微调计算机视觉模型

使用 Hugging Face 的多模态模型

James Chapman

Curriculum Manager, DataCamp

微调视觉模型的目的

新类别，如真实与AI生成图像的二分类
新成像领域，如X光

用于预训练的通用海豹图像

AI生成图像示例

¹ https://image-net.org/index.php

微调视觉模型

AI生成图像示例

将模型输出调整为新预测
为训练准备数据集
配置训练选项
开始训练！

模型更新

from datasets import load_dataset
dataset = load_dataset("ideepankarsharma2003/Midjourney_v6_Classification_small_shuf
fled")['train']

data_splits = dataset.train_test_split(test_size=0.2, seed=42)


labels = data_splits["train"].features["label"].names

label2id, id2label = dict(), dict()
for i, label in enumerate(labels):
    label2id[label] = str(i)
    id2label[str(i)] = label

模型更新

from transformers import AutoModelForImageClassification
checkpoint = "google/mobilenet_v2_1.0_224"
model = AutoModelForImageClassification.from_pretrained(
    checkpoint,
    num_labels=len(labels),

    id2label=id2label, 
    label2id=label2id,

    ignore_mismatched_sizes=True

)

数据集准备

from transformers import AutoImageProcessor
image_processor = AutoImageProcessor.from_pretrained(checkpoint)


from torchvision.transforms import Compose, Normalize, ToTensor

normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)

transform = Compose([ToTensor(), normalize])


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


dataset = dataset.with_transform(transforms)

绘制变换后的数据

import matplotlib.pyplot as plt
plt.imshow(dataset["train"][0]["pixel_values"].permute(1, 2, 0))
plt.show()

来自新数据集的变换后图像

训练

from transformers import TrainingArguments

training_args = TrainingArguments(
    output_dir="dataset_finetune",

    learning_rate=6e-5,

    gradient_accumulation_steps=4,

    num_train_epochs=3,

    push_to_hub=False
)

from transformers import Trainer,
    DefaultDataCollator

data_collator = DefaultDataCollator()

trainer = Trainer(

    model=model,

    args=training_args,

    train_dataset=dataset["train"],
    eval_dataset=dataset["test"],

    processing_class=image_processor,

    data_collator=data_collator

)

评估

predictions = trainer.predict(dataset["test"])
predictions.metrics["test_accuracy"]

0.455

trainer.train()

{..., 'eval_accuracy': 0.93, ...}

¡Vamos a practicar!

使用 Hugging Face 的多模态模型