diff --git a/applications/ConvNeXt/ConvNeXt.ipynb b/applications/ConvNeXt/ConvNeXt.ipynb
new file mode 100644
index 000000000..2a3b05f60
--- /dev/null
+++ b/applications/ConvNeXt/ConvNeXt.ipynb
@@ -0,0 +1,474 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "8bceff6e-9047-45cd-a30d-ee8a05ac316a",
+ "metadata": {},
+ "source": [
+ "### 环境配置\n",
+ "MindSpore 2.3\n",
+ "MindNLP 0.3.1\n",
+ "Python 3.9"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "2edff912-48fd-40ec-a79f-b37429c147a2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%capture captured_output\n",
+ "!/home/ma-user/anaconda3/bin/conda create -n python-3.9.0 python=3.9.0 -y --override-channels --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main\n",
+ "!/home/ma-user/anaconda3/envs/python-3.9.0/bin/pip install ipykernel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "138400a3-55c3-4f18-9b2d-030dc1871018",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "import os\n",
+ "\n",
+ "data = {\n",
+ " \"display_name\": \"python-3.9.0\",\n",
+ " \"env\": {\n",
+ " \"PATH\": \"/home/ma-user/anaconda3/envs/python-3.9.0/bin:/home/ma-user/anaconda3/envs/python-3.7.10/bin:/modelarts/authoring/notebook-conda/bin:/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/home/ma-user/modelarts/ma-cli/bin:/home/ma-user/modelarts/ma-cli/bin\"\n",
+ " },\n",
+ " \"language\": \"python\",\n",
+ " \"argv\": [\n",
+ " \"/home/ma-user/anaconda3/envs/python-3.9.0/bin/python\",\n",
+ " \"-m\",\n",
+ " \"ipykernel\",\n",
+ " \"-f\",\n",
+ " \"{connection_file}\"\n",
+ " ]\n",
+ "}\n",
+ "\n",
+ "if not os.path.exists(\"/home/ma-user/anaconda3/share/jupyter/kernels/python-3.9.0/\"):\n",
+ " os.mkdir(\"/home/ma-user/anaconda3/share/jupyter/kernels/python-3.9.0/\")\n",
+ "\n",
+ "with open('/home/ma-user/anaconda3/share/jupyter/kernels/python-3.9.0/kernel.json', 'w') as f:\n",
+ " json.dump(data, f, indent=4)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9c0b75ca-1563-4896-aa14-dd54c204a18a",
+ "metadata": {},
+ "source": [
+ "安装完成后重启kernel，选择python3.9"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "11bc67c7-a7be-4f2a-aae5-66ac77ef2816",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%capture captured_output\n",
+ "!pip uninstall mindspore-gpu -y\n",
+ "!pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.3/MindSpore/unified/x86_64/mindspore-2.3-cp39-cp39-linux_x86_64.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple\n",
+ "!pip install download nltk -i https://pypi.tuna.tsinghua.edu.cn/simple\n",
+ "!pip install mindnlp"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7e618a33-c44e-49ce-a1bc-adc21c1d802f",
+ "metadata": {},
+ "source": [
+ "### 加载数据\n",
+ "首先，加载图像数据集"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "4891b58b-8b8f-4a53-a7f7-43633da2c52f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Generating train split: 27000 examples [00:05, 4738.60 examples/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from datasets import load_dataset\n",
+ "# 数据来源：https://github.com/phelber/EuroSAT\n",
+ "dataset = load_dataset(\"imagefolder\", data_files=\"EuroSAT_RGB.zip\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "743c6d59-d2a3-4be6-b0c0-0bc5ef29204b",
+ "metadata": {},
+ "source": [
+ "图像分类任务数据通常包含两类数据：图像和标签"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "75544b36-3af7-40f1-b53e-2a0d2b4b4ba2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'image': Image(mode=None, decode=True, id=None),\n",
+ " 'label': ClassLabel(names=['AnnualCrop', 'Forest', 'HerbaceousVegetation', 'Highway', 'Industrial', 'Pasture', 'PermanentCrop', 'Residential', 'River', 'SeaLake'], id=None)}"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dataset[\"train\"].features"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "621ac53a-775f-4e00-a174-9ff3848c4d3a",
