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AIcrowd-AIBlitz7-Solution/challenge2/requirements.txt\n", "!pip install aicrowd-cli==0.1" ], "execution_count": 1, "outputs": [ { "output_type": "stream", "text": [ "Cloning into 'AIcrowd-AIBlitz7-Solution'...\n", "remote: warning: multi-pack bitmap is missing required reverse index\u001b[K\n", "remote: Enumerating objects: 305, done.\u001b[K\n", "remote: Counting objects: 100% (305/305), done.\u001b[K\n", "remote: Compressing objects: 100% (179/179), done.\u001b[K\n", "remote: Total 305 (delta 177), reused 219 (delta 121), pack-reused 0\u001b[K\n", "Receiving objects: 100% (305/305), 456.81 KiB | 24.04 MiB/s, done.\n", "Resolving deltas: 100% (177/177), done.\n", "Collecting pandas==1.0.5\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/af/f3/683bf2547a3eaeec15b39cef86f61e921b3b187f250fcd2b5c5fb4386369/pandas-1.0.5-cp37-cp37m-manylinux1_x86_64.whl (10.1MB)\n", "\u001b[K |████████████████████████████████| 10.1MB 18.8MB/s \n", "\u001b[?25hCollecting numpy==1.20.2\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/73/ef/8967d406f3f85018ceb5efab50431e901683188f1741ceb053efcab26c87/numpy-1.20.2-cp37-cp37m-manylinux2010_x86_64.whl (15.3MB)\n", "\u001b[K |████████████████████████████████| 15.3MB 207kB/s \n", "\u001b[?25hCollecting opencv-python==4.2.0.32\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/34/a3/403dbaef909fee9f9f6a8eaff51d44085a14e5bb1a1ff7257117d744986a/opencv_python-4.2.0.32-cp37-cp37m-manylinux1_x86_64.whl (28.2MB)\n", "\u001b[K |████████████████████████████████| 28.2MB 168kB/s \n", "\u001b[?25hCollecting tqdm==4.59.0\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/f8/3e/2730d0effc282960dbff3cf91599ad0d8f3faedc8e75720fdf224b31ab24/tqdm-4.59.0-py2.py3-none-any.whl (74kB)\n", "\u001b[K |████████████████████████████████| 81kB 10.1MB/s \n", "\u001b[?25hCollecting detecto==1.2.1\n", " Downloading 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(line 5)) (0.9.1+cu101)\n", "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from detecto==1.2.1->-r AIcrowd-AIBlitz7-Solution/challenge2/requirements.txt (line 5)) (1.8.1+cu101)\n", "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.0->-r AIcrowd-AIBlitz7-Solution/challenge2/requirements.txt (line 6)) (7.1.2)\n", "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.0->-r AIcrowd-AIBlitz7-Solution/challenge2/requirements.txt (line 6)) (2.4.7)\n", "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.0->-r AIcrowd-AIBlitz7-Solution/challenge2/requirements.txt (line 6)) (0.10.0)\n", "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.3.0->-r AIcrowd-AIBlitz7-Solution/challenge2/requirements.txt (line 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"Installing collected packages: numpy, pandas, opencv-python, tqdm, matplotlib, detecto\n", " Found existing installation: numpy 1.19.5\n", " Uninstalling numpy-1.19.5:\n", " Successfully uninstalled numpy-1.19.5\n", " Found existing installation: pandas 1.1.5\n", " Uninstalling pandas-1.1.5:\n", " Successfully uninstalled pandas-1.1.5\n", " Found existing installation: opencv-python 4.1.2.30\n", " Uninstalling opencv-python-4.1.2.30:\n", " Successfully uninstalled opencv-python-4.1.2.30\n", " Found existing installation: tqdm 