{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using a custom made dataset to detect bolts using Mediapipe" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "BLzZ7EADLpfN", "outputId": "0dd7ec3d-e831-46f6-f7c5-58264da552f3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mounted at /content/drive\n" ] } ], "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-94RhG5x-Bxz", "outputId": "523d9e26-936e-4b6a-d43c-aa32975544e7" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: pip in /usr/local/lib/python3.10/dist-packages (23.1.2)\n", "Collecting pip\n", " Downloading pip-24.0-py3-none-any.whl (2.1 MB)\n", "\u001b[2K 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/root/.cache/pip/wheels/1a/67/4a/ad4082dd7dfc30f2abfe4d80a2ed5926a506eb8a972b4767fa\n", "Successfully built seqeval\n", "Installing collected packages: typeguard, tensorflow-model-optimization, portalocker, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, immutabledict, colorama, tensorflow-addons, sounddevice, sacrebleu, nvidia-cusparse-cu12, nvidia-cudnn-cu12, seqeval, nvidia-cusolver-cu12, mediapipe, tensorflow-text, tf-models-official, mediapipe-model-maker\n", "Successfully installed colorama-0.4.6 immutabledict-4.2.0 mediapipe-0.10.11 mediapipe-model-maker-0.2.1.3 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 portalocker-2.8.2 sacrebleu-2.4.2 seqeval-1.2.2 sounddevice-0.4.6 tensorflow-addons-0.23.0 tensorflow-model-optimization-0.8.0 tensorflow-text-2.15.0 tf-models-official-2.15.0 typeguard-2.13.3\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", "\u001b[0m" ] } ], "source": [ "!pip install --upgrade pip\n", "!pip install 'keras<3.0.0' mediapipe-model-maker" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ulNlmQVX-wTX", "outputId": "c1822f5a-9b45-4347-984b-6cc460f1d980" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.10/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n", "\n", "TensorFlow Addons (TFA) has ended development and introduction of new features.\n", "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n", "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n", "\n", "For more information see: https://github.com/tensorflow/addons/issues/2807 \n", "\n", " warnings.warn(\n" ] } ], "source": [ "import os\n", "import tensorflow as tf\n", "assert tf.__version__.startswith('2')\n", "from google.colab import files\n", "\n", "from mediapipe_model_maker import object_detector" ] }, { "cell_type": "markdown", "metadata": { "id": "EddI6pFPJ0dm" }, "source": [ "## Creating the steps for training" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "oLmnLEhjJ8m5" }, "outputs": [], "source": [ "class TransferLearning():\n", " \"\"\" Transfer Learning class for object detection using MediaPipe Model Maker\n", " 2 datasets are required: train and validation\n", " \n", " Attributes:\n", " train_dataset_path: path to the train dataset\n", " validation_dataset_path: path to the validation dataset\n", " model: model to be trained\n", " train_data: train dataset\n", " validation_data: validation dataset\n", " \"\"\"\n", " def __init__(self):\n", " self.train_dataset_path = \"/content/drive/MyDrive/Detection-nuts-bolts.v1i.voc/train\"\n", " self.validation_dataset_path = \"/content/drive/MyDrive/Detection-nuts-bolts.v1i.voc/valid\"\n", " self.model = None\n", " self.train_data = None\n", " self.validation_data = None\n", " self.hparams = None\n", " self.options = None\n", " \n", " def load_data(self):\n", " \"\"\" Load train and validation datasets from the given paths \"\"\"\n", " self.train_data = object_detector.DataLoader.from_pascal_voc(self.train_dataset_path)\n", " self.validation_data = object_detector.DataLoader.from_pascal_voc(self.validation_dataset_path)\n", " \n", " def train_model(self,batch_size=8, learning_rate=0.3, epochs=100, export_dir='exported_model'):\n", " \"\"\" Train the model using the loaded datasets\n", " \n", " Args:\n", " batch_size: batch size for training\n", " learning_rate: learning rate for training\n", " epochs: number of epochs for training\n", " export_dir: directory to export the trained model\n", " \"\"\"\n", " self.hparams = object_detector.HParams(batch_size, learning_rate, epochs, export_dir)\n", " self.options = object_detector.ObjectDetectorOptions(\n", " supported_model=object_detector.SupportedModels.MOBILENET_V2,\n", " hparams=self.hparams\n", " )\n", " \n", " self.model = object_detector.ObjectDetector.create(\n", " train_data=self.train_data,\n", " validation_data=self.val_data,\n", " options=self.options)\n", "\n", " def evaluate_model(self,batch_size=8):\n", " \"\"\" Evaluate the trained model \n", " \n", " Args:\n", " batch_size: batch size for evaluation\n", " \"\"\"\n", " loss, metrics = self.model.evaluate(self.validation_data,batch_size)\n", " print(f\"Validation loss: {loss}\")\n", " print(f\"Validation coco metrics: {metrics}\")\n", " \n", " def export_model(self):\n", " \"\"\" Export the trained model \"\"\"\n", " self.model.export_model('bolt-detection.tflite')\n", " print(f\"Model exported\")\n", " \n", " \n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading the data, and training" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "U0zYXJNMRLja", "outputId": "244c2576-7f85-417c-b69d-dfbc3c73815f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading https://storage.googleapis.com/tf_model_garden/vision/qat/mobilenetv2_ssd_coco/mobilenetv2_ssd_i256_ckpt.tar.gz to /tmp/model_maker/object_detector/mobilenetv2_i256\n", "Model: \"retina_net_model\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " mobile_net_1 (MobileNet) {'2': (None, 64, 64, 24 2257984 \n", " ), \n", " '3': (None, 32, 32, 32 \n", " ), \n", " '4': (None, 16, 16, 96 \n", " ), \n", " '5': (None, 8, 8, 320) \n", " , '6': (None, 8, 8, 128 \n", " 0)} \n", " \n", " fpn_1 (FPN) {'5': (None, 8, 8, 128) 149056 \n", " , '4': (None, 16, 16, 1 \n", " 28), \n", " '3': (None, 32, 32, 12 \n", " 8), \n", " '6': (None, 4, 4, 128) \n", " , '7': (None, 2, 2, 128 \n", " )} \n", " \n", " multilevel_detection_gener multiple 0 (unused)\n", " ator (MultilevelDetectionG \n", " enerator) \n", " \n", " retina_net_head_1 (RetinaN ({'3': (None, 32, 32, 1 171062 \n", " etHead) 8), \n", " '4': (None, 16, 16, 18 \n", " ), \n", " '5': (None, 8, 8, 18), \n", " '6': (None, 4, 4, 18), \n", " '7': (None, 2, 2, 18)} \n", " , {'3': (None, 32, 32, \n", " 36), \n", " '4': (None, 16, 16, 36 \n", " ), \n", " '5': (None, 8, 8, 36), \n", " '6': (None, 4, 4, 36), \n", " '7': (None, 2, 2, 36)} \n", " , {}) \n", " \n", "=================================================================\n", "Total params: 2578102 (9.83 MB)\n", "Trainable params: 2532470 (9.66 MB)\n", "Non-trainable params: 45632 (178.25 KB)\n", "_________________________________________________________________\n", "Epoch 1/100\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.10/dist-packages/keras/src/backend.py:452: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.