+ "metadata": {},
+ "source": [
+ "我们首先可视化一个示例"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "399fc20a-62a1-4935-90bf-8de847a97eb8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/jpeg": "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",
+ "image/png": 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AgNchkcboJ6Csgqw3mIt6r3sRl+MLQZjSVzbzVfI04HPz4cdJjVXpo4fxKw5y76JweoAoInhYAEnixHkqj7Yw6B18+SSkfYD4nTKdaXC77V3wogxgoENK4aIqPBJKQiqlxFVYqSKrmGFBABTrQqzx9xOC9NuDynOctxv39w0tpunScRKL2JIAS9wC7IGmWGHF4ogeThOsFoYKBNxYRyilJsVZFilpXH0CQP2m6CaYD45KHFv288Gpgmap4r5XDqwX/gdZze2iyxjokBEyEmKPKLCyXYm628NrtpqGk8hRMX3FDppS2rp1f4KHbLClznSeoiiMv90JwGCpfCmDVOOW/UXPusUnI91D87uEJ30QuZhUkpFDdU7V/aBHjXeWDODczT6pff32GVqHq4arCfhyjVicTks6niwtmAr1WHQAANK85RmVeASLeGfV5sDtZhbbaVgTVf29Pw8id4yEJ2J5hYqb1NsNUWCpf7E22OeGAsCIi4TIEELZ6+DSWmPbUhWtJBC0GqDdZa+O1jcHeMqyx09nBxI6YzECPQTcjIspd7djBYD+GJZ7SHbLuY1EWF0kUGiCI2SSkJHKQwkmJ+MjqTLll3oPL3hqWdy1V4nFcupJMe4iihyzvdlkwDDrLAGMBd0YEAZZmAhaAxUHAMTaST5w9ujnaeWjMIUgWQBoNWwnilpHRMbp4xeeftdDrPp+XKa0onnNtLIyoyhsOp0g0JcNjIxsW9meVwjVCJ1qbzsB897VFDoAhVKvuoIIDmumin6nkp/vseBSuDyrU9QYB6xxu4pW87bz7Y08cq4jGIvMvOnqEfQBLx0szxwk8JtN4sAzjHimKSYFkhhykK4VHcEZ0WkgAofOKgNVaCbYcuesRPLNWusfFCXDI4n7nC5hsmViF3flHJC1rMSANKKU3NUpftXvU+6rWVzkTK+CElHEZV/5WjqKIJB0mGXv6D5atfUeYERYsSuYgudZ7S0DuNWnQ2P2/dp++cMV/Ti0cwpkFEIegVQAOF9pZS06b7rYpBojZbmahToZXHQCIx1izXM22HiLcsM7P+NIFarvvOhMDjiuZDokgoALDsRM0c4vE4whZNbzYbryCXGxIIKMoAUXPZWDwXdjsEYAfreyHykrSmohMG861SkY6DBVRMqgovjXfV24d6cgO2VwXo1xYGqc3UPJ0L4+qWPTXRtEUZeHchL8CHNdXBSKLtyKEDaIgbCssS97JeF/fyCvB+nwSkTnLl5vCbh/XH9gCWCwYkmoVBlZ+Ud00pBLpOw6F6Z2dmWETOQxjSjGVLOJIyWgGVVg+EjiZR5FiSYFoPQv/zD+xhLzNV6dwuJJxMOL1YZxROdF+mavsuoN/+7GBtPiyzgN3PdISYhRqtwDzPlcYxG53oEhJUnpMRvpNSTeYBpHQMK1EXJdJSs6r6lKXQhPNlgvKyPwVZEkqSGAyYaJxZ9zfRqD1jCtWfaKJrJwKwWWaLGU1CJDrY9SNjMQ8PDL5KUtLuZlCUdt6MoT/Oq8bVAUmWfWQa52XjYLR2NU6wIR0EIPKSsjFwrXpX+WAez9txVvIsXojUWnwk9WL3l6PEK/VmKe2/2uDv5JDKJr0Y7A1Y1bKJAJIPxAsMehEY6HVLudtFYxrrSgaiqN3yzYrzDpmX/mb8H4C51cjDKCcIqV77ksyoyUN2IbaVf8lDzMara8cMrI3Q9/8OTFJH91lFOGhk0EqIM+Af/9jEq+VlMgl3QUoOtPAHSaPVtHB75HvB9U+FtjBWUEH/NdkhcWL0bY04WOGYJNoFpUMAodpmj0QYxsqKeh29+/3vdx5i947tsTgMvFj07uEI3ankBgApqMoxujZ/vEWS6kLaCIi8lRvHQGmy2R5kQ//3WXcvnnOm2gZT+LQfxiQ/KouVwYUFykRrdgmRHMkoR+x4QJ6NFErdpf//OFu9wN009tuS04gMtFpqEpXUR4NQbpVoC5/TXzFRfKxEiucSFIM5dJbtdk3QPTco8bAnzB1R8GyqmADFYUaGL+dDyAYpfogDm8AvNv2OTDGLYvGfRYuaV94a+mJr5iaECGyEAUZmsHM8jXvZyBBdGuiSQsQErFj5dDmMScRe2XtPEyswzmqXGdQ27YbefGlRHBUELwJIjiA0QXnjQnL1yhve6D41RfwDA/dIxrzabK7e5RDzcCYXRhTN0oufg0C0p9S8QGIBidCm6fEeL1NW6ND01Qa02UCMDQRcvqQ7VsiRB0aOyHkJhOKQo8/Vyw0HWBdyCmMqKMNdNUIUPzf9x++opka0AEAAAAAElFTkSuQmCC",
+ "text/plain": [
+ "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=64x64>"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "example = dataset[\"train\"][0]\n",
+ "example[\"image\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "b7a50755-6b85-419d-8674-8297008d3b36",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "example[\"label\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aaeb2ab2-6057-4505-87a8-51c5be7cb82b",
+ "metadata": {},
+ "source": [
+ "当前标签展示为整数，我们可以通过如下的方式将它们转化为真实的分类名"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "ce5c4469-6c26-42a4-a1b0-6d6ad3a9b5ab",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['AnnualCrop',\n",
+ " 'Forest',\n",
+ " 'HerbaceousVegetation',\n",
+ " 'Highway',\n",
+ " 'Industrial',\n",
+ " 'Pasture',\n",
+ " 'PermanentCrop',\n",
+ " 'Residential',\n",
+ " 'River',\n",
+ " 'SeaLake']"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "labels = dataset[\"train\"].features[\"label\"].names\n",
+ "labels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "18e83cad-da55-43ed-b6f5-11bf5210684f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{0: 'AnnualCrop',\n",