4.41.1\n", " Uninstalling tqdm-4.41.1:\n", " Successfully uninstalled tqdm-4.41.1\n", " Found existing installation: matplotlib 3.2.2\n", " Uninstalling matplotlib-3.2.2:\n", " Successfully uninstalled matplotlib-3.2.2\n", "Successfully installed detecto-1.2.1 matplotlib-3.3.0 numpy-1.20.2 opencv-python-4.2.0.32 pandas-1.0.5 tqdm-4.59.0\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.colab-display-data+json": { "pip_warning": { "packages": [ "matplotlib", "mpl_toolkits", "numpy", "pandas" ] } } }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Collecting aicrowd-cli==0.1\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/84/45/232809ceb8b1462a2c7eec17335cef9e74bdd0fbde6d5d31a9fb570f1bcf/aicrowd-cli-0.1.0.tar.gz (40kB)\n", "\u001b[K |████████████████████████████████| 40kB 6.3MB/s \n", "\u001b[?25hRequirement already satisfied: click<8,>=7.1.2 in /usr/local/lib/python3.7/dist-packages (from aicrowd-cli==0.1) (7.1.2)\n", "Collecting gitpython\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/a6/99/98019716955ba243657daedd1de8f3a88ca1f5b75057c38e959db22fb87b/GitPython-3.1.14-py3-none-any.whl (159kB)\n", "\u001b[K |████████████████████████████████| 163kB 24.5MB/s \n", "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from aicrowd-cli==0.1) (2.23.0)\n", "Collecting requests-toolbelt\n", "\u001b[?25l Downloading 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pygments<3.0.0,>=2.6.0 in /usr/local/lib/python3.7/dist-packages (from rich->aicrowd-cli==0.1) (2.6.1)\n", "Collecting commonmark<0.10.0,>=0.9.0\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/b1/92/dfd892312d822f36c55366118b95d914e5f16de11044a27cf10a7d71bbbf/commonmark-0.9.1-py2.py3-none-any.whl (51kB)\n", "\u001b[K |████████████████████████████████| 51kB 9.0MB/s \n", "\u001b[?25hCollecting smmap<5,>=3.0.1\n", " Downloading https://files.pythonhosted.org/packages/68/ee/d540eb5e5996eb81c26ceffac6ee49041d473bc5125f2aa995cf51ec1cf1/smmap-4.0.0-py2.py3-none-any.whl\n", "Building wheels for collected packages: aicrowd-cli\n", " Building wheel for aicrowd-cli (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for aicrowd-cli: filename=aicrowd_cli-0.1.0-cp37-none-any.whl size=34929 sha256=745f5ecd9fadfc7b07c82093b1fd5c23da3d1359a34a2c14d5ce6035f31faf03\n", " Stored in directory: /root/.cache/pip/wheels/7e/ec/cc/a38391f23ad9bd044c91cd5e6e0a30f2a4265a1eb5adedf9f5\n", "Successfully built aicrowd-cli\n", "Installing collected packages: smmap, gitdb, gitpython, requests-toolbelt, colorama, commonmark, rich, aicrowd-cli\n", "Successfully installed aicrowd-cli-0.1.0 colorama-0.4.4 commonmark-0.9.1 gitdb-4.0.7 gitpython-3.1.14 requests-toolbelt-0.9.1 rich-10.1.0 smmap-4.0.0\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "Z_9aIANwPsDc" }, "source": [ "###RESTART RUNTIME TO USE NEW PACKAGES" ] }, { "cell_type": "code", "metadata": { "id": "0-hLNX9hN9Tc", "outputId": "dab48b98-d461-4382-b22a-2f98a216ff6f", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "API_KEY = \"\" # Please enter your API Key from [https://www.aicrowd.com/participants/me]\n", "!aicrowd login --api-key $API_KEY" ], "execution_count": 1, "outputs": [ { "output_type": "stream", "text": [ "\u001b[32mAPI Key valid\u001b[0m\n", "\u001b[32mSaved API Key successfully!