{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"source": [
"#**Step 01: Clone the YOLOv10 Github Rep**"
],
"metadata": {
"id": "zmS6OemVl9d4"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-iDzyqG7llKq",
"outputId": "fe32e193-9c22-4541-c69d-5d423ebf7b3d"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cloning into 'yolov10'...\n",
"remote: Enumerating objects: 20304, done.\u001b[K\n",
"remote: Counting objects: 100% (1347/1347), done.\u001b[K\n",
"remote: Compressing objects: 100% (127/127), done.\u001b[K\n",
"remote: Total 20304 (delta 1301), reused 1223 (delta 1220), pack-reused 18957\u001b[K\n",
"Receiving objects: 100% (20304/20304), 11.18 MiB | 14.83 MiB/s, done.\n",
"Resolving deltas: 100% (14304/14304), done.\n",
"/content/yolov10\n"
]
}
],
"source": [
"!git clone https://github.com/THU-MIG/yolov10.git\n",
"%cd yolov10"
]
},
{
"cell_type": "markdown",
"source": [
"#**Step 02: Install All the Required Packages**"
],
"metadata": {
"id": "FeRR_yEtmZA9"
}
},
{
"cell_type": "code",
"source": [
"!pip install -q ."
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "EUeEPY0DqwLF",
"outputId": "a6ed17a3-07ec-4397-ede9-6cdf1322dd2a"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m32.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Building wheel for ultralytics (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"#**Step 03: Download the Pretrained Model Weights**"
],
"metadata": {
"id": "K8U4gfGnngpY"
}
},
{
"cell_type": "code",
"source": [
"import os\n",
"import urllib.request\n",
"#Create a directory for the weights in the current working directory\n",
"weights_dir = os.path.join(os.getcwd(),'weights')\n",
"os.makedirs(weights_dir, exist_ok = True)\n",
"#urls of the weight file\n",
"urls = [\"https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10n.pt\",\n",
" \"https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10s.pt\",\n",
" \"https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10m.pt\",\n",
" \"https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10b.pt\",\n",
" \"https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10l.pt\",\n",
" \"https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10x.pt\"]\n",
"\n",
"#Download each file\n",
"for url in urls:\n",
" filename = os.path.basename(url)\n",
" filepath = os.path.join(weights_dir, filename)\n",
" urllib.request.urlretrieve(url, filepath)\n",
" print(f\"Downloaded: {filepath}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "E-yvQyjUmgdo",
"outputId": "13c5d3f3-dd4d-473a-8846-1a49a34b7488"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloaded: /content/yolov10/weights/yolov10n.pt\n",
"Downloaded: /content/yolov10/weights/yolov10s.pt\n",
"Downloaded: /content/yolov10/weights/yolov10m.pt\n",
"Downloaded: /content/yolov10/weights/yolov10b.pt\n",
"Downloaded: /content/yolov10/weights/yolov10l.pt\n",
"Downloaded: /content/yolov10/weights/yolov10x.pt\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"#**Step 04: Inference on Image using the Pretrained Model**"
],
"metadata": {
"id": "6YM1kYg3qPLq"
}
},
{
"cell_type": "code",
"source": [
"!gdown \"https://drive.google.com/uc?id=1q-sNrJfUi6PjvT8mFfEw1Cq4eCavdklK&confirm=t\""
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WlMk4fkkop0M",
"outputId": "04a1c89d-bbfd-4638-f423-5536b475a4db"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading...\n",
