{"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":"# [Tensorflow - Help Protect the Great Barrier Reef](https://www.kaggle.com/c/tensorflow-great-barrier-reef)\n> Detect crown-of-thorns starfish in underwater image data\n\n![](\"https://storage.googleapis.com/kaggle-competitions/kaggle/31703/logos/header.png?t=2021-10-29-00-30-04\")
","metadata":{}},{"cell_type":"markdown","source":"## ๐ Notebooks:\n* Train: [Great-Barrier-Reef: YOLOv5 [train] ๐](https://www.kaggle.com/awsaf49/great-barrier-reef-yolov5-train)\n* Infer: [Great-Barrier-Reef: YOLOv5 [infer] ๐](https://www.kaggle.com/awsaf49/great-barrier-reef-yolov5-infer)","metadata":{}},{"cell_type":"markdown","source":"# ๐ Install Libraries","metadata":{}},{"cell_type":"code","source":"# !pip install -qU wandb\n# !add-apt-repository ppa:ubuntu-toolchain-r/test -y\n# !apt-get update\n# !apt-get upgrade libstdc++6 -y","metadata":{"_kg_hide-output":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-12-21T20:21:11.949271Z","iopub.execute_input":"2021-12-21T20:21:11.949621Z","iopub.status.idle":"2021-12-21T20:21:11.967998Z","shell.execute_reply.started":"2021-12-21T20:21:11.949519Z","shell.execute_reply":"2021-12-21T20:21:11.967343Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ๐ Import Libraries","metadata":{}},{"cell_type":"code","source":"from itertools import groupby\nimport numpy as np\nfrom tqdm.notebook import tqdm\ntqdm.pandas()\nimport pandas as pd\nimport os\nimport pickle\nimport cv2\nfrom multiprocessing import Pool\nimport matplotlib.pyplot as plt\n# import cupy as cp\nimport ast\nimport glob\n\nimport shutil\nimport sys\nsys.path.append('../input/tensorflow-great-barrier-reef')\n\nfrom joblib import Parallel, delayed\n\nfrom IPython.display import display, HTML\n\nfrom matplotlib import animation, rc\nrc('animation', html='jshtml')","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-12-21T20:21:11.969237Z","iopub.execute_input":"2021-12-21T20:21:11.969601Z","iopub.status.idle":"2021-12-21T20:21:12.333589Z","shell.execute_reply.started":"2021-12-21T20:21:11.969567Z","shell.execute_reply":"2021-12-21T20:21:12.332876Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ๐ Key-Points\n* One have to submit prediction using the provided **python time-series API**, which makes this competition different from previous Object Detection Competitions.\n* Each prediction row needs to include all bounding boxes for the image. Submission is format seems also **COCO** which means `[x_min, y_min, width, height]`\n* Copmetition metric `F2` tolerates some false positives(FP) in order to ensure very few starfish are missed. Which means tackling **false negatives(FN)** is more important than false positives(FP). \n$$F2 = 5 \\cdot \\frac{precision \\cdot recall}{4\\cdot precision + recall}$$","metadata":{}},{"cell_type":"markdown","source":"## Please Upvote if you find this Helpful","metadata":{}},{"cell_type":"markdown","source":"# โญ WandB\n![](\"https://camo.githubusercontent.com/dd842f7b0be57140e68b2ab9cb007992acd131c48284eaf6b1aca758bfea358b/68747470733a2f2f692e696d6775722e636f6d2f52557469567a482e706e67\")
\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":"markdown","source":"# ๐ Meta Data\n* `train_images/` - Folder containing training set photos of the form `video_{video_id}/{video_frame}.jpg`.\n\n* `[train/test].csv` - Metadata for the images. As with other test files, most of the test metadata data is only available to your notebook upon submission. Just the first few rows available for download.\n\n* `video_id` - ID number of the video the image was part of. The video ids are not meaningfully ordered.\n* `video_frame` - The frame number of the image within the video. Expect to see occasional gaps in the frame number from when the diver surfaced.