{ "cells": [ { "cell_type": "markdown", "id": "438daa7c", "metadata": { "origin_pos": 0 }, "source": [ "# Phân đoạn ngữ nghĩa và tập dữ liệu\n", ":label:`sec_semantic_segmentation`\n", "\n", "Khi thảo luận về các tác vụ phát hiện đối tượng trong :numref:`sec_bbox`—:numref:`sec_rcnn`, các hộp giới hạn hình chữ nhật được sử dụng để dán nhãn và dự đoán các đối tượng trong ảnh. Phần này sẽ thảo luận về vấn đề phân đoạn *ngữ nghĩa *, tập trung vào cách chia hình ảnh thành các vùng thuộc các lớp ngữ nghĩa khác nhau. Khác với phát hiện đối tượng, phân đoạn ngữ nghĩa nhận ra và hiểu những gì có trong hình ảnh ở cấp độ pixel: ghi nhãn và dự đoán các vùng ngữ nghĩa của nó ở mức pixel. :numref:`fig_segmentation` hiển thị nhãn của chó, mèo và nền của hình ảnh trong phân khúc ngữ nghĩa. So với trong phát hiện đối tượng, các đường viền cấp pixel được dán nhãn trong phân đoạn ngữ nghĩa rõ ràng là hạt mịn hơn. \n", "\n", "![Labels of the dog, cat, and background of the image in semantic segmentation.](../img/segmentation.svg)\n", ":label:`fig_segmentation`\n", "\n", "## Phân đoạn hình ảnh và Phân đoạn phiên bản\n", "\n", "Ngoài ra còn có hai nhiệm vụ quan trọng trong lĩnh vực thị giác máy tính tương tự như phân đoạn ngữ nghĩa, đó là phân đoạn hình ảnh và phân đoạn phiên bản. Chúng tôi sẽ phân biệt ngắn gọn chúng với phân khúc ngữ nghĩa như sau. \n", "\n", "* *Phân khúc hình ảnh* chia một hình ảnh thành nhiều vùng cấu thành. Các phương pháp cho loại vấn đề này thường sử dụng mối tương quan giữa các pixel trong hình ảnh. Nó không cần thông tin nhãn về pixel hình ảnh trong quá trình đào tạo và nó không thể đảm bảo rằng các vùng được phân đoạn sẽ có ngữ nghĩa mà chúng tôi hy vọng sẽ có được trong quá trình dự đoán. Chụp ảnh trong :numref:`fig_segmentation` làm đầu vào, phân đoạn hình ảnh có thể chia chó thành hai vùng: một vùng che miệng và mắt chủ yếu là màu đen, và phần còn lại bao phủ phần còn lại của cơ thể chủ yếu là màu vàng.\n", "* * Phân khúc Instance* còn được gọi là * phát hiện và phân đoạn đồng thời*. Nó nghiên cứu làm thế nào để nhận ra các vùng cấp pixel của mỗi đối tượng trong một hình ảnh. Khác với phân đoạn ngữ nghĩa, phân đoạn phiên bản cần phân biệt không chỉ ngữ nghĩa, mà còn các trường hợp đối tượng khác nhau. Ví dụ, nếu có hai chú chó trong hình ảnh, phân đoạn ví dụ cần phân biệt một pixel thuộc về cái nào trong hai chú chó.\n", "\n", "## Tập dữ liệu phân đoạn ngữ nghĩa Pascal VOC2012\n", "\n", "[**Trên tập dữ liệu phân đoạn ngữ nghĩa quan trọng nhất là [Pascal VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/).**] Sau đây, chúng ta sẽ xem tập dữ liệu này.\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "06a0a842", "metadata": { "execution": { "iopub.execute_input": "2022-04-26T10:55:19.651225Z", "iopub.status.busy": "2022-04-26T10:55:19.650561Z", "iopub.status.idle": "2022-04-26T10:55:22.686883Z", "shell.execute_reply": "2022-04-26T10:55:22.685798Z" }, "origin_pos": 1, "tab": [ "mxnet" ] }, "outputs": [], "source": [ "%matplotlib inline\n", "import os\n", "from mxnet import gluon, image, np, npx\n", "from d2l import mxnet as d2l\n", "\n", "npx.set_np()" ] }, { "cell_type": "markdown", "id": "d0523d28", "metadata": { "origin_pos": 3 }, "source": [ "Tệp tar của tập dữ liệu khoảng 2 GB, vì vậy có thể mất một thời gian để tải xuống tệp. Tập dữ liệu được trích xuất nằm ở `../data/VOCdevkit/VOC2012`.