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yolov5/demo.ipynb

210 lines
5.8 MiB

4 years ago
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 导入相关库"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 53
},
"colab_type": "code",
"id": "wbvMlHd_QwMG",
"outputId": "669566b2-391f-4596-f290-110e2e177946"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Setup complete. Using torch 1.8.1+cu102 CPU\n"
]
}
],
"source": [
"import torch\n",
"import os\n",
"from IPython.display import Image, clear_output # to display images\n",
"from utils.google_utils import gdrive_download # to download models/datasets\n",
"\n",
"clear_output()\n",
"print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "N3qM6T0W53gh"
},
"source": [
"## 图像目标检测"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', img_size=416, iou_thres=0.5, output='inference/output', save_txt=False, source='inference/images/', update=False, view_img=False, weights=['yolov5s.pt'])\n",
"Using CPU\n",
"\n",
"Fusing layers... \n",
"Model Summary: 140 layers, 7.45958e+06 parameters, 6.61683e+06 gradients\n",
"image 1/5 /root/yolov5/inference/images/all.jpg: 352x416 11 persons, Done. (0.118s)\n",
"image 2/5 /root/yolov5/inference/images/bus.jpg: 416x352 3 persons, 1 buss, Done. (0.135s)\n",
"image 3/5 /root/yolov5/inference/images/office1.jpg: 352x416 5 persons, 3 chairs, Done. (0.115s)\n",
"image 4/5 /root/yolov5/inference/images/office2.jpg: 352x416 4 chairs, 1 tvs, Done. (0.118s)\n",
"image 5/5 /root/yolov5/inference/images/zidane.jpg: 288x416 2 persons, 1 ties, Done. (0.100s)\n",
"Results saved to inference/output\n",
"Done. (2.070s)\n"
]
}
],
"source": [
"!python detect.py --weights yolov5s.pt --img 416 --conf 0.4 --source inference/images/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 图片1"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/jpeg": "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
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 14,
"metadata": {
"image/jpeg": {
"width": 600
}
},
"output_type": "execute_result"
}
],
"source": [
"Image(filename='inference/output/bus.jpg', width=600)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 图片2"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/jpeg": "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
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 18,
"metadata": {
"image/jpeg": {
"width": 600
}
},
"output_type": "execute_result"
}
],
"source": [
"Image(filename='inference/output/office2.jpg', width=600)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 图片3"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 36,
"metadata": {
"image/jpeg": {
"width": 600
}
},
"output_type": "execute_result"
}
],
"source": [
"Image(filename='inference/output/zidane.jpg', width=600)"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"include_colab_link": true,
"name": "YOLOv5 Tutorial",
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"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": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}