\n* `sequence` - ID of a gap-free subset of a given video. The sequence ids are not meaningfully ordered.\n* `sequence_frame` - The frame number within a given sequence.\n* `image_id` - ID code for the image, in the format `{video_id}-{video_frame}`\n* `annotations` - The bounding boxes of any starfish detections in a string format that can be evaluated directly with Python. Does not use the same format as the predictions you will submit. Not available in test.csv. A bounding box is described by the pixel coordinate `(x_min, y_min)` of its lower left corner within the image together with its `width` and `height` in pixels --> (COCO format).","metadata":{}},{"cell_type":"code","source":"FOLD = 4 # which fold to train\nREMOVE_NOBBOX = True # remove images with no bbox\nROOT_DIR = '/kaggle/input/tensorflow-great-barrier-reef/'\nIMAGE_DIR = '/kaggle/images' # directory to save images\nLABEL_DIR = '/kaggle/labels' # directory to save labels","metadata":{"execution":{"iopub.status.busy":"2021-12-21T20:21:12.335372Z","iopub.execute_input":"2021-12-21T20:21:12.335623Z","iopub.status.idle":"2021-12-21T20:21:12.341563Z","shell.execute_reply.started":"2021-12-21T20:21:12.335589Z","shell.execute_reply":"2021-12-21T20:21:12.340798Z"},"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":"2021-12-21T20:21:12.34272Z","iopub.execute_input":"2021-12-21T20:21:12.343048Z","iopub.status.idle":"2021-12-21T20:21:13.655632Z","shell.execute_reply.started":"2021-12-21T20:21:12.343013Z","shell.execute_reply":"2021-12-21T20:21:13.654695Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Get Paths","metadata":{}},{"cell_type":"code","source":"def get_path(row):\n row['old_image_path'] = f'{ROOT_DIR}/train_images/video_{row.video_id}/{row.video_frame}.jpg'\n row['image_path'] = f'{IMAGE_DIR}/video_{row.video_id}_{row.video_frame}.jpg'\n row['label_path'] = f'{LABEL_DIR}/video_{row.video_id}_{row.video_frame}.txt'\n return row","metadata":{"execution":{"iopub.status.busy":"2021-12-21T20:21:13.65786Z","iopub.execute_input":"2021-12-21T20:21:13.658077Z","iopub.status.idle":"2021-12-21T20:21:13.664776Z","shell.execute_reply.started":"2021-12-21T20:21:13.658049Z","shell.execute_reply":"2021-12-21T20:21:13.663494Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Train Data\ndf = pd.read_csv(f'{ROOT_DIR}/train.csv')\ndf = df.progress_apply(get_path, axis=1)\ndf['annotations'] = df['annotations'].progress_apply(lambda x: ast.literal_eval(x))\ndisplay(df.head(2))","metadata":{"execution":{"iopub.status.busy":"2021-12-21T20:21:13.666019Z","iopub.execute_input":"2021-12-21T20:21:13.666664Z","iopub.status.idle":"2021-12-21T20:21:52.937607Z","shell.execute_reply.started":"2021-12-21T20:21:13.666623Z","shell.execute_reply":"2021-12-21T20:21:52.936827Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Number of BBoxes\n> Nearly 80% images are without any bbox.","metadata":{}},{"cell_type":"code","source":"df['num_bbox'] = df['annotations'].progress_apply(lambda x: len(x))\ndata = (df.num_bbox>0).value_counts(normalize=True)*100\nprint(f\"No BBox: {data[0]:0.2f}% | With BBox: {data[1]:0.2f}%\")","metadata":{"execution":{"iopub.status.busy":"2021-12-21T20:21:52.938818Z","iopub.execute_input":"2021-12-21T20:21:52.939141Z","iopub.status.idle":"2021-12-21T20:21:53.03345Z","shell.execute_reply.started":"2021-12-21T20:21:52.939103Z","shell.execute_reply":"2021-12-21T20:21:53.03265Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ๐งน Clean Data\n* In this notebook, we use only **bboxed-images** (`~5k`). We can use all `~23K` images for train but most of them don't have any labels. So it would be easier to carry out experiments using only **bboxed images**.","metadata":{}},{"cell_type":"code","source":"if