\u001b[0m\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "l0DgLU2fN9Ri", "outputId": "2fb58b61-6be6-49e5-c5bc-6ffe26feb086", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "!aicrowd dataset download --challenge debris-detection\n", "\n", "!rm -rf data\n", "!mkdir data\n", "\n", "!unzip -q train.zip -d data/train\n", "!unzip -q val.zip -d data/val\n", "!unzip -q test.zip -d data/test\n", "\n", "!mv train.csv data/train.csv\n", "!mv val.csv data/val.csv\n", "!mv sample_submission.csv data/sample_submission.csv" ], "execution_count": 2, "outputs": [ { "output_type": "stream", "text": [ "sample_submission.csv: 100% 1.98M/1.98M [00:00<00:00, 4.18MB/s]\n", "test.zip: 100% 259M/259M [00:33<00:00, 7.71MB/s]\n", "train.csv: 100% 1.78M/1.78M [00:00<00:00, 3.67MB/s]\n", "train.zip: 100% 1.04G/1.04G [02:11<00:00, 7.88MB/s]\n", "val.csv: 100% 176k/176k [00:00<00:00, 764kB/s]\n", "val.zip: 100% 104M/104M [00:18<00:00, 5.70MB/s]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "0qaJfYWfNy3c" }, "source": [ "import pandas as pd\n", "import ast\n", "import numpy as np\n", "from detecto import core, utils, visualize\n", "from matplotlib import pyplot as plt\n", "from tqdm import tqdm\n", "import cv2" ], "execution_count": 3, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Kk5B8D8CNy3e" }, "source": [ "data = pd.read_csv('data/train.csv')\n", "fn, w, h, cl, boxes, imgid = [], [], [], [], [], []\n", "counter = 0\n", "for i in range(300):\n", " idx = int(data['ImageID'].iloc[i])\n", " bbox = ast.literal_eval(data['bboxes'].iloc[i])\n", " for box in bbox:\n", " fn.append(f'{idx}.jpg')\n", " w.append(512)\n", " h.append(512)\n", " cl.append('debris')\n", " boxes.append(box)\n", " imgid.append(counter)\n", " counter += 1\n", "boxes = np.array(boxes).T\n", "pd.DataFrame(\n", " {'filename':fn, \n", " 'width' : w, \n", " 'height' : h, \n", " 'class' : cl,\n", " 'xmin' : boxes[0],\n", " 'ymin' : boxes[2],\n", " 'xmax' : boxes[1],\n", " 'ymax':boxes[3],\n", " 'image_id':imgid\n", " }\n", ").to_csv('data/labels.csv',index = False)" ], "execution_count": 4, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "_yVWlcynNy3f", "outputId": "f2a57792-da0a-42b8-dd4d-242207da44b9", "colab": { "base_uri": "https://localhost:8080/", "height": 122, "referenced_widgets": [ "b57e74cbbcaf4c468df42482c7245c51", "52074349172f48f9b3d822c1a698d239", "696e1751d8714a69afe570eaa26ad4de", "6e717e0091784a6fbe51a78920fe554a", "da12324d404642c7b12c3a15b9709a60", "88a7a3d9b61f40be9587974f05c9663d", "531c86bbfc164d71b2793aa16e93b528", "d3f4948357524f3a89daf34484e2df9e", "c5ea11e7b07242719ff248ec75d3f1c5", "cb062489075b4fc6a49fb3cd6133d655", "5cad173f945e4f76b71fff448f1cbfc9" ] } }, "source": [ "dataset = core.Dataset('data/labels.csv','data/train')\n", "model = core.Model(['debris'],pretrained = False)\n", "m = model.fit(dataset,epochs=10, learning_rate=0.001, lr_step_size=5,verbose = True)" ], "execution_count": 6, "outputs": [ { "output_type": "stream", "text": [ "Downloading: \"https://download.pytorch.org/models/resnet50-19c8e357.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-19c8e357.pth\n" ], "name": "stderr" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b57e74cbbcaf4c468df42482c7245c51", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0.00/97.8M [00:00