用 Transformers 微调图像分类模型-未完纪

图像分类为图片分配一个标签或类别。与文本或音频分类不同，它的输入是构成图片的像素值。应用包括自然灾害后的损害识别、作物健康监测，以及辅助检查医学影像中的疾病迹象。

本指南介绍：

在 Food-101 数据集上微调 ViT，识别图片中的食物。
使用微调后的模型进行推理。

要了解兼容此任务的全部模型架构和检查点，可以查看图像分类任务页。

开始之前，需要安装以下库：

pip install transformers datasets evaluate accelerate pillow torchvision scikit-learn trackio

如果希望上传模型并与社区分享，可以登录自己的 Hugging Face 账户。在登录组件提示时输入访问令牌：

>>> from huggingface_hub import notebook_login

>>> notebook_login()

加载 Food-101 数据集

先用 Datasets 加载 Food-101 的一个较小子集。这样可以先验证流程，再投入更多时间训练完整数据集。

>>> from datasets import load_dataset

>>> food = load_dataset("ethz/food101", split="train[:5000]")

用 train_test_split 把原始 train 划分成训练集和测试集：

>>> food = food.train_test_split(test_size=0.2)

查看一个样本。以下对象地址和标签属于原文示例输出：

>>> food["train"][0]
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=512x512 at 0x7F52AFC8AC50>,
 'label': 79}

每个样本有两个字段：

image：食物图片的 PIL 图像对象。
label：食物所属类别的标签。

为了能在标签编号和标签名称之间转换，建立双向映射字典。这里代码把编号保存为字符串键或值：

>>> labels = food["train"].features["label"].names
>>> label2id, id2label = dict(), dict()
>>> for i, label in enumerate(labels):
...     label2id[label] = str(i)
...     id2label[str(i)] = label

现在可以把标签编号转换为名称：

>>> id2label[str(79)]
'prime_rib'

预处理

加载 ViT 图像处理器，把图片处理成张量：

>>> from transformers import AutoImageProcessor

>>> checkpoint = "google/vit-base-patch16-224-in21k"
>>> image_processor = AutoImageProcessor.from_pretrained(checkpoint)

使用图像变换，降低模型过拟合的风险。这里采用 torchvision 的 transforms，也可以使用其他图像库。

随机裁剪图片的一部分、调整尺寸，再根据图像均值和标准差进行归一化：

>>> from torchvision.transforms import RandomResizedCrop, Compose, Normalize, ToTensor

>>> normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
>>> size = (
...     image_processor.size["shortest_edge"]
...     if "shortest_edge" in image_processor.size
...     else (image_processor.size["height"], image_processor.size["width"])
... )
>>> _transforms = Compose([RandomResizedCrop(size), ToTensor(), normalize])

定义预处理函数，应用这些变换，返回模型输入 pixel_values：

>>> def transforms(examples):
...     examples["pixel_values"] = [_transforms(img.convert("RGB")) for img in examples["image"]]
...     del examples["image"]
...     return examples

用 Datasets 的 with_transform 为整个数据集设置预处理。每次读取样本时，变换会即时执行：

>>> food = food.with_transform(transforms)

使用 DefaultDataCollator 把样本组成批次。与其他某些 Transformers 数据整理器不同，它不会额外执行填充等预处理。

>>> from transformers import DefaultDataCollator

>>> data_collator = DefaultDataCollator()

评估

训练中加入评估指标有助于衡量模型表现。用 Evaluate 库加载本任务所需的 accuracy 指标。关于指标的加载与计算，可参考 Evaluate 入门指南：

>>> import evaluate

>>> accuracy = evaluate.load("accuracy")

定义函数，把预测结果与真实标签传给 compute，计算准确率：

>>> import numpy as np


>>> def compute_metrics(eval_pred):
...     predictions, labels = eval_pred
...     predictions = np.argmax(predictions, axis=1)
...     return accuracy.compute(predictions=predictions, references=labels)

compute_metrics 已准备好，设置训练器时会用到它。

训练

如果不熟悉使用 Trainer 微调模型，可以先阅读基础训练教程。

用 AutoModelForImageClassification 加载 ViT，并指定类别数量和双向标签映射：

>>> from transformers import AutoModelForImageClassification, TrainingArguments, Trainer

>>> model = AutoModelForImageClassification.from_pretrained(
...     checkpoint,
...     num_labels=len(labels),
...     id2label=id2label,
...     label2id=label2id,
... )