REMOVE_NOBBOX:\n df = df.query(\"num_bbox>0\")","metadata":{"execution":{"iopub.status.busy":"2021-12-21T20:21:53.034825Z","iopub.execute_input":"2021-12-21T20:21:53.035075Z","iopub.status.idle":"2021-12-21T20:21:53.059461Z","shell.execute_reply.started":"2021-12-21T20:21:53.03504Z","shell.execute_reply":"2021-12-21T20:21:53.058822Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# โ๏ธ 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(path):\n data = path.split('/')\n filename = data[-1]\n video_id = data[-2]\n new_path = os.path.join(IMAGE_DIR,f'{video_id}_{filename}')\n shutil.copy(path, new_path)\n return","metadata":{"execution":{"iopub.status.busy":"2021-12-21T20:21:53.060488Z","iopub.execute_input":"2021-12-21T20:21:53.060787Z","iopub.status.idle":"2021-12-21T20:21:53.065735Z","shell.execute_reply.started":"2021-12-21T20:21:53.060751Z","shell.execute_reply":"2021-12-21T20:21:53.064922Z"},"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)(path) for path in tqdm(image_paths))","metadata":{"execution":{"iopub.status.busy":"2021-12-21T20:21:53.067197Z","iopub.execute_input":"2021-12-21T20:21:53.06746Z","iopub.status.idle":"2021-12-21T20:22:25.588683Z","shell.execute_reply.started":"2021-12-21T20:21:53.067425Z","shell.execute_reply":"2021-12-21T20:22:25.588018Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ๐จ Helper","metadata":{}},{"cell_type":"code","source":"def voc2yolo(image_height, image_width, bboxes):\n \"\"\"\n voc => [x1, y1, x2, y1]\n yolo => [xmid, ymid, w, h] (normalized)\n \"\"\"\n \n bboxes = bboxes.copy().astype(float) # otherwise all value will be 0 as voc_pascal dtype is np.int\n \n bboxes[..., [0, 2]] = bboxes[..., [0, 2]]/ image_width\n bboxes[..., [1, 3]] = bboxes[..., [1, 3]]/ image_height\n \n w = bboxes[..., 2] - bboxes[..., 0]\n h = bboxes[..., 3] - bboxes[..., 1]\n \n bboxes[..., 0] = bboxes[..., 0] + w/2\n bboxes[..., 1] = bboxes[..., 1] + h/2\n bboxes[..., 2] = w\n bboxes[..., 3] = h\n \n return bboxes\n\ndef yolo2voc(image_height, image_width, bboxes):\n \"\"\"\n yolo => [xmid, ymid, w, h] (normalized)\n voc => [x1, y1, x2, y1]\n \n \"\"\" \n bboxes = bboxes.copy().astype(float) # otherwise all value will be 0 as voc_pascal dtype is np.int\n \n bboxes[..., [0, 2]] = bboxes[..., [0, 2]]* image_width\n bboxes[..., [1, 3]] = bboxes[..., [1, 3]]* image_height\n \n bboxes[..., [0, 1]] = bboxes[..., [0, 1]] - bboxes[..., [2, 3]]/2\n bboxes[..., [2, 3]] = bboxes[..., [0, 1]] + bboxes[..., [2, 3]]\n \n return bboxes\n\ndef coco2yolo(image_height, image_width, bboxes):\n \"\"\"\n coco => [xmin, ymin, w, h]\n yolo => [xmid, ymid, w, h] (normalized)\n \"\"\"\n \n bboxes = bboxes.copy().astype(float) # otherwise all value will be 0 as voc_pascal dtype is np.int\n \n # normolizinig\n bboxes[..., [0, 2]]= bboxes[..., [0, 2]]/ image_width\n bboxes[..., [1, 3]]= bboxes[..., [1, 3]]/ image_height\n \n # converstion (xmin, ymin) => (xmid, ymid)\n bboxes[..., [0, 1]] = bboxes[..., [0, 1]] + bboxes[..., [2, 3]]/2\n \n return bboxes\n\ndef yolo2coco(image_height, image_width, bboxes):\n \"\"\"\n yolo => [xmid, ymid, w, h] (normalized)\n coco => [xmin, ymin, w, h]\n \n \"\"\" \n bboxes = bboxes.copy().astype(float) # otherwise all value will be 0 as voc_pascal dtype is np.int\n \n # denormalizing\n bboxes[..., [0, 2]]= bboxes[..., [0, 2]]* image_width\n bboxes[..., [1, 3]]= bboxes[..., [1, 3]]* image_height\n \n # converstion (xmid, ymid) => (xmin, ymin) \n bboxes[..., [0, 1]] = bboxes[..., [0, 1]] - bboxes[..., [2, 3]]/2\n \n return bboxes\n\n\ndef load_image(image_path):\n return cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB)\n\n\ndef plot_one_box(x, img, color=None, label=None, line_thickness=None):\n # Plots one