"From: https://drive.google.com/uc?id=1q-sNrJfUi6PjvT8mFfEw1Cq4eCavdklK&confirm=t\n",
"To: /content/yolov10/image1.jpg\n",
"\r 0% 0.00/214k [00:00"
]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "markdown",
"source": [
"#**Step 05: Inference on Video using Pretrained Model**"
],
"metadata": {
"id": "RoHgZWicspDA"
}
},
{
"cell_type": "code",
"source": [
"!gdown \"https://drive.google.com/uc?id=1hl_NNFciFwImmqplL3RkozpiIa8I38cd&confirm=t\""
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "iCptYWmusiME",
"outputId": "1877a631-a90d-4e6c-f0d2-21c669f0b379"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading...\n",
"From: https://drive.google.com/uc?id=1hl_NNFciFwImmqplL3RkozpiIa8I38cd&confirm=t\n",
"To: /content/yolov10/demo.mp4\n",
"100% 11.5M/11.5M [00:00<00:00, 16.7MB/s]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!yolo task=detect mode=predict save=True model=weights/yolov10x.pt source = \"demo.mp4\""
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3eMkBfr7s0xz",
"outputId": "4532c29b-3d74-42e0-e4ad-d236ad303db2"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Ultralytics YOLOv8.1.34 🚀 Python-3.10.12 torch-2.3.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"YOLOv10x summary (fused): 503 layers, 31738160 parameters, 185184 gradients, 170.6 GFLOPs\n",
"\n",
"video 1/1 (frame 1/1314) /content/yolov10/demo.mp4: 384x640 2 cars, 2 trucks, 117.7ms\n",
"video 1/1 (frame 2/1314) /content/yolov10/demo.mp4: 384x640 1 car, 2 trucks, 36.4ms\n",
"video 1/1 (frame 3/1314) /content/yolov10/demo.mp4: 384x640 1 car, 3 trucks, 36.4ms\n",
"video 1/1 (frame 4/1314) /content/yolov10/demo.mp4: 384x640 2 cars, 1 truck, 34.7ms\n",
"video 1/1 (frame 5/1314) /content/yolov10/demo.mp4: 384x640 2 cars, 1 truck, 33.1ms\n",
"video 1/1 (frame 6/1314) /content/yolov10/demo.mp4: 384x640 2 cars, 1 truck, 33.1ms\n",
"video 1/1 (frame 7/1314) /content/yolov10/demo.mp4: 384x640 2 cars, 2 trucks, 33.1ms\n",
"video 1/1 (frame 8/1314) /content/yolov10/demo.mp4: 384x640 2 cars, 1 truck, 25.8ms\n",
"video 1/1 (frame 9/1314) /content/yolov10/demo.mp4: 384x640 3 cars, 23.6ms\n",
"video 1/1 (frame 10/1314) /content/yolov10/demo.mp4: 384x640 4 cars, 1 truck, 23.7ms\n",
"video 1/1 (frame 11/1314) /content/yolov10/demo.mp4: 384x640 4 cars, 1 truck, 23.5ms\n",
"video 1/1 (frame 12/1314) /content/yolov10/demo.mp4: 384x640 2 cars, 2 trucks, 23.9ms\n",
"video 1/1 (frame 13/1314) /content/yolov10/demo.mp4: 384x640 3 cars, 3 trucks, 25.4ms\n",
"video 1/1 (frame 14/1314) /content/yolov10/demo.mp4: 384x640 2 cars, 1 truck, 25.0ms\n",
"video 1/1 (frame 15/1314) /content/yolov10/demo.mp4: 384x640 3 cars, 3 trucks, 23.8ms\n",
"video 1/1 (frame 16/1314) /content/yolov10/demo.mp4: 384x640 2 cars, 2 trucks, 23.9ms\n",
"video 1/1 (frame 17/1314) /content/yolov10/demo.mp4: 384x640 2 cars, 3 trucks, 23.9ms\n",
"video 1/1 (frame 18/1314) /content/yolov10/demo.mp4: 384x640 4 cars, 1 truck, 23.9ms\n",
"video 1/1 (frame 19/1314) /content/yolov10/demo.mp4: 384x640 3 cars, 2 trucks, 23.9ms\n",
"video 1/1 (frame 20/1314) /content/yolov10/demo.mp4: 384x640 3 cars, 1 truck, 23.4ms\n",
"video 1/1 (frame 21/1314) /content/yolov10/demo.mp4: 384x640 4 cars, 1 truck, 23.3ms\n",
"video 1/1 (frame 22/1314) /content/yolov10/demo.mp4: 384x640 3 cars, 1 truck, 24.1ms\n",
"video 1/1 (frame 23/1314) /content/yolov10/demo.mp4: 384x640 3 cars, 2 trucks, 23.8ms\n",