接下来还有三步：

在 TrainingArguments 中定义训练超参数。必须保留未直接使用的列，否则 image 会被删除，预处理就无法生成 pixel_values。设置 remove_unused_columns=False 可以避免这一问题。另一个必需参数是 output_dir，用于指定模型保存目录。原文示例设置 push_to_hub=True，会把模型上传到 Hub，因此需要预先登录。每轮训练结束后，Trainer 会评估准确率并保存检查点。
把训练参数、模型、数据集、处理组件、数据整理器和 compute_metrics 传给 Trainer。图像任务在下面的代码中通过 processing_class=image_processor 提供处理组件。
调用 train() 微调模型。

>>> training_args = TrainingArguments(
...     output_dir="my_awesome_food_model",
...     remove_unused_columns=False,
...     eval_strategy="epoch",
...     save_strategy="epoch",
...     learning_rate=5e-5,
...     per_device_train_batch_size=16,
...     gradient_accumulation_steps=4,
...     per_device_eval_batch_size=16,
...     num_train_epochs=3,
...     warmup_steps=0.1,
...     logging_steps=10,
...     report_to="trackio",
...     run_name="food101",
...     load_best_model_at_end=True,
...     metric_for_best_model="accuracy",
...     push_to_hub=True,
... )

>>> trainer = Trainer(
...     model=model,
...     args=training_args,
...     data_collator=data_collator,
...     train_dataset=food["train"],
...     eval_dataset=food["test"],
...     processing_class=image_processor,
...     compute_metrics=compute_metrics,
... )

>>> trainer.train()

训练完成后，可以通过 push_to_hub() 分享模型，供其他人使用：

>>> trainer.push_to_hub()

更详细的图像分类微调案例见对应的 PyTorch notebook。

推理

完成微调后，就可以用模型进行推理。

加载要识别的图片：

>>> ds = load_dataset("ethz/food101", split="validation[:10]")
>>> image = ds["image"][0]

原文用于推理的贝涅饼图片（仅作来源参考，图片独立许可待确认，交付稿不嵌入。）

最方便的方式是使用 pipeline：用自己的模型创建图像分类流水线，再传入图片。下面的分数与类别是原文示例结果，并非本机训练或推理结果：

>>> from transformers import pipeline

>>> classifier = pipeline("image-classification", model="my_awesome_food_model")
>>> classifier(image)
[{'score': 0.31856709718704224, 'label': 'beignets'},
 {'score': 0.015232225880026817, 'label': 'bruschetta'},
 {'score': 0.01519392803311348, 'label': 'chicken_wings'},
 {'score': 0.013022331520915031, 'label': 'pork_chop'},
 {'score': 0.012728818692266941, 'label': 'prime_rib'}]

也可以手动重现流水线执行的步骤：

加载图像处理器，预处理图片，并以 PyTorch 张量形式返回输入：

>>> from transformers import AutoImageProcessor
>>> import torch

>>> image_processor = AutoImageProcessor.from_pretrained("my_awesome_food_model")
>>> inputs = image_processor(image, return_tensors="pt")

把输入传给模型，取得 logits：

>>> from transformers import AutoModelForImageClassification

>>> model = AutoModelForImageClassification.from_pretrained("my_awesome_food_model")
>>> with torch.no_grad():
...     logits = model(**inputs).logits

选择概率最大的预测类别，再通过模型的 id2label 映射取得标签名称：

>>> predicted_label = logits.argmax(-1).item()
>>> model.config.id2label[predicted_label]
'beignets'

复现时的两个细节

上面的原样代码把 with_transform 应用于整个训练与验证数据集合，因此验证时也使用随机裁剪。若要稳定比较检查点，可为验证集另设确定性的预处理。

当前官方 TrainingArguments 支持把 warmup_steps 写为 [0, 1) 内的小数比例，所以示例中的 0.1 表示总训练步数的 10%。旧版 API 的行为应单独核对。Hub 上传是可选操作；若只在本地训练，应相应调整上传设置。

来源与许可

原文：Image classification。作者或贡献者：Hugging Face Transformers 文档贡献者。本中文版本为翻译；代码、注释和原文示例输出保留原样，必要的技术澄清已在相应段落说明。适用许可：Apache-2.0。

Copyright 2022 The HuggingFace Team. All rights reserved.

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