\n", " warnings.warn(\n", "WARNING:tensorflow:Gradients do not exist for variables ['conv2dbn_block_3/batch_normalization/gamma:0', 'conv2dbn_block_3/batch_normalization/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\n", "WARNING:tensorflow:Gradients do not exist for variables ['conv2dbn_block_3/batch_normalization/gamma:0', 'conv2dbn_block_3/batch_normalization/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\n", "WARNING:tensorflow:Gradients do not exist for variables ['conv2dbn_block_3/batch_normalization/gamma:0', 'conv2dbn_block_3/batch_normalization/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\n", "WARNING:tensorflow:Gradients do not exist for variables ['conv2dbn_block_3/batch_normalization/gamma:0', 'conv2dbn_block_3/batch_normalization/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "30/30 [==============================] - 66s 441ms/step - total_loss: 14.3356 - cls_loss: 13.9902 - box_loss: 0.0058 - model_loss: 14.2806 - val_total_loss: 1.0421 - val_cls_loss: 0.8848 - val_box_loss: 0.0020 - val_model_loss: 0.9872\n", "Epoch 2/100\n", "30/30 [==============================] - 5s 176ms/step - total_loss: 1.1946 - cls_loss: 0.9375 - box_loss: 0.0040 - model_loss: 1.1396 - val_total_loss: 0.8976 - val_cls_loss: 0.7552 - val_box_loss: 0.0017 - val_model_loss: 0.8426\n", "Epoch 3/100\n", "30/30 [==============================] - 5s 175ms/step - total_loss: 0.9612 - cls_loss: 0.7566 - box_loss: 0.0030 - model_loss: 0.9062 - val_total_loss: 0.6888 - val_cls_loss: 0.5560 - val_box_loss: 0.0016 - val_model_loss: 0.6338\n", "Epoch 4/100\n", "30/30 [==============================] - 6s 207ms/step - total_loss: 0.7521 - cls_loss: 0.5655 - box_loss: 0.0026 - model_loss: 0.6972 - val_total_loss: 0.5471 - val_cls_loss: 0.4144 - val_box_loss: 0.0016 - val_model_loss: 0.4921\n", "Epoch 5/100\n", "30/30 [==============================] - 5s 175ms/step - total_loss: 0.5779 - cls_loss: 0.4233 - box_loss: 0.0020 - model_loss: 0.5229 - val_total_loss: 0.4687 - val_cls_loss: 0.3420 - val_box_loss: 0.0014 - val_model_loss: 0.4137\n", "Epoch 6/100\n", "30/30 [==============================] - 6s 211ms/step - total_loss: 0.4730 - cls_loss: 0.3290 - box_loss: 0.0018 - model_loss: 0.4181 - val_total_loss: 0.4130 - val_cls_loss: 0.2878 - val_box_loss: 0.0014 - val_model_loss: 0.3580\n", "Epoch 7/100\n", "30/30 [==============================] - 5s 173ms/step - total_loss: 0.4050 - cls_loss: 0.2677 - box_loss: 0.0016 - model_loss: 0.3501 - val_total_loss: 0.3947 - val_cls_loss: 0.2682 - val_box_loss: 0.0014 - val_model_loss: 0.3398\n", "Epoch 8/100\n", "30/30 [==============================] - 6s 189ms/step - total_loss: 0.3531 - cls_loss: 0.2312 - box_loss: 0.0013 - model_loss: 0.2981 - val_total_loss: 0.3557 - val_cls_loss: 0.2361 - val_box_loss: 0.0013 - val_model_loss: 0.3007\n", "Epoch 9/100\n", "30/30 [==============================] - 5s 172ms/step - total_loss: 0.3165 - cls_loss: 0.1939 - box_loss: 0.0014 - model_loss: 0.2615 - val_total_loss: 0.3577 - val_cls_loss: 0.2351 - val_box_loss: 0.0014 - val_model_loss: 0.3028\n", "Epoch 10/100\n", "30/30 [==============================] - 6s 207ms/step - total_loss: 0.2874 - cls_loss: 0.1704 - box_loss: 0.0012 - model_loss: 0.2324 - val_total_loss: 0.3425 - val_cls_loss: 0.2216 - val_box_loss: 0.0013 - val_model_loss: 0.2875\n", "Epoch 11/100\n", "30/30 [==============================] - 7s 240ms/step - total_loss: 0.2890 - cls_loss: 0.1581 - box_loss: 0.0015 - model_loss: 0.2341 - val_total_loss: 0.3433 - val_cls_loss: 0.2185 - val_box_loss: 0.0014 - val_model_loss: 0.2883\n", "Epoch 12/100\n", "30/30 [==============================] - 6s 205ms/step - total_loss: 0.2900 - cls_loss: 0.1405 - box_loss: 0.0019 - model_loss: 0.2351 - val_total_loss: 0.3282 - val_cls_loss: 0.2080 - val_box_loss: 0.0013 - val_model_loss: 0.2732\n", "Epoch 13/100\n", "30/30 [==============================] - 6s 198ms/step - total_loss: 0.2404 - cls_loss: 0.1235 - box_loss: 0.0012 - model_loss: 0.1854 - val_total_loss: 0.3248 - val_cls_loss: 0.2061 - val_box_loss: 0.0013 - val_model_loss: 0.2699\n", "Epoch 14/100\n", "30/30 [==============================] - 6s 206ms/step - total_loss: 0.2353 - cls_loss: 0.1144 - box_loss: 0.0013 - model_loss: 0.1804 - val_total_loss: 0.3236 - val_cls_loss: 0.2079 - val_box_loss: 0.0012 - val_model_loss: 0.2686\n", "Epoch 15/100\n", "30/30 [==============================] - 5s 170ms/step - total_loss: 0.2212 - cls_loss: 0.1204 - box_loss: 9.1521e-04 - model_loss: 0.1662 - val_total_loss: 0.3054 - val_cls_loss: 0.1907 - val_box_loss: 0.0012 - val_model_loss: 0.2504\n", "Epoch 16/100\n", "30/30 [==============================] - 6s 213ms/step - total_loss: 0.2121 - cls_loss: 0.1135 - box_loss: 8.7249e-04 - model_loss: 0.1571 - val_total_loss: 0.3318 - val_cls_loss: 0.2135 - val_box_loss: 0.0013 - val_model_loss: 0.2769\n", "Epoch 17/100\n", "30/30 [==============================] - 5s 170ms/step - total_loss: 0.2067 - cls_loss: 0.1011 - box_loss: 0.0010 - model_loss: 0.1518 - val_total_loss: 0.3385 - val_cls_loss: 0.2174 - val_box_loss: 0.0013 - val_model_loss: 0.2835\n", "Epoch 18/100\n", "30/30 [==============================] - 6s 207ms/step - total_loss: 0.1969 - cls_loss: 0.0953 - box_loss: 9.3325e-04 - model_loss: 0.1419 - val_total_loss: 0.3334 - val_cls_loss: 0.2168 - val_box_loss: 0.0012 - val_model_loss: 0.2784\n", "Epoch 19/100\n", "30/30 [==============================] - 5s 168ms/step - total_loss: 0.1938 - cls_loss: 0.0807 - box_loss: 0.0012 - model_loss: 0.1389 - val_total_loss: 0.3305 - val_cls_loss: 0.2077 - val_box_loss: 0.0014 - val_model_loss: 0.2755\n", "Epoch 20/100\n", "30/30 [==============================] - 6s 199ms/step - total_loss: 0.2085 - cls_loss: 0.0807 - box_loss: 0.0015 - model_loss: 0.1535 - val_total_loss: 0.3459 - val_cls_loss: 0.2253 - val_box_loss: 0.0013 - val_model_loss: 0.2910\n", "Epoch 21/100\n", "30/30 [==============================] - 5s 168ms/step - total_loss: 0.1651 - cls_loss: 0.0721 - box_loss: 7.6187e-04 - model_loss: 0.1102 - val_total_loss: 0.3273 - val_cls_loss: 0.2085 - val_box_loss: 0.0013 - val_model_loss: 0.2723\n", "Epoch 