\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "9a67ed41", "metadata": { "execution": { "iopub.execute_input": "2022-04-26T10:55:22.690989Z", "iopub.status.busy": "2022-04-26T10:55:22.690603Z", "iopub.status.idle": "2022-04-26T10:55:42.034416Z", "shell.execute_reply": "2022-04-26T10:55:42.033369Z" }, "origin_pos": 4, "tab": [ "mxnet" ] }, "outputs": [], "source": [ "#@save\n", "d2l.DATA_HUB['voc2012'] = (d2l.DATA_URL + 'VOCtrainval_11-May-2012.tar',\n", " '4e443f8a2eca6b1dac8a6c57641b67dd40621a49')\n", "\n", "voc_dir = d2l.download_extract('voc2012', 'VOCdevkit/VOC2012')" ] }, { "cell_type": "markdown", "id": "79789052", "metadata": { "origin_pos": 5 }, "source": [ "Sau khi nhập đường dẫn `../data/VOCdevkit/VOC2012`, chúng ta có thể thấy các thành phần khác nhau của tập dữ liệu. Đường dẫn `ImageSets/Segmentation` chứa các tệp văn bản chỉ định các mẫu đào tạo và thử nghiệm, trong khi các đường dẫn `JPEGImages` và `SegmentationClass` lưu trữ hình ảnh đầu vào và nhãn cho mỗi ví dụ tương ứng. Nhãn ở đây cũng ở định dạng hình ảnh, có cùng kích thước với hình ảnh đầu vào được dán nhãn của nó. Bên cạnh đó, các pixel có cùng màu trong bất kỳ hình ảnh nhãn nào thuộc cùng một lớp ngữ nghĩa. Sau đây xác định hàm `read_voc_images` thành [** đọc tất cả các hình ảnh đầu vào và nhãn vào bộ nhớ**].\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "5f21444e", "metadata": { "execution": { "iopub.execute_input": "2022-04-26T10:55:42.039461Z", "iopub.status.busy": "2022-04-26T10:55:42.038889Z", "iopub.status.idle": "2022-04-26T10:55:42.472732Z", "shell.execute_reply": "2022-04-26T10:55:42.471769Z" }, "origin_pos": 6, "tab": [ "mxnet" ] }, "outputs": [], "source": [ "#@save\n", "def read_voc_images(voc_dir, is_train=True):\n", " \"\"\"Read all VOC feature and label images.\"\"\"\n", " txt_fname = os.path.join(voc_dir, 'ImageSets', 'Segmentation',\n", " 'train.txt' if is_train else 'val.txt')\n", " with open(txt_fname, 'r') as f:\n", " images = f.read().split()\n", " features, labels = [], []\n", " for i, fname in enumerate(images):\n", " features.append(image.imread(os.path.join(\n", " voc_dir, 'JPEGImages', f'{fname}.jpg')))\n", " labels.append(image.imread(os.path.join(\n", " voc_dir, 'SegmentationClass', f'{fname}.png')))\n", " return features, labels\n", "\n", "train_features, train_labels = read_voc_images(voc_dir, True)" ] }, { "cell_type": "markdown", "id": "024099d2", "metadata": { "origin_pos": 8 }, "source": [ "Chúng tôi [** vẽ năm hình ảnh đầu vào đầu tiên và nhãn của chúng**]. Trong hình ảnh nhãn, màu trắng và đen đại diện cho đường viền và nền tương ứng, trong khi các màu khác tương ứng với các lớp khác nhau.\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "132e6824", "metadata": { "execution": { "iopub.execute_input": "2022-04-26T10:55:42.478884Z", "iopub.status.busy": "2022-04-26T10:55:42.478216Z", "iopub.status.idle": "2022-04-26T10:55:43.580932Z", "shell.execute_reply": "2022-04-26T10:55:43.578655Z" }, "origin_pos": 9, "tab": [ "mxnet" ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "n = 5\n", "imgs = train_features[0:n] + train_labels[0:n]\n", "d2l.show_images(imgs, 2, n);" ] }, { "cell_type": "markdown", "id": "8d0ff4b0", "metadata": { "origin_pos": 11 }, "source": [ "Tiếp theo, chúng ta [** liệt kê các giá trị màu RGB và tên lớp **] cho tất cả các nhãn trong tập dữ liệu này.