start working on object-detection
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CIFAR-10.ipynb
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CIFAR-10.ipynb
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TorchObjectDetection.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"本教程将会在预训练模型 [Mask R_CNN](https://arxiv.org/abs/1703.06870) 上针对 [Penn-Fudan Database for Pedestrian Detection and Segmentation](https://www.cis.upenn.edu/~jshi/ped_html/)数据进行调优。这个数据集有170张图片,345个行人,通过本教程可学习到如何使用 torchvision 的新特性来训练针对特定数据集的分割模型。\n",
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"\n",
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"\n",
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"# 定义数据集\n",
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"\n",
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"按照训练物体检测,分割和人体关键点模型的参考脚本,可以很方便地支持添加新的自定义数据集。新的数据集必须继承 `torch.utils.data.Dataset` 类,同时实现 `__len__` 和 `__getitem__` 方法\n",
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"\n",
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"唯一需要注意的话,我们要坟 `__getitem__` 返回的格式如下:\n",
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"\n",
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"* image: 一个 `PILImage` 图像对象,其尺寸为 `(H,W)`\n",
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"* target: 一个 `dict` 对象,含有以下的键:\n",
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" * `boxes[FloatTensor[N, 4)`: 含有 `N` 个 bounding box 的数组,其元素为4个,格式为`[x0, y0, x1, y1]`\n",
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" * `labels (Int64Tensor[N])`: 每个 bounding box 的标签。 `0` 表示背景\n",
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" * `image_id (Int64Tensor[1])`: 图像id,必须在整个数据集中唯一。\n",
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" * `area (Tensor[N])`: bounding box 的面积。用以 Coco metric 评估,分离大小不同的boxes\n",
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" * `iscrowd (UintTensor[N])': 该值为True时,将不会被用以评估\n",
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" * 可选 `masks (UInt8Tensor[N, H, W])`: 每个物体的分离蒙板\n",
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" * 可选 `keypoints (FloatTensor[N, K, 3])`: 对于 `N` 个物体,含有 `K` 个关键点。关键点的格式为`[x, y, visibility]`。`visibility=0` 表示关键点不可见。\n",
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"\n",
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"\n",
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"data source: https://www.cis.upenn.edu/~jshi/ped_html/PennFudanPed.zip"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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