22/100\n", "30/30 [==============================] - 6s 199ms/step - total_loss: 0.1857 - cls_loss: 0.0766 - box_loss: 0.0011 - model_loss: 0.1308 - val_total_loss: 0.3338 - val_cls_loss: 0.2168 - val_box_loss: 0.0012 - val_model_loss: 0.2788\n", "Epoch 23/100\n", "30/30 [==============================] - 6s 204ms/step - total_loss: 0.1784 - cls_loss: 0.0817 - box_loss: 8.3515e-04 - model_loss: 0.1235 - val_total_loss: 0.3349 - val_cls_loss: 0.2178 - val_box_loss: 0.0012 - val_model_loss: 0.2800\n", "Epoch 24/100\n", "30/30 [==============================] - 5s 169ms/step - total_loss: 0.2730 - cls_loss: 0.1471 - box_loss: 0.0014 - model_loss: 0.2180 - val_total_loss: 0.4500 - val_cls_loss: 0.3031 - val_box_loss: 0.0018 - val_model_loss: 0.3949\n", "Epoch 25/100\n", "30/30 [==============================] - 6s 189ms/step - total_loss: 0.2669 - cls_loss: 0.1418 - box_loss: 0.0014 - model_loss: 0.2118 - val_total_loss: 0.3923 - val_cls_loss: 0.2517 - val_box_loss: 0.0017 - val_model_loss: 0.3372\n", "Epoch 26/100\n", "30/30 [==============================] - 5s 174ms/step - total_loss: 0.1942 - cls_loss: 0.0968 - box_loss: 8.4464e-04 - model_loss: 0.1390 - val_total_loss: 0.3679 - val_cls_loss: 0.2363 - val_box_loss: 0.0015 - val_model_loss: 0.3128\n", "Epoch 27/100\n", "30/30 [==============================] - 6s 211ms/step - total_loss: 0.1984 - cls_loss: 0.0896 - box_loss: 0.0011 - model_loss: 0.1433 - val_total_loss: 0.3603 - val_cls_loss: 0.2296 - val_box_loss: 0.0015 - val_model_loss: 0.3051\n", "Epoch 28/100\n", "30/30 [==============================] - 6s 200ms/step - total_loss: 0.1724 - cls_loss: 0.0719 - box_loss: 9.0732e-04 - model_loss: 0.1173 - val_total_loss: 0.3525 - val_cls_loss: 0.2263 - val_box_loss: 0.0014 - val_model_loss: 0.2973\n", "Epoch 29/100\n", "30/30 [==============================] - 5s 176ms/step - total_loss: 0.1706 - cls_loss: 0.0753 - box_loss: 8.0214e-04 - model_loss: 0.1155 - val_total_loss: 0.3587 - val_cls_loss: 0.2341 - val_box_loss: 0.0014 - val_model_loss: 0.3036\n", "Epoch 30/100\n", "30/30 [==============================] - 5s 168ms/step - total_loss: 0.1683 - cls_loss: 0.0783 - box_loss: 6.9788e-04 - model_loss: 0.1132 - val_total_loss: 0.3515 - val_cls_loss: 0.2311 - val_box_loss: 0.0013 - val_model_loss: 0.2964\n", "Epoch 31/100\n", "30/30 [==============================] - 6s 207ms/step - total_loss: 0.1615 - cls_loss: 0.0685 - box_loss: 7.5762e-04 - model_loss: 0.1064 - val_total_loss: 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TransferLearning()\n", "TransferLearning_model.load_data()\n", "TransferLearning_model.train_model()" ] }, { "cell_type": "markdown", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wZTLElC3RNoW", "outputId": "0716a37c-0b43-4778-f007-44fb886f28f9" }, "source": [ "## Evaluating the model " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "bo4RkJVsRXmL", "outputId": "f487e576-cc89-4908-b554-c80e5eaa0672" }, "outputs": [], "source": [ "TransferLearning_model.evaluate_model()\n", "TransferLearning_model.export_model()\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 619 }, "id": "MApBXEdOLM70", "outputId": "03ae1cf5-9d4d-4f56-b1ee-a0367f42039c" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\aditi\\AppData\\Local\\Temp\\ipykernel_48100\\310583337.py:218: MatplotlibDeprecationWarning: Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.