\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "b7d1a694", "metadata": { "execution": { "iopub.execute_input": "2022-04-26T10:55:43.585901Z", "iopub.status.busy": "2022-04-26T10:55:43.585119Z", "iopub.status.idle": "2022-04-26T10:55:43.599687Z", "shell.execute_reply": "2022-04-26T10:55:43.598731Z" }, "origin_pos": 12, "tab": [ "mxnet" ] }, "outputs": [], "source": [ "#@save\n", "VOC_COLORMAP = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0],\n", " [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128],\n", " [64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0],\n", " [64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128],\n", " [0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0],\n", " [0, 64, 128]]\n", "\n", "#@save\n", "VOC_CLASSES = ['background', 'aeroplane', 'bicycle', 'bird', 'boat',\n", " 'bottle', 'bus', 'car', 'cat', 'chair', 'cow',\n", " 'diningtable', 'dog', 'horse', 'motorbike', 'person',\n", " 'potted plant', 'sheep', 'sofa', 'train', 'tv/monitor']" ] }, { "cell_type": "markdown", "id": "5d264cf2", "metadata": { "origin_pos": 13 }, "source": [ "Với hai hằng số được định nghĩa ở trên, chúng ta có thể thuận tiện [** tìm chỉ mục lớp cho mỗi pixel trong một nhãy**]. Chúng tôi xác định hàm `voc_colormap2label` để xây dựng ánh xạ từ các giá trị màu RGB ở trên đến các chỉ số lớp và hàm `voc_label_indices` để ánh xạ bất kỳ giá trị RGB nào với chỉ số lớp của chúng trong bộ dữ liệu Pascal VOC2012 này.\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "12307f1c", "metadata": { "execution": { "iopub.execute_input": "2022-04-26T10:55:43.605762Z", "iopub.status.busy": "2022-04-26T10:55:43.603769Z", "iopub.status.idle": "2022-04-26T10:55:43.615247Z", "shell.execute_reply": "2022-04-26T10:55:43.614486Z" }, "origin_pos": 14, "tab": [ "mxnet" ] }, "outputs": [], "source": [ "#@save\n", "def voc_colormap2label():\n", " \"\"\"Build the mapping from RGB to class indices for VOC labels.\"\"\"\n", " colormap2label = np.zeros(256 ** 3)\n", " for i, colormap in enumerate(VOC_COLORMAP):\n", " colormap2label[\n", " (colormap[0] * 256 + colormap[1]) * 256 + colormap[2]] = i\n", " return colormap2label\n", "\n", "#@save\n", "def voc_label_indices(colormap, colormap2label):\n", " \"\"\"Map any RGB values in VOC labels to their class indices.\"\"\"\n", " colormap = colormap.astype(np.int32)\n", " idx = ((colormap[:, :, 0] * 256 + colormap[:, :, 1]) * 256\n", " + colormap[:, :, 2])\n", " return colormap2label[idx]" ] }, { "cell_type": "markdown", "id": "5413ecee", "metadata": { "origin_pos": 16 }, "source": [ "[**Ví dụ**], trong ảnh ví dụ đầu tiên, chỉ mục lớp cho phần trước của máy bay là 1, trong khi chỉ mục nền là 0.