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added simple example

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vd
2022-09-09 23:26:27 +03:00
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU",
"gpuClass": "standard"
},
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DbeYatBLu8pf",
"outputId": "4ed57e67-ae61-4662-e0cc-faed1c4062e4"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cloning into 'triplet-loss-cars'...\n",
"remote: Enumerating objects: 17560, done.\u001b[K\n",
"remote: Counting objects: 100% (17560/17560), done.\u001b[K\n",
"remote: Compressing objects: 100% (9229/9229), done.\u001b[K\n",
"remote: Total 17560 (delta 8336), reused 17531 (delta 8326)\u001b[K\n",
"Receiving objects: 100% (17560/17560), 773.26 MiB | 16.83 MiB/s, done.\n",
"Resolving deltas: 100% (8336/8336), done.\n"
]
}
],
"source": [
"!git clone https://git.drivecast.tech/vd/triplet-loss-cars.git"
]
},
{
"cell_type": "code",
"source": [
"%cd triplet-loss-cars/"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "264RI2RHu_u4",
"outputId": "5cea8550-3c8f-453f-ee09-de205e117769"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content/triplet-loss-cars\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!./download-dataset"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lboCMkqrvQtO",
"outputId": "015dad3e-fda4-4daf-a86c-54bf37253fa4"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading...\n",
"From: https://drive.google.com/uc?id=1rP7GHDqx6BKTGTh9I6ecEmRgn5-HG1N0\n",
"To: /content/triplet-loss-cars/triplet_dataset.zip\n",
"100% 27.3M/27.3M [00:01<00:00, 20.6MB/s]\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "Jx07-q67wGfD"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!python3 train-embedding.py"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "iat8B1qNv96T",
"outputId": "712601bf-9a2a-44d9-8d27-6dbc82a941f1"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Using GPU.\n",
"Training.\n",
"Epoch: 1/10 - Training loss: 0.2827 - Test loss: 0.1240\n",
"Epoch: 2/10 - Training loss: 0.1167 - Test loss: 0.1096\n",
"Epoch: 3/10 - Training loss: 0.0887 - Test loss: 0.0779\n",
"Epoch: 4/10 - Training loss: 0.0789 - Test loss: 0.0882\n",
"Epoch: 5/10 - Training loss: 0.0718 - Test loss: 0.0383\n",
"Epoch: 6/10 - Training loss: 0.0643 - Test loss: 0.0566\n",
"^C\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!python3 train-classifier.py"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "XhCmibyKwC-U",
"outputId": "e4b28c5a-d878-40d5-aaaf-f256e1ab2fa7"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Using GPU.\n",
"Score: 0.9918\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!./download-test-videos"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9PRr_7pV4qwE",
"outputId": "a71579b1-992b-41db-a275-79e5e55b5715"
},
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading...\n",
"From: https://drive.google.com/uc?id=1Mm24z7fe1fkbcTt05IQpLlNdSALJQFRc\n",
"To: /content/triplet-loss-cars/test_videos_2022.zip\n",
"100% 212M/212M [00:03<00:00, 63.2MB/s]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!unzip -q ./yolov5.zip"
],
"metadata": {
"id": "wZjPyaNo6XAa"
},
"execution_count": 11,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!python3 model_inference.py"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "EW8pO4f86fBj",
"outputId": "15c057b3-c4df-43cb-9a6a-4201404b226e"
},
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Using GPU.\n",
"Fusing layers... \n",
"custom_YOLOv5l summary: 290 layers, 20852934 parameters, 0 gradients\n",
"WARNING: --img-size [1920, 1080] must be multiple of max stride 32, updating to [1920, 1088]\n",
"0\n",
"Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
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]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "jVEQTWoW6l_9"
},
"execution_count": null,
"outputs": []
}
]
}