bounding box on image img\n tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness\n color = color or [random.randint(0, 255) for _ in range(3)]\n c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))\n cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)\n if label:\n tf = max(tl - 1, 1) # font thickness\n t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]\n c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3\n cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled\n cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)\n\ndef draw_bboxes(img, bboxes, classes, class_ids, colors = None, show_classes = None, bbox_format = 'yolo', class_name = False, line_thickness = 2): \n \n image = img.copy()\n show_classes = classes if show_classes is None else show_classes\n colors = (0, 255 ,0) if colors is None else colors\n \n if bbox_format == 'yolo':\n \n for idx in range(len(bboxes)): \n \n bbox = bboxes[idx]\n cls = classes[idx]\n cls_id = class_ids[idx]\n color = colors[cls_id] if type(colors) is list else colors\n \n if cls in show_classes:\n \n x1 = round(float(bbox[0])*image.shape[1])\n y1 = round(float(bbox[1])*image.shape[0])\n w = round(float(bbox[2])*image.shape[1]/2) #w/2 \n h = round(float(bbox[3])*image.shape[0]/2)\n\n voc_bbox = (x1-w, y1-h, x1+w, y1+h)\n plot_one_box(voc_bbox, \n image,\n color = color,\n label = cls if class_name else str(get_label(cls)),\n line_thickness = line_thickness)\n \n elif bbox_format == 'coco':\n \n for idx in range(len(bboxes)): \n \n bbox = bboxes[idx]\n cls = classes[idx]\n cls_id = class_ids[idx]\n color = colors[cls_id] if type(colors) is list else colors\n \n if cls in show_classes: \n x1 = int(round(bbox[0]))\n y1 = int(round(bbox[1]))\n w = int(round(bbox[2]))\n h = int(round(bbox[3]))\n\n voc_bbox = (x1, y1, x1+w, y1+h)\n plot_one_box(voc_bbox, \n image,\n color = color,\n label = cls if class_name else str(cls_id),\n line_thickness = line_thickness)\n\n elif bbox_format == 'voc_pascal':\n \n for idx in range(len(bboxes)): \n \n bbox = bboxes[idx]\n cls = classes[idx]\n cls_id = class_ids[idx]\n color = colors[cls_id] if type(colors) is list else colors\n \n if cls in show_classes: \n x1 = int(round(bbox[0]))\n y1 = int(round(bbox[1]))\n x2 = int(round(bbox[2]))\n y2 = int(round(bbox[3]))\n voc_bbox = (x1, y1, x2, y2)\n plot_one_box(voc_bbox, \n image,\n color = color,\n label = cls if class_name else str(cls_id),\n line_thickness = line_thickness)\n else:\n raise ValueError('wrong bbox format')\n\n return image\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\n\n# https://www.kaggle.com/diegoalejogm/great-barrier-reefs-eda-with-animations\ndef create_animation(ims):\n fig = plt.figure(figsize=(16, 12))\n plt.axis('off')\n im = plt.imshow(ims[0])\n\n def animate_func(i):\n im.set_array(ims[i])\n return [im]\n\n return animation.FuncAnimation(fig, animate_func, frames = len(ims), interval = 1000//12)\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":true,"execution":{"iopub.status.busy":"2021-12-21T20:22:25.591991Z","iopub.execute_input":"2021-12-21T20:22:25.592472Z","iopub.status.idle":"2021-12-21T20:22:25.630233Z","shell.execute_reply.started":"2021-12-21T20:22:25.59244Z","shell.execute_reply":"2021-12-21T20:22:25.629502Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Create BBox","metadata":{}},{"cell_type":"code","source":"df['bboxes'] = df.annotations.progress_apply(get_bbox)\ndf.head(2)","metadata":{"execution":{"iopub.status.busy":"2021-12-21T20:22:25.632802Z","iopub.execute_input":"2021-12-21T20:22:25.632988Z","iopub.status.idle":"2021-12-21T20:22:25.711493Z","shell.execute_reply.started":"2021-12-21T20:22:25.632963Z","shell.execute_reply":"2021-12-21T20:22:25.710854Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Get