"video 1/1 (frame 24/1314) /content/yolov10/demo.mp4: 384x640 3 cars, 1 truck, 24.0ms\n",
"video 1/1 (frame 25/1314) /content/yolov10/demo.mp4: 384x640 4 cars, 2 trucks, 24.0ms\n",
"video 1/1 (frame 26/1314) /content/yolov10/demo.mp4: 384x640 4 cars, 28.1ms\n",
"video 1/1 (frame 27/1314) /content/yolov10/demo.mp4: 384x640 4 cars, 27.2ms\n",
"video 1/1 (frame 28/1314) /content/yolov10/demo.mp4: 384x640 4 cars, 23.4ms\n",
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"video 1/1 (frame 30/1314) /content/yolov10/demo.mp4: 384x640 4 cars, 25.3ms\n",
"video 1/1 (frame 31/1314) /content/yolov10/demo.mp4: 384x640 4 cars, 25.2ms\n",
"video 1/1 (frame 32/1314) /content/yolov10/demo.mp4: 384x640 4 cars, 2 trucks, 23.1ms\n",
"video 1/1 (frame 33/1314) /content/yolov10/demo.mp4: 384x640 4 cars, 1 truck, 28.2ms\n",
"video 1/1 (frame 34/1314) /content/yolov10/demo.mp4: 384x640 5 cars, 1 truck, 23.2ms\n",
"video 1/1 (frame 35/1314) /content/yolov10/demo.mp4: 384x640 5 cars, 1 truck, 22.7ms\n",
"video 1/1 (frame 36/1314) /content/yolov10/demo.mp4: 384x640 4 cars, 2 trucks, 23.7ms\n",
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"video 1/1 (frame 1302/1314) /content/yolov10/demo.mp4: 384x640 8 cars, 3 trucks, 24.6ms\n",
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"video 1/1 (frame 1307/1314) /content/yolov10/demo.mp4: 384x640 7 cars, 2 trucks, 25.3ms\n",
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"video 1/1 (frame 1309/1314) /content/yolov10/demo.mp4: 384x640 5 cars, 2 trucks, 23.8ms\n",
"video 1/1 (frame 1310/1314) /content/yolov10/demo.mp4: 384x640 6 cars, 2 trucks, 24.7ms\n",
"video 1/1 (frame 1311/1314) /content/yolov10/demo.mp4: 384x640 5 cars, 2 trucks, 24.6ms\n",
"video 1/1 (frame 1312/1314) /content/yolov10/demo.mp4: 384x640 5 cars, 2 trucks, 24.2ms\n",
"video 1/1 (frame 1313/1314) /content/yolov10/demo.mp4: 384x640 5 cars, 2 trucks, 24.1ms\n",
"video 1/1 (frame 1314/1314) /content/yolov10/demo.mp4: 384x640 5 cars, 2 trucks, 24.3ms\n",
"Speed: 1.8ms preprocess, 24.6ms inference, 1.3ms postprocess per image at shape (1, 3, 384, 640)\n",
"Results saved to \u001b[1mruns/detect/predict2\u001b[0m\n",
"💡 Learn more at https://docs.ultralytics.com/modes/predict\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!rm '/content/result_compressed.mp4'"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WuHHJP3ntCdv",
"outputId": "b571354b-da65-4b49-a60b-ba4a86da2472"
},
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"rm: cannot remove '/content/result_compressed.mp4': No such file or directory\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#Display output video\n",
"from IPython.display import HTML\n",
"from base64 import b64encode\n",
"import os\n",
"\n",
"# Input video path\n",
"save_path = 'runs/detect/predict2/demo.avi'\n",
"\n",
"# Compressed video path\n",
"compressed_path = \"/content/result_compressed.mp4\"\n",
"\n",
"os.system(f\"ffmpeg -i {save_path} -vcodec libx264 {compressed_path}\")\n",
"\n",
"# Show video\n",
"mp4 = open(compressed_path,'rb').read()\n",
"data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
"HTML(\"\"\"\n",
"\n",
"\"\"\" % data_url)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 247
},
"id": "xKac_4dPs40w",
"outputId": "d6678612-a78a-43ae-bbba-37247c286551"
},
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
],
"text/html": [
"\n",
"