\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "4390a51a", "metadata": { "execution": { "iopub.execute_input": "2022-04-26T10:55:43.619215Z", "iopub.status.busy": "2022-04-26T10:55:43.618732Z", "iopub.status.idle": "2022-04-26T10:56:00.456660Z", "shell.execute_reply": "2022-04-26T10:56:00.455717Z" }, "origin_pos": 17, "tab": [ "mxnet" ] }, "outputs": [ { "data": { "text/plain": [ "(array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n", " [0., 0., 0., 0., 0., 0., 0., 1., 1., 1.],\n", " [0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n", " [0., 0., 0., 0., 0., 1., 1., 1., 1., 1.],\n", " [0., 0., 0., 0., 0., 1., 1., 1., 1., 1.],\n", " [0., 0., 0., 0., 1., 1., 1., 1., 1., 1.],\n", " [0., 0., 0., 0., 0., 1., 1., 1., 1., 1.],\n", " [0., 0., 0., 0., 0., 1., 1., 1., 1., 1.],\n", " [0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n", " [0., 0., 0., 0., 0., 0., 0., 0., 1., 1.]]),\n", " 'aeroplane')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = voc_label_indices(train_labels[0], voc_colormap2label())\n", "y[105:115, 130:140], VOC_CLASSES[1]" ] }, { "cell_type": "markdown", "id": "96b487bd", "metadata": { "origin_pos": 18 }, "source": [ "### Xử lý sơ bộ dữ liệu\n", "\n", "Trong các thí nghiệm trước đó như trong :numref:`sec_alexnet`—:numref:`sec_googlenet`, hình ảnh được thay đổi lại để phù hợp với hình dạng đầu vào yêu cầu của mô hình. Tuy nhiên, trong phân đoạn ngữ nghĩa, làm như vậy đòi hỏi phải thay đổi lại các lớp pixel dự đoán trở lại hình dạng ban đầu của hình ảnh đầu vào. Việc tái cặn như vậy có thể không chính xác, đặc biệt là đối với các vùng được phân đoạn với các lớp khác nhau. Để tránh sự cố này, chúng tôi cắt hình ảnh thành một hình dạng *fixed* thay vì rescaling. Cụ thể, [** sử dụng cắt xén ngẫu nhiên từ nâng hình ảnh, chúng ta cắt cùng một vùng của hình ảnh đầu vào và nhãn**].\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "1163b069", "metadata": { "execution": { "iopub.execute_input": "2022-04-26T10:56:00.460266Z", "iopub.status.busy": "2022-04-26T10:56:00.460054Z", "iopub.status.idle": "2022-04-26T10:56:00.474438Z", "shell.execute_reply": "2022-04-26T10:56:00.473001Z" }, "origin_pos": 19, "tab": [ "mxnet" ] }, "outputs": [], "source": [ "#@save\n", "def voc_rand_crop(feature, label, height, width):\n", " \"\"\"Randomly crop both feature and label images.\"\"\"\n", " feature, rect = image.random_crop(feature, (width, height))\n", " label = image.fixed_crop(label, *rect)\n", " return feature, label" ] }, { "cell_type": "code", "execution_count": 9, "id": "71a4ac66", "metadata": { "execution": { "iopub.execute_input": "2022-04-26T10:56:00.481687Z", "iopub.status.busy": "2022-04-26T10:56:00.479703Z", "iopub.status.idle": "2022-04-26T10:56:01.138213Z", "shell.execute_reply": "2022-04-26T10:56:01.137451Z" }, "origin_pos": 21, "tab": [ "mxnet" ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "imgs = []\n", "for _ in range(n):\n", " imgs += voc_rand_crop(train_features[0], train_labels[0], 200, 300)\n", "d2l.show_images(imgs[::2] + imgs[1::2], 2, n);" ] }, { "cell_type": "markdown", "id": "5a7cde47", "metadata": { "origin_pos": 23 }, "source": [ "### [**Custom Semantic Segmentation Dataset Class**]\n", "\n", "Chúng tôi xác định một tập dữ liệu phân đoạn ngữ nghĩa tùy chỉnh lớp `VOCSegDataset` bằng cách kế thừa lớp `Dataset` được cung cấp bởi các API cấp cao. Bằng cách thực hiện hàm `__getitem__`, chúng ta có thể tùy ý truy cập vào hình ảnh đầu vào được lập chỉ mục là `idx` trong tập dữ liệu và chỉ số lớp của mỗi pixel trong hình ảnh này. Vì một số hình ảnh trong tập dữ liệu có kích thước nhỏ hơn kích thước đầu ra của cắt ngẫu nhiên, các ví dụ này được lọc ra bởi một hàm `filter` tùy chỉnh. Ngoài ra, chúng tôi cũng xác định hàm `normalize_image` để chuẩn hóa các giá trị của ba kênh RGB của hình ảnh đầu vào.