Image-Size\n> All Images have same dimension, [Width, Height] = `[1280, 720]`","metadata":{}},{"cell_type":"code","source":"df['width'] = 1280\ndf['height'] = 720\ndisplay(df.head(2))","metadata":{"execution":{"iopub.status.busy":"2021-12-21T20:22:25.712667Z","iopub.execute_input":"2021-12-21T20:22:25.713416Z","iopub.status.idle":"2021-12-21T20:22:25.729938Z","shell.execute_reply.started":"2021-12-21T20:22:25.71338Z","shell.execute_reply":"2021-12-21T20:22:25.729161Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ๐ท๏ธ 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> Competition bbox format is **COCO** hence `[x_min, y_min, width, height]`. So, we need to convert form **COCO** to **YOLO** format.\n","metadata":{}},{"cell_type":"code","source":"cnt = 0\nall_bboxes = []\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_coco = np.array(row.bboxes).astype(np.float32).copy()\n num_bbox = len(bboxes_coco)\n names = ['cots']*num_bbox\n labels = [0]*num_bbox\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_yolo = coco2yolo(image_height, image_width, bboxes_coco)\n bboxes_yolo = np.clip(bboxes_yolo, 0, 1)\n all_bboxes.extend(bboxes_yolo)\n for bbox_idx in range(len(bboxes_yolo)):\n annot = [str(labels[bbox_idx])]+ list(bboxes_yolo[bbox_idx].astype(str))+(['\\n'] if num_bbox!=(bbox_idx+1) else [''])\n annot = ' '.join(annot)\n annot = annot.strip(' ')\n f.write(annot)\nprint('Missing:',cnt)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-12-21T20:22:25.731174Z","iopub.execute_input":"2021-12-21T20:22:25.73193Z","iopub.status.idle":"2021-12-21T20:22:28.245972Z","shell.execute_reply.started":"2021-12-21T20:22:25.731892Z","shell.execute_reply":"2021-12-21T20:22:28.245251Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ๐ 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 GroupKFold\nkf = GroupKFold(n_splits = 5)\ndf = df.reset_index(drop=True)\ndf['fold'] = -1\nfor fold, (train_idx, val_idx) in enumerate(kf.split(df, y = df.video_id.tolist(), groups=df.sequence)):\n df.loc[val_idx, 'fold'] = fold\ndisplay(df.fold.value_counts())","metadata":{"execution":{"iopub.status.busy":"2021-12-21T20:22:28.247212Z","iopub.execute_input":"2021-12-21T20:22:28.247973Z","iopub.status.idle":"2021-12-21T20:22:29.164085Z","shell.execute_reply.started":"2021-12-21T20:22:28.247933Z","shell.execute_reply":"2021-12-21T20:22:29.163375Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ๐ 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":"2021-12-21T20:22:29.165409Z","iopub.execute_input":"2021-12-21T20:22:29.16584Z","iopub.status.idle":"2021-12-21T20:22:29.552505Z","shell.execute_reply.started":"2021-12-21T20:22:29.165804Z","shell.execute_reply":"2021-12-21T20:22:29.551763Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# โ๏ธ 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 = '/kaggle/working',\n train = os.path.join( cwd , 'train.txt') ,\n val = os.path.join( cwd , 'val.txt' ),\n nc = 1,\n names = ['cots'],\n )\n\nwith open(os.path.join( cwd , 'bgr.yaml'), 'w') as outfile:\n yaml.dump(data, outfile, default_flow_style=False)\n\nf = open(os.path.join( cwd , 'bgr.yaml'), 'r')\nprint('\\nyaml:')\nprint(f.read())","metadata":{"execution":{"iopub.status.busy":"2021-12-21T20:22:29.553866Z","iopub.execute_input":"2021-12-21T20:22:29.554719Z","iopub.status.idle":"2021-12-21T20:22:29.585636Z","shell.execute_reply.started":"2021-12-21T20:22:29.554677Z","shell.execute_reply":"2021-12-21T20:22:29.584975Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ๐ฆ [YOLOv5](https://github.com/ultralytics/yolov5/)\n![](\"https://github.com/ultralytics/yolov5/releases/download/v1.0/splash.jpg\")
