cwd,\n    train =  os.path.join( cwd , 'train.txt') ,\n    val   =  os.path.join( cwd , 'val.txt' ),\n    nc    = 1,\n    names = ['whale'],\n    )\n\nwith open(os.path.join( cwd , 'happywhale.yaml'), 'w') as outfile:\n    yaml.dump(data, outfile, default_flow_style=False)\n\nf = open(os.path.join( cwd , 'happywhale.yaml'), 'r')\nprint('\\nyaml:')\nprint(f.read())","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-02-06T03:48:00.452286Z","iopub.execute_input":"2022-02-06T03:48:00.452803Z","iopub.status.idle":"2022-02-06T03:48:00.46668Z","shell.execute_reply.started":"2022-02-06T03:48:00.452768Z","shell.execute_reply":"2022-02-06T03:48:00.466011Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%writefile /kaggle/working/hyp.yaml\nlr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)\nlrf: 0.1  # final OneCycleLR learning rate (lr0 * lrf)\nmomentum: 0.937  # SGD momentum/Adam beta1\nweight_decay: 0.0005  # optimizer weight decay 5e-4\nwarmup_epochs: 3.0  # warmup epochs (fractions ok)\nwarmup_momentum: 0.8  # warmup initial momentum\nwarmup_bias_lr: 0.1  # warmup initial bias lr\nbox: 0.05  # box loss gain\ncls: 0.5  # cls loss gain\ncls_pw: 1.0  # cls BCELoss positive_weight\nobj: 1.0  # obj loss gain (scale with pixels)\nobj_pw: 1.0  # obj BCELoss positive_weight\niou_t: 0.20  # IoU training threshold\nanchor_t: 4.0  # anchor-multiple threshold\n# anchors: 3  # anchors per output layer (0 to ignore)\nfl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)\nhsv_h: 0.015  # image HSV-Hue augmentation (fraction)\nhsv_s: 0.7  # image HSV-Saturation augmentation (fraction)\nhsv_v: 0.4  # image HSV-Value augmentation (fraction)\ndegrees: 30.0  # image rotation (+/- deg)\ntranslate: 0.10  # image translation (+/- fraction)\nscale: 0.80  # image scale (+/- gain)\nshear: 10.0  # image shear (+/- deg)\nperspective: 0.0  # image perspective (+/- fraction), range 0-0.001\nflipud: 0.5  # image flip up-down (probability)\nfliplr: 0.5  # image flip left-right (probability)\nmosaic: 0.75  # image mosaic (probability)\nmixup: 0.0 # image mixup (probability)\ncopy_paste: 0.0  # segment copy-paste (probability)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-02-06T03:48:00.468007Z","iopub.execute_input":"2022-02-06T03:48:00.468528Z","iopub.status.idle":"2022-02-06T03:48:00.476658Z","shell.execute_reply.started":"2022-02-06T03:48:00.468485Z","shell.execute_reply":"2022-02-06T03:48:00.475821Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 📦 [YOLOv5](https://github.com/ultralytics/yolov5/)\n<div align=center><img src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/splash.jpg\" width=600>","metadata":{}},{"cell_type":"code","source":"%cd /kaggle/working\n!rm -r /kaggle/working/yolov5\n# !git clone https://github.com/ultralytics/yolov5 # clone\n!cp -r /kaggle/input/yolov5-lib-ds /kaggle/working/yolov5\n%cd yolov5\n%pip install -qr requirements.txt  # install\n\nfrom yolov5 import utils\n_ = utils.notebook_init()  # check","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:48:00.478011Z","iopub.execute_input":"2022-02-06T03:48:00.479196Z","iopub.status.idle":"2022-02-06T03:48:22.252201Z","shell.execute_reply.started":"2022-02-06T03:48:00.479018Z","shell.execute_reply":"2022-02-06T03:48:22.251327Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🚅 Training\nUncomment following cell for **training**. I'm using previously **trained** weights from earlier **version** of this notebook.","metadata":{}},{"cell_type":"code","source":"!python train.py --img {DIM}\\\n--batch {BATCH}\\\n--epochs {EPOCHS}\\\n--optimizer {OPTMIZER}\\\n--data /kaggle/working/happywhale.yaml\\\n--hyp /kaggle/working/hyp.yaml\\\n--weights {MODEL}.pt\\\n--project {PROJECT} --name {NAME}\\\n--exist-ok","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-02-06T03:48:22.254177Z","iopub.execute_input":"2022-02-06T03:48:22.254456Z","iopub.status.idle":"2022-02-06T03:48:22.260238Z","shell.execute_reply.started":"2022-02-06T03:48:22.254418Z","shell.execute_reply":"2022-02-06T03:48:22.259327Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ✨ Overview\n<span style=\"color: #000508; font-family: Segoe UI; font-size: 1.5em; font-weight: 300;\"><a href=\"https://wandb.ai/awsaf49/happywhale-det-public\">View the Complete Dashboard Here ⮕</a></span>\n![image.png](attachment:9d581a53-dd94-4f69-bd96-864ad0e7ceae.png)","metadata":{},"attachments":{"9d581a53-dd94-4f69-bd96-864ad0e7ceae.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"## Output Files","metadata":{}},{"cell_type":"code","source":"OUTPUT_DIR = '{}/{}'.format(PROJECT, NAME)\n!ls {OUTPUT_DIR}","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:48:22.263717Z","iopub.execute_input":"2022-02-06T03:48:22.263941Z","iopub.status.idle":"2022-02-06T03:48:23.143266Z","shell.execute_reply.started":"2022-02-06T03:48:22.263916Z","shell.execute_reply":"2022-02-06T03:48:23.142435Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!ls {OUTPUT_DIR}/weights/best.pt","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:48:23.144881Z","iopub.execute_input":"2022-02-06T03:48:23.145151Z","iopub.status.idle":"2022-02-06T03:48:23.802246Z","shell.execute_reply.started":"2022-02-06T03:48:23.145114Z","shell.execute_reply":"2022-02-06T03:48:23.801428Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🔭 Batch Image","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nplt.figure(figsize = (10, 10))\nplt.imshow(plt.imread(f'{OUTPUT_DIR}/train_batch0.jpg'))\n\nplt.figure(figsize = (10, 10))\nplt.imshow(plt.imread(f'{OUTPUT_DIR}/train_batch1.jpg'))\n\nplt.figure(figsize = (10, 10))\nplt.imshow(plt.imread(f'{OUTPUT_DIR}/train_batch2.jpg'))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-02-06T03:48:23.804027Z","iopub.execute_input":"2022-02-06T03:48:23.804288Z","iopub.status.idle":"2022-02-06T03:48:26.368645Z","shell.execute_reply.started":"2022-02-06T03:48:23.804252Z","shell.execute_reply":"2022-02-06T03:48:26.36801Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## GT Vs Pred","metadata":{}},{"cell_type":"code","source":"fig, ax = plt.subplots(3, 2, figsize = (2*9,3*5), constrained_layout = True)\nfor row in range(3):\n    ax[row][0].imshow(plt.imread(f'{OUTPUT_DIR}/val_batch{row}_labels.jpg'))\n    ax[row][0].set_xticks([])\n    ax[row][0].set_yticks([])\n    ax[row][0].set_title(f'{OUTPUT_DIR}/val_batch{row}_labels.jpg', fontsize = 12)\n    \n    ax[row][1].imshow(plt.imread(f'{OUTPUT_DIR}/val_batch{row}_pred.jpg'))\n    ax[row][1].set_xticks([])\n    ax[row][1].set_yticks([])\n    ax[row][1].set_title(f'{OUTPUT_DIR}/val_batch{row}_pred.jpg', fontsize = 12)\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-02-06T03:48:26.370039Z","iopub.execute_input":"2022-02-06T03:48:26.370521Z","iopub.status.idle":"2022-02-06T03:48:28.46929Z","shell.execute_reply.started":"2022-02-06T03:48:26.370482Z","shell.execute_reply":"2022-02-06T03:48:28.468591Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🔍 Result","metadata":{}},{"cell_type":"markdown","source":"## Score Vs