\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "1f65eb56", "metadata": { "execution": { "iopub.execute_input": "2022-04-26T10:56:01.142716Z", "iopub.status.busy": "2022-04-26T10:56:01.142185Z", "iopub.status.idle": "2022-04-26T10:56:01.150692Z", "shell.execute_reply": "2022-04-26T10:56:01.150016Z" }, "origin_pos": 24, "tab": [ "mxnet" ] }, "outputs": [], "source": [ "#@save\n", "class VOCSegDataset(gluon.data.Dataset):\n", " \"\"\"A customized dataset to load the VOC dataset.\"\"\"\n", " def __init__(self, is_train, crop_size, voc_dir):\n", " self.rgb_mean = np.array([0.485, 0.456, 0.406])\n", " self.rgb_std = np.array([0.229, 0.224, 0.225])\n", " self.crop_size = crop_size\n", " features, labels = read_voc_images(voc_dir, is_train=is_train)\n", " self.features = [self.normalize_image(feature)\n", " for feature in self.filter(features)]\n", " self.labels = self.filter(labels)\n", " self.colormap2label = voc_colormap2label()\n", " print('read ' + str(len(self.features)) + ' examples')\n", "\n", " def normalize_image(self, img):\n", " return (img.astype('float32') / 255 - self.rgb_mean) / self.rgb_std\n", "\n", " def filter(self, imgs):\n", " return [img for img in imgs if (\n", " img.shape[0] >= self.crop_size[0] and\n", " img.shape[1] >= self.crop_size[1])]\n", "\n", " def __getitem__(self, idx):\n", " feature, label = voc_rand_crop(self.features[idx], self.labels[idx],\n", " *self.crop_size)\n", " return (feature.transpose(2, 0, 1),\n", " voc_label_indices(label, self.colormap2label))\n", "\n", " def __len__(self):\n", " return len(self.features)" ] }, { "cell_type": "markdown", "id": "72b0ae44", "metadata": { "origin_pos": 26 }, "source": [ "### [**Đọc dữ liệu**]\n", "\n", "Chúng tôi sử dụng lớp `VOCSegDatase`t tùy chỉnh để tạo các phiên bản của bộ đào tạo và bộ kiểm tra, tương ứng. Giả sử rằng chúng ta chỉ định rằng hình dạng đầu ra của hình ảnh được cắt ngẫu nhiên là $320\\times 480$. Dưới đây chúng ta có thể xem số ví dụ được giữ lại trong bộ đào tạo và bộ kiểm tra.\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "b35767a6", "metadata": { "execution": { "iopub.execute_input": "2022-04-26T10:56:01.154290Z", "iopub.status.busy": "2022-04-26T10:56:01.153808Z", "iopub.status.idle": "2022-04-26T10:56:24.460038Z", "shell.execute_reply": "2022-04-26T10:56:24.459237Z" }, "origin_pos": 27, "tab": [ "mxnet" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "read 1114 examples\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "read 1078 examples\n" ] } ], "source": [ "crop_size = (320, 480)\n", "voc_train = VOCSegDataset(True, crop_size, voc_dir)\n", "voc_test = VOCSegDataset(False, crop_size, voc_dir)" ] }, { "cell_type": "markdown", "id": "3c56aebc", "metadata": { "origin_pos": 28 }, "source": [ "Đặt kích thước lô thành 64, chúng tôi xác định bộ lặp dữ liệu cho bộ đào tạo. Hãy để chúng tôi in hình dạng của minibatch đầu tiên. Khác với phân loại hình ảnh hoặc phát hiện đối tượng, nhãn ở đây là hàng chục ba chiều.