","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\ndisplay = utils.notebook_init() # check","metadata":{"execution":{"iopub.status.busy":"2021-12-21T20:22:29.588288Z","iopub.execute_input":"2021-12-21T20:22:29.588503Z","iopub.status.idle":"2021-12-21T20:22:45.15801Z","shell.execute_reply.started":"2021-12-21T20:22:29.588478Z","shell.execute_reply":"2021-12-21T20:22:45.157274Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Custom Hyperparameters (Fine-Tuning)\n\nimport os\nimport yaml\n\ncwd = '/kaggle/working/custom.yaml'\n\ndata = hyperparams = {'lr0': 0.01,\n 'lrf': 0.1, \n 'momentum': 0.937, \n 'weight_decay': 0.0005,\n 'warmup_epochs': 5.0, #3.0\n 'warmup_momentum': 0.8, #0.8\n 'warmup_bias_lr': 0.1, #0.1\n 'box': 0.05,\n 'cls': 0.5, #0.5\n 'cls_pw': 1.0,\n 'obj': 1.0,\n 'obj_pw': 1.0,\n 'iou_t': 0.2,\n 'anchor_t': 4.0,\n 'fl_gamma': 0.0,\n 'hsv_h': 0.015,\n 'hsv_s': 0.7,#0.7\n 'hsv_v': 0.3,#0.3\n 'degrees': 0.0,\n 'translate': 0.1,\n 'scale': 0.7, #0.5\n 'shear': 0.0,\n 'perspective': 0.0,\n 'flipud': 0.0,\n 'fliplr': 0.5,\n 'mosaic': 0.5, #1.0 # 0.0 was better\n 'mixup': 0.5, #0.0\n 'copy_paste': 0.0}\n\nwith open(os.path.join( cwd ), 'w') as outfile:\n yaml.dump(data, outfile, default_flow_style=False)\n\nf = open(os.path.join( cwd ), 'r')\nprint('\\nyaml:')\nprint(f.read())\n\n","metadata":{"execution":{"iopub.status.busy":"2021-12-21T20:22:45.159886Z","iopub.execute_input":"2021-12-21T20:22:45.160416Z","iopub.status.idle":"2021-12-21T20:22:45.174838Z","shell.execute_reply.started":"2021-12-21T20:22:45.160372Z","shell.execute_reply":"2021-12-21T20:22:45.174064Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Weights & Biases (optional)\nimport wandb\nwandb.login(anonymous='must')","metadata":{"execution":{"iopub.status.busy":"2021-12-21T20:22:45.176099Z","iopub.execute_input":"2021-12-21T20:22:45.176554Z","iopub.status.idle":"2021-12-21T20:22:46.62836Z","shell.execute_reply.started":"2021-12-21T20:22:45.176498Z","shell.execute_reply":"2021-12-21T20:22:46.627643Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ๐
Training","metadata":{}},{"cell_type":"code","source":"# Train YOLOv5s on COCO128 for 3 epochs\n!python train.py --img 1280\\\n--hyp /kaggle/working/custom.yaml\\\n--batch 12\\\n--epochs 18\\\n--data /kaggle/working/bgr.yaml\\\n--weights yolov5m.pt --workers 0","metadata":{"execution":{"iopub.status.busy":"2021-12-21T20:22:46.629926Z","iopub.execute_input":"2021-12-21T20:22:46.630179Z","iopub.status.idle":"2021-12-21T20:41:02.684712Z","shell.execute_reply.started":"2021-12-21T20:22:46.630142Z","shell.execute_reply":"2021-12-21T20:41:02.683841Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# โจ Overview\n","metadata":{},"attachments":{"14c7fea9-9a96-45de-a620-675270d74c8d.png":{"image/png":"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Output Files","metadata":{}},{"cell_type":"code","source":"!ls runs/train/exp","metadata":{"execution":{"iopub.status.busy":"2021-12-21T20:51:37.839895Z","iopub.execute_input":"2021-12-21T20:51:37.840201Z","iopub.status.idle":"2021-12-21T20:51:38.509778Z","shell.execute_reply.started":"2021-12-21T20:51:37.840167Z","shell.execute_reply":"2021-12-21T20:51:38.508964Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ๐ Class Distribution","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize = (10,10))\nplt.axis('off')\nplt.imshow(plt.imread('runs/train/exp/labels_correlogram.jpg'));","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-12-21T20:51:39.798369Z","iopub.execute_input":"2021-12-21T20:51:39.799135Z","iopub.status.idle":"2021-12-21T20:51:40.50081Z","shell.execute_reply.started":"2021-12-21T20:51:39.799096Z","shell.execute_reply":"2021-12-21T20:51:40.500148Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize = (10,10))\nplt.axis('off')\nplt.imshow(plt.imread('runs/train/exp/labels.jpg'));","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-12-21T20:51:42.767021Z","iopub.execute_input":"2021-12-21T20:51:42.767419Z","iopub.status.idle":"2021-12-21T20:51:43.287013Z","shell.execute_reply.started":"2021-12-21T20:51:42.767387Z","shell.execute_reply":"2021-12-21T20:51:43.286346Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ๐ญ Batch Image","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nplt.figure(figsize = (10, 10))\nplt.imshow(plt.imread('runs/train/exp/train_batch0.jpg'))\n\nplt.figure(figsize = (10, 10))\nplt.imshow(plt.imread('runs/train/exp/train_batch1.jpg'))\n\nplt.figure(figsize = (10, 10))\nplt.imshow(plt.imread('runs/train/exp/train_batch2.jpg'))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-12-21T20:51:45.742964Z","iopub.execute_input":"2021-12-21T20:51:45.74322Z","iopub.status.idle":"2021-12-21T20:51:48.05014Z","shell.execute_reply.started":"2021-12-21T20:51:45.743191Z","shell.execute_reply":"2021-12-21T20:51:48.049319Z"},"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'runs/train/exp/val_batch{row}_labels.jpg'))\n ax[row][0].set_xticks([])\n ax[row][0].set_yticks([])\n ax[row][0].set_title(f'runs/train/exp/val_batch{row}_labels.jpg', fontsize = 12)\n \n ax[row][1].imshow(plt.imread(f'runs/train/exp/val_batch{row}_pred.jpg'))\n ax[row][1].set_xticks([])\n ax[row][1].set_yticks([])\n ax[row][1].set_title(f'runs/train/exp/val_batch{row}_pred.jpg', fontsize = 12)\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-12-21T20:51:53.200655Z","iopub.execute_input":"2021-12-21T20:51:53.200912Z","iopub.status.idle":"2021-12-21T20:51:55.615932Z","shell.execute_reply.started":"2021-12-21T20:51:53.200882Z","shell.execute_reply":"2021-12-21T20:51:55.615328Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ๐ 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('runs/train/exp/results.png'));","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-12-21T20:52:09.441774Z","iopub.execute_input":"2021-12-21T20:52:09.442551Z","iopub.status.idle":"2021-12-21T20:52:10.485827Z","shell.execute_reply.started":"2021-12-21T20:52:09.442515Z","shell.execute_reply":"2021-12-21T20:52:10.485212Z"},"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('runs/train/exp/confusion_matrix.png'));","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-12-21T20:52:13.055949Z","iopub.execute_input":"2021-12-21T20:52:13.056506Z","iopub.status.idle":"2021-12-21T20:52:14.715849Z","shell.execute_reply.started":"2021-12-21T20:52:13.056469Z","shell.execute_reply":"2021-12-21T20:52:14.715132Z"},"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'runs/train/exp/{metric}_curve.png'));\n plt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2021-12-21T20:52:16.709112Z","iopub.execute_input":"2021-12-21T20:52:16.709384Z","iopub.status.idle":"2021-12-21T20:52:19.451888Z","shell.execute_reply.started":"2021-12-21T20:52:16.709353Z","shell.execute_reply":"2021-12-21T20:52:19.45117Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Please Upvote if you find this Helpful","metadata":{}},{"cell_type":"markdown","source":"# โ๏ธ Remove Files","metadata":{}},{"cell_type":"code","source":"!rm -r {IMAGE_DIR}\n!rm -r {LABEL_DIR}","metadata":{"execution":{"iopub.status.busy":"2021-12-21T20:41:14.692664Z","iopub.execute_input":"2021-12-21T20:41:14.693447Z","iopub.status.idle":"2021-12-21T20:41:16.598837Z","shell.execute_reply.started":"2021-12-21T20:41:14.693405Z","shell.execute_reply":"2021-12-21T20:41:16.592395Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":" Download File ","metadata":{}},{"cell_type":"markdown","source":"![](\"https://www.pngall.com/wp-content/uploads/2018/04/Under-Construction-PNG-File.png\")
","metadata":{}}]}