+ " 1: 'Forest',\n",
+ " 2: 'HerbaceousVegetation',\n",
+ " 3: 'Highway',\n",
+ " 4: 'Industrial',\n",
+ " 5: 'Pasture',\n",
+ " 6: 'PermanentCrop',\n",
+ " 7: 'Residential',\n",
+ " 8: 'River',\n",
+ " 9: 'SeaLake'}"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "id2label = {k:v for k,v in enumerate(labels)}\n",
+ "id2label"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aad7e9db-fed0-4e5c-8277-2c19b93a691b",
+ "metadata": {},
+ "source": [
+ "上图图像的标签为'AnnualCrop'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "8b9b91e3-1341-4727-ada4-ea521b53e140",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'AnnualCrop'"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "id2label[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b483c867-dd31-4f88-870a-ae1879ce7b80",
+ "metadata": {},
+ "source": [
+ "### 加载模型\n",
+ "从hub加载模型和处理器"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "34617c66-b73d-40d1-9521-bb5ffeb64324",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Building prefix dict from the default dictionary ...\n",
+ "Dumping model to file cache /tmp/jieba.cache\n",
+ "Loading model cost 0.613 seconds.\n",
+ "Prefix dict has been built successfully.\n",
+ "100%|██████████| 266/266 [00:00<00:00, 1.38MB/s]\n",
+ "1.04kB [00:00, 3.59MB/s] \n",
+ "100%|██████████| 106M/106M [03:11<00:00, 583kB/s] \n"
+ ]
+ }
+ ],
+ "source": [
+ "from mindnlp.transformers.models import ConvNextForImageClassification,ConvNextImageProcessor\n",
+ "img_processor = ConvNextImageProcessor.from_pretrained(\"nielsr/convnext-tiny-finetuned-eurosat\")\n",
+ "model = ConvNextForImageClassification.from_pretrained(\"nielsr/convnext-tiny-finetuned-eurosat\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ff23a9b4-d50f-4146-abad-7b4f56a4fe9a",
+ "metadata": {},
+ "source": [
+ "我们取前边的例子进行测试"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "e9193803-b77c-47af-b923-eb98bbb48e9c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "image/png": 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",
+ "text/plain": [
+ "<PIL.Image.Image image mode=RGB size=64x64>"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test_image = example[\"image\"].convert(\"RGB\")\n",
+ "test_image"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cfccd8f1-448a-4024-8441-14b3e61e570c",
+ "metadata": {},
+ "source": [
+ "### 图像预处理\n",
+ "使用处理器对测试图像进行预处理，将其转换为张量，并提取预处理后的像素值"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "63690316-692e-46ce-a1c9-c097a6d8cf2e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(1, 3, 224, 224)"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pixel_values = img_processor(test_image,return_tensors=\"ms\").pixel_values\n",
+ "pixel_values.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "17b830ff-2687-46ab-8519-c9b2fbeb21f7",
+ "metadata": {},
+ "source": [
+ "### 前向传播\n",
+ "将预处理后的像素值输入到模型中进行前向传播，提取模型输出的logits"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "16e514d2-ad87-4052-85b2-5327a198b268",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(1, 10)"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "outputs = model(pixel_values)\n",
+ "logits = outputs.logits\n",
+ "logits.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5cbf96ad-fc2b-4083-9ba1-938e32b2324e",
+ "metadata": {},
+ "source": [
+ "### 预测类别\n",
+ "根据logits得分，获取预测类别。"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "877d5752-5052-4ada-b6e4-eceaca3ce1b2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'AnnualCrop'"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "predicted_class_idx = logits.argmax(-1).item()\n",
+ "model.config.id2label[predicted_class_idx]"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "python-3.9.0",
+ "language": "python",
+ "name": "python-3.9.0"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}