\n", " plt.subplot(4, 2, 2)\n" ] }, { "data": { "image/png": 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", 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0.0263 - box_loss: 6.6052e-04 - model_loss: 0.0593 - val_total_loss: 0.4672 - val_cls_loss: 0.3369 - val_box_loss: 0.0015 - val_model_loss: 0.4124\n", "Epoch 94/100\n", "30/30 [==============================] - 6s 210ms/step - total_loss: 0.1126 - cls_loss: 0.0160 - box_loss: 8.3728e-04 - model_loss: 0.0579 - val_total_loss: 0.4634 - val_cls_loss: 0.3342 - val_box_loss: 0.0015 - val_model_loss: 0.4087\n", "Epoch 95/100\n", "30/30 [==============================] - 6s 209ms/step - total_loss: 0.0937 - cls_loss: 0.0155 - box_loss: 4.7037e-04 - model_loss: 0.0390 - val_total_loss: 0.4526 - val_cls_loss: 0.3252 - val_box_loss: 0.0015 - val_model_loss: 0.3979\n", "Epoch 96/100\n", "30/30 [==============================] - 5s 172ms/step - total_loss: 0.0838 - cls_loss: 0.0101 - box_loss: 3.7900e-04 - model_loss: 0.0290 - val_total_loss: 0.4674 - val_cls_loss: 0.3381 - val_box_loss: 0.0015 - val_model_loss: 0.4127\n", "Epoch 97/100\n", "30/30 [==============================] - 6s 202ms/step - total_loss: 0.1081 - cls_loss: 0.0171 - box_loss: 7.2588e-04 - model_loss: 0.0534 - val_total_loss: 0.4780 - val_cls_loss: 0.3481 - val_box_loss: 0.0015 - val_model_loss: 0.4233\n", "Epoch 98/100\n", "30/30 [==============================] - 5s 170ms/step - total_loss: 0.0818 - cls_loss: 0.0094 - box_loss: 3.5368e-04 - model_loss: 0.0271 - val_total_loss: 0.4714 - val_cls_loss: 0.3459 - val_box_loss: 0.0014 - val_model_loss: 0.4167\n", "Epoch 99/100\n", "30/30 [==============================] - 5s 168ms/step - total_loss: 0.0778 - cls_loss: 0.0082 - box_loss: 2.9762e-04 - model_loss: 0.0231 - val_total_loss: 0.4700 - val_cls_loss: 0.3445 - val_box_loss: 0.0014 - val_model_loss: 0.4153\n", "Epoch 100/100\n", "30/30 [==============================] - 5s 173ms/step - total_loss: 0.0788 - cls_loss: 0.0074 - box_loss: 3.3287e-04 - model_loss: 0.0241 - val_total_loss: 0.4701 - val_cls_loss: 0.3474 - val_box_loss: 0.0014 - val_model_loss: 0.4154\n", "\"\"\"\n", "\n", "pattern = r\"total_loss: (\\d+\\.\\d+(?:e[+-]?\\d+)?) - cls_loss: (\\d+\\.\\d+(?:e[+-]?\\d+)?) - box_loss: (\\d+\\.\\d+(?:e[+-]?\\d+)?) - model_loss: (\\d+\\.\\d+(?:e[+-]?\\d+)?) - val_total_loss: (\\d+\\.\\d+(?:e[+-]?\\d+)?) - val_cls_loss: (\\d+\\.\\d+(?:e[+-]?\\d+)?) - val_box_loss: (\\d+\\.\\d+(?:e[+-]?\\d+)?) - val_model_loss: (\\d+\\.\\d+(?:e[+-]?\\d+)?)\"\n", "\n", "matches = re.findall(pattern, txt_file)\n", "# Convert the matched strings to float and separate them into lists\n", "total_losses, cls_losses, box_losses, model_losses, val_total_losses, val_cls_losses, val_box_losses, val_model_losses = zip(*((float(total), float(cls), float(box), float(model), float(val_total), float(val_cls), float(val_box), float(val_model)) \n", " for total, cls, box, model,val_total, val_cls, val_box, val_model in matches))\n", "\n", "epochs = range(1, len(total_losses) + 1)\n", "\n", "plt.figure(figsize=(9,9))\n", "plt.plot(epochs, total_losses, ('#D71939'), label=\"Total