\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "3e69c03c", "metadata": { "execution": { "iopub.execute_input": "2022-04-26T10:56:24.468875Z", "iopub.status.busy": "2022-04-26T10:56:24.465687Z", "iopub.status.idle": "2022-04-26T10:57:07.955016Z", "shell.execute_reply": "2022-04-26T10:57:07.953844Z" }, "origin_pos": 29, "tab": [ "mxnet" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(64, 3, 320, 480)\n", "(64, 320, 480)\n" ] } ], "source": [ "batch_size = 64\n", "train_iter = gluon.data.DataLoader(voc_train, batch_size, shuffle=True,\n", " last_batch='discard',\n", " num_workers=d2l.get_dataloader_workers())\n", "for X, Y in train_iter:\n", " print(X.shape)\n", " print(Y.shape)\n", " break" ] }, { "cell_type": "markdown", "id": "3f029bb4", "metadata": { "origin_pos": 31 }, "source": [ "### [** Putting All Things Together**]\n", "\n", "Cuối cùng, chúng tôi xác định hàm `load_data_voc` sau để tải xuống và đọc tập dữ liệu phân đoạn ngữ nghĩa Pascal VOC2012. Nó trả về bộ lặp dữ liệu cho cả tập dữ liệu đào tạo và kiểm tra.\n" ] }, { "cell_type": "code", "execution_count": 13, "id": "fa54d527", "metadata": { "execution": { "iopub.execute_input": "2022-04-26T10:57:07.965025Z", "iopub.status.busy": "2022-04-26T10:57:07.964418Z", "iopub.status.idle": "2022-04-26T10:57:07.973547Z", "shell.execute_reply": "2022-04-26T10:57:07.972735Z" }, "origin_pos": 32, "tab": [ "mxnet" ] }, "outputs": [], "source": [ "#@save\n", "def load_data_voc(batch_size, crop_size):\n", " \"\"\"Load the VOC semantic segmentation dataset.\"\"\"\n", " voc_dir = d2l.download_extract('voc2012', os.path.join(\n", " 'VOCdevkit', 'VOC2012'))\n", " num_workers = d2l.get_dataloader_workers()\n", " train_iter = gluon.data.DataLoader(\n", " VOCSegDataset(True, crop_size, voc_dir), batch_size,\n", " shuffle=True, last_batch='discard', num_workers=num_workers)\n", " test_iter = gluon.data.DataLoader(\n", " VOCSegDataset(False, crop_size, voc_dir), batch_size,\n", " last_batch='discard', num_workers=num_workers)\n", " return train_iter, test_iter" ] }, { "cell_type": "markdown", "id": "9917647c", "metadata": { "origin_pos": 34 }, "source": [ "## Tóm tắt\n", "\n", "* Phân đoạn ngữ nghĩa nhận ra và hiểu những gì trong một hình ảnh ở mức pixel bằng cách chia hình ảnh thành các vùng thuộc các lớp ngữ nghĩa khác nhau.\n", "* Trên của tập dữ liệu phân khúc ngữ nghĩa quan trọng nhất là Pascal VOC2012.\n", "* Trong phân đoạn ngữ nghĩa, vì hình ảnh đầu vào và nhãn tương ứng một-một trên pixel, hình ảnh đầu vào được cắt ngẫu nhiên thành một hình dạng cố định chứ không phải rescaled.\n", "\n", "## Bài tập\n", "\n", "1. Làm thế nào phân khúc ngữ nghĩa có thể được áp dụng trong các phương tiện tự trị và chẩn đoán hình ảnh y tế? Bạn có thể nghĩ về các ứng dụng khác?\n", "1. Nhớ lại các mô tả về tăng cường dữ liệu trong :numref:`sec_image_augmentation`. Phương pháp nâng hình ảnh nào được sử dụng trong phân loại hình ảnh sẽ không khả thi được áp dụng trong phân khúc ngữ nghĩa?\n" ] }, { "cell_type": "markdown", "id": "6635544e", "metadata": { "origin_pos": 35, "tab": [ "mxnet" ] }, "source": [ "[Discussions](https://discuss.d2l.ai/t/375)\n" ] } ], "metadata": { "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 5 }