Epoch","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(30,15))\nplt.axis('off')\nplt.imshow(plt.imread(f'{OUTPUT_DIR}/results.png'));","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-02-06T03:48:28.47052Z","iopub.execute_input":"2022-02-06T03:48:28.470867Z","iopub.status.idle":"2022-02-06T03:48:29.5499Z","shell.execute_reply.started":"2022-02-06T03:48:28.470834Z","shell.execute_reply":"2022-02-06T03:48:29.549252Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Confusion Matrix","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(12,10))\nplt.axis('off')\nplt.imshow(plt.imread(f'{OUTPUT_DIR}/confusion_matrix.png'));","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-02-06T03:48:29.551301Z","iopub.execute_input":"2022-02-06T03:48:29.551782Z","iopub.status.idle":"2022-02-06T03:48:31.597551Z","shell.execute_reply.started":"2022-02-06T03:48:29.551746Z","shell.execute_reply":"2022-02-06T03:48:31.596831Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Metrics","metadata":{}},{"cell_type":"code","source":"for metric in ['F1', 'PR', 'P', 'R']:\n    print(f'Metric: {metric}')\n    plt.figure(figsize=(12,10))\n    plt.axis('off')\n    plt.imshow(plt.imread(f'{OUTPUT_DIR}/{metric}_curve.png'));\n    plt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-02-06T03:48:31.598937Z","iopub.execute_input":"2022-02-06T03:48:31.599363Z","iopub.status.idle":"2022-02-06T03:48:34.297925Z","shell.execute_reply.started":"2022-02-06T03:48:31.599324Z","shell.execute_reply":"2022-02-06T03:48:34.297246Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🐋🐬 **Whale and Dolphin** Data","metadata":{}},{"cell_type":"code","source":"class CFG:\n    seed = 42\n    base_path = '/kaggle/input/happy-whale-and-dolphin'","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:48:34.299228Z","iopub.execute_input":"2022-02-06T03:48:34.299842Z","iopub.status.idle":"2022-02-06T03:48:34.303906Z","shell.execute_reply.started":"2022-02-06T03:48:34.299802Z","shell.execute_reply":"2022-02-06T03:48:34.303242Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_output = '/kaggle/working/output/train'\ntest_output = '/kaggle/working/output/test'","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:48:34.305414Z","iopub.execute_input":"2022-02-06T03:48:34.306Z","iopub.status.idle":"2022-02-06T03:48:34.313672Z","shell.execute_reply.started":"2022-02-06T03:48:34.305901Z","shell.execute_reply":"2022-02-06T03:48:34.312994Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df2 = pd.read_csv(f\"/kaggle/input/happywhale-data-distribution/train.csv\")\ndf2[\"image_id\"] = df2[\"image\"]\ndf2[\"label_path\"] = train_output + \"/labels/\" + df2[\"image_id\"].str.replace('jpg','txt')\n\n\ntest_df2 = pd.read_csv(f\"/kaggle/input/happywhale-data-distribution/test.csv\")\ntest_df2[\"image_id\"] = test_df2[\"image\"]\ntest_df2[\"label_path\"] = test_output + \"/labels/\" + test_df2[\"image_id\"].str.replace('jpg','txt')\n\nprint(\"Train Images: {:,} | Test Images: {:,}\".format(len(df2), len(test_df2)))\n","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:48:34.315183Z","iopub.execute_input":"2022-02-06T03:48:34.315536Z","iopub.status.idle":"2022-02-06T03:48:34.706875Z","shell.execute_reply.started":"2022-02-06T03:48:34.315485Z","shell.execute_reply":"2022-02-06T03:48:34.706062Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Predict on **Train**","metadata":{}},{"cell_type":"code","source":"!rm -rf {train_output}\n!mkdir -p {train_output}","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:48:47.682327Z","iopub.execute_input":"2022-02-06T03:48:47.683054Z","iopub.status.idle":"2022-02-06T03:48:48.989302Z","shell.execute_reply.started":"2022-02-06T03:48:47.683015Z","shell.execute_reply":"2022-02-06T03:48:48.988348Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!python