Loss\", linewidth=4)\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Total Loss\")\n", "plt.legend()\n", "plt.subplot(4, 2, 2)\n", "plt.plot(epochs, cls_losses, ('#D71939'), label=\"Classification Loss\",linewidth=4)\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Classification Loss\")\n", "plt.legend()\n", "\n", "plt.subplot(4, 2, 3)\n", "plt.plot(epochs, box_losses, ('#D71939'), label=\"Box Loss\",linewidth=4)\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Box Loss\")\n", "plt.legend()\n", "\n", "plt.subplot(4, 2, 4)\n", "plt.plot(epochs, model_losses, ('#D71939'), label=\"Model Loss\",linewidth=4)\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Model Loss\")\n", "plt.legend()\n", "\n", "plt.subplot(4, 2, 5)\n", "plt.plot(epochs, val_total_losses, ('#D71939'), label=\"Validation Total Loss\",linewidth=4)\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Validation Total Loss\")\n", "plt.legend()\n", "\n", "plt.subplot(4, 2, 6)\n", "plt.plot(epochs, val_cls_losses, ('#D71939'), label=\"Validation Classification Loss\",linewidth=4)\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Validation Classification Loss\")\n", "plt.legend()\n", "\n", "plt.subplot(4, 2, 7)\n", "plt.plot(epochs, val_box_losses, ('#D71939'), label=\"Validation Box Loss\",linewidth=4)\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Validation Box Loss\")\n", "plt.legend()\n", "plt.subplot(4, 2, 8)\n", "plt.plot(epochs, val_model_losses, ('#D71939'), label=\"Validation Model Loss\",linewidth=4)\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Validation Model Loss\")\n", "plt.legend()\n", "plt.show()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "-hMWBOveLY34", "outputId": "dcc957e7-9d42-46bf-944b-d0fdb54433a5" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "# Given data\n", "metrics = {\n", " 'AP': [0.712, 0.940, 0.898, 0.057, 0.800, 0.778],\n", " 'AR': [0.331, 0.762, 0.762, 0.200, 0.800, 0.822]\n", "}\n", "categories = ['IoU=0.50:0.95, all', 'IoU=0.50, all', 'IoU=0.75, all',\n", " 'IoU=0.50:0.95, small', 'IoU=0.50:0.95, medium', 'IoU=0.50:0.95, large']\n", "\n", "# Set up the matplotlib figure\n", "bar_width = 0.35 # the width of the bars\n", "index = np.arange(len(metrics['AP'])) # the label locations\n", "\n", "fig, ax1 = plt.subplots(figsize=(16,13))\n", "\n", "# Bar plots\n", "rects1 = ax1.bar(index - bar_width/2, metrics['AP'], bar_width, label='Average Precision (AP)', color='#DB2E4B') # noqa\n", "rects2 = ax1.bar(index + bar_width/2, metrics['AR'], bar_width, label='Average Recall (AR)', color='#FFF572') # noqa\n", "\n", "\n", "# Labels and legends\n", "ax1.set_xlabel('IoU Thresholds and Categories',fontsize=16)\n", "ax1.set_ylabel(' Metric value',fontsize=16)\n", "ax1.set_title('Average Precision, Average Recall per Category(Bolt)',fontsize=20)\n", "\n", "# Adjust tick label size (new)\n", "ax1.tick_params(axis='x', labelsize=14)\n", "ax1.tick_params(axis='y', labelsize=14)\n", "\n", "ax1.set_xticks(index)\n", "ax1.set_xticklabels(categories, rotation=45, fontsize=12)\n", "\n", "ax1.legend(loc='upper left',fontsize = 'large')\n", "plt.savefig('barplot.png')\n", "#plt.show()\n" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "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": "3.8.19" } }, "nbformat": 4, "nbformat_minor": 0 }