detect.py --img {DIM}\\\n--source /kaggle/input/happy-whale-and-dolphin/train_images\\\n--weights {OUTPUT_DIR}/weights/best.pt\\\n--project /kaggle/working/output --name train\\\n--conf 0.01 --iou 0.4 --max-det 1\\\n--save-txt --save-conf\\\n--nosave\\\n--half\\\n--exist-ok","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:48:51.613526Z","iopub.execute_input":"2022-02-06T03:48:51.614175Z","iopub.status.idle":"2022-02-06T03:51:25.63192Z","shell.execute_reply.started":"2022-02-06T03:48:51.614133Z","shell.execute_reply":"2022-02-06T03:51:25.631037Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Visualize **Train**","metadata":{}},{"cell_type":"code","source":"label_paths = glob.glob(f\"{train_output}/labels/*\")\nnp.random.shuffle(label_paths)\n\ny = 3\nx = 5\nplt.figure(figsize=(4 * x, 4 * y))\nfor idx in range(x * y):\n    label_path = label_paths[idx]\n    image_id = label_path.split(\"/\")[-1].replace(\"txt\", \"jpg\")\n    row = df2[df2.image_id == image_id].squeeze()\n    image_path = f\"/kaggle/input/happy-whale-and-dolphin/train_images/{image_id}\"\n    img = load_image(image_path)\n    img = cv2.resize(img, (512, 512))\n    image_height = row.height\n    image_width = row.width\n    with open(label_path) as f:\n        data = f.read().strip().split(' ')\n    annot = np.array(data).reshape(-1, 6).astype(float)\n    bboxes_yolo = annot[..., 1:]\n    labels = annot[..., 0].astype(int).tolist()\n    names = [\"whale/dolphin\"] * len(bboxes_yolo)\n    plt.subplot(y, x, idx + 1)\n    plt.imshow(\n        draw_bboxes(\n            img=img,\n            bboxes=bboxes_yolo,\n            classes=names,\n            class_ids=labels,\n            class_name=True,\n            colors=colors,\n            bbox_format=\"yolo\",\n            line_thickness=2,\n        )\n    )\n    plt.axis(\"OFF\")\nplt.tight_layout()\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:55:28.074749Z","iopub.execute_input":"2022-02-06T03:55:28.075024Z","iopub.status.idle":"2022-02-06T03:55:30.835302Z","shell.execute_reply.started":"2022-02-06T03:55:28.074993Z","shell.execute_reply":"2022-02-06T03:55:30.834534Z"},"_kg_hide-input":true,"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Predict on **Test**","metadata":{}},{"cell_type":"code","source":"!rm -rf {test_output}\n!mkdir -p {test_output}","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:51:40.227964Z","iopub.execute_input":"2022-02-06T03:51:40.228278Z","iopub.status.idle":"2022-02-06T03:51:41.547795Z","shell.execute_reply.started":"2022-02-06T03:51:40.228242Z","shell.execute_reply":"2022-02-06T03:51:41.546808Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!python detect.py --img {DIM}\\\n--source /kaggle/input/happy-whale-and-dolphin/test_images\\\n--weights {OUTPUT_DIR}/weights/best.pt\\\n--project /kaggle/working/output --name test\\\n--conf 0.001 --iou 0.4 --max-det 1\\\n--save-txt --save-conf\\\n--nosave\\\n--half\\\n--exist-ok","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:51:41.550974Z","iopub.execute_input":"2022-02-06T03:51:41.551611Z","iopub.status.idle":"2022-02-06T03:53:40.530824Z","shell.execute_reply.started":"2022-02-06T03:51:41.551564Z","shell.execute_reply":"2022-02-06T03:53:40.529951Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Visualize **Test**","metadata":{}},{"cell_type":"code","source":"label_paths = glob.glob(f\"{test_output}/labels/*\")\nnp.random.shuffle(label_paths)\n\ny = 3\nx = 5\nplt.figure(figsize=(4 * x, 4 * y))\nfor idx in range(x * y):\n    label_path = label_paths[idx]\n    image_id = label_path.split(\"/\")[-1].replace(\"txt\", \"jpg\")\n    row = test_df2[test_df2.image_id == image_id].squeeze()\n    image_path = f\"/kaggle/input/happy-whale-and-dolphin/test_images/{image_id}\"\n    img = load_image(image_path)\n    img = cv2.resize(img, (512, 512))\n    image_height = row.height\n    image_width = row.width\n    with open(label_path) as f:\n        data = f.read().strip().split(' ')\n    annot = np.array(data).reshape(-1, 6).astype(float)\n    bboxes_yolo = annot[..., 1:]\n    labels = annot[..., 0].astype(int).tolist()\n    names = [\"whale/dolphin\"] * len(bboxes_yolo)\n    plt.subplot(y, x, idx + 1)\n    plt.imshow(\n        draw_bboxes(\n            img=img,\n            bboxes=bboxes_yolo,\n            classes=names,\n            class_ids=labels,\n            class_name=True,\n            colors=colors,\n            bbox_format=\"yolo\",\n            line_thickness=2,\n        )\n    )\n    plt.axis(\"OFF\")\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:55:44.005338Z","iopub.execute_input":"2022-02-06T03:55:44.005623Z","iopub.status.idle":"2022-02-06T03:55:47.19561Z","shell.execute_reply.started":"2022-02-06T03:55:44.005592Z","shell.execute_reply":"2022-02-06T03:55:47.190449Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Process **Annotations**","metadata":{}},{"cell_type":"code","source":"def get_annot(row, with_conf=True):\n    try:\n        with open(row['label_path'], 'r') as f:\n            data = f.read().strip().split(' ')\n    except:\n        return row # don't have any label\n    data = np.array(data).reshape(-1, 6 if with_conf else 5).astype(float)\n    if with_conf:\n        bbox = data[:, 1:-1]\n        conf = data[:, -1]\n        row['conf'] = conf.tolist()\n    else:\n        bbox = data[:, 1:]\n    bbox = yolo2voc(bbox, row['height'], row['width'])\n    bbox = bbox.round().astype(np.int32)\n    row['bbox'] = bbox.tolist()\n    return row","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:39:34.886323Z","iopub.execute_input":"2022-02-06T03:39:34.886591Z","iopub.status.idle":"2022-02-06T03:39:34.895056Z","shell.execute_reply.started":"2022-02-06T03:39:34.886559Z","shell.execute_reply":"2022-02-06T03:39:34.894294Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df2 = df2.progress_apply(get_annot, axis=1)\ndf2.to_csv('/kaggle/working/train.csv',index=False)\ndisplay(df2.head())\n\ntest_df2 = test_df2.progress_apply(get_annot, axis=1)\ntest_df2.to_csv('/kaggle/working/test.csv',index=False)\ndisplay(test_df2.head())","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:26:43.449074Z","iopub.execute_input":"2022-02-06T03:26:43.449382Z","iopub.status.idle":"2022-02-06T03:27:01.378436Z","shell.execute_reply.started":"2022-02-06T03:26:43.449349Z","shell.execute_reply":"2022-02-06T03:27:01.377721Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ✂️ Remove Files","metadata":{}},{"cell_type":"code","source":"!rm -r {IMAGE_DIR}\n!rm -r {LABEL_DIR}\n!rm -r /kaggle/working/output","metadata":{"execution":{"iopub.status.busy":"2022-02-06T03:14:36.630204Z","iopub.status.idle":"2022-02-06T03:14:36.630718Z","shell.execute_reply.started":"2022-02-06T03:14:36.630458Z","shell.execute_reply":"2022-02-06T03:14:36.630483Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 💡 Reference\n* [Humpback Whale Identification - Fluke Location](https://www.kaggle.com/martinpiotte/humpback-whale-identification-fluke-location)","metadata":{}},{"cell_type":"markdown","source":"<div align=\"center\"><img src=\"https://www.pngall.com/wp-content/uploads/2018/04/Under-Construction-PNG-File.png\" width=400>","metadata":{}}]}