深度学习区分不同种类的图片_google/vit-base-patch16-224-in21k

数据集格式

之前利用resnet从一开始训练，效果比较差，后来利用谷歌的模型进行微调达到了很好的效果

训练代码如下：

from datasets import load_dataset
# /home/huhao/TensorFlow2.0_ResNet/dataset
# /home/huhao/dataset
import numpy as np
from datasets import load_metric
scene = load_dataset("/home/huhao/TensorFlow2.0_ResNet/dataset")
dataset = scene['train']
scene = dataset.train_test_split(test_size=0.2)
 
labels = scene["train"].features["label"].names
label2id, id2label = dict(), dict()
for i, label in enumerate(labels):
    label2id[label] = str(i)
    id2label[str(i)] = label
 
from transformers import AutoFeatureExtractor
# google/vit-base-patch16-224-in21k
feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
 
from torchvision.transforms import RandomResizedCrop, Compose, Normalize, ToTensor
 
normalize = Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
_transforms = Compose([RandomResizedCrop(feature_extractor.size), ToTensor(), normalize])
 
def transforms(examples):
    examples["pixel_values"] = [_transforms(img.convert("RGB")) for img in examples["image"]]
    del examples["image"]
    return examples
 
 
 
scene = scene.with_transform(transforms)
 
 
from transformers import DefaultDataCollator
 
data_collator = DefaultDataCollator()
 
from transformers import AutoModelForImageClassification, TrainingArguments, Trainer
 
 
def compute_metric(eval_pred):
    metric = load_metric("accuracy")
    logits,labels = eval_pred
    print(logits,labels)
    print(len(logits),len(labels))
    predictions = np.argmax(logits,axis=-1)
    print(len(predictions))
    print('predictions')
    print(predictions)
    return metric.compute(predictions = predictions,references = labels)
 
 
model = AutoModelForImageClassification.from_pretrained(
    "google/vit-base-patch16-224-in21k",
    num_labels=len(labels),
    id2label=id2label,
    label2id=label2id,
)
 
 
training_args = TrainingArguments(
    output_dir="./results",
    overwrite_output_dir = 'True',
    per_device_train_batch_size=16,
    evaluation_strategy="steps",
    num_train_epochs=4,
    save_steps=100,
    eval_steps=100,
    logging_steps=10,
    learning_rate=2e-4,
    save_total_limit=2,
    remove_unused_columns=False,
    load_best_model_at_end=False,
    save_strategy='no',
)
 
trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=scene["train"],
    eval_dataset=scene["test"],
    tokenizer=feature_extractor,
    compute_metrics=compute_metric,
 
)
 
trainer.train()
trainer.evaluate()
trainer.save_model('/home/huhao/script/model')

测试代码如下

from transformers import AutoFeatureExtractor, AutoModelForImageClassification
 
extractor = AutoFeatureExtractor.from_pretrained("HaoHu/vit-base-patch16-224-in21k-classify-4scence")
model = AutoModelForImageClassification.from_pretrained("HaoHu/vit-base-patch16-224-in21k-classify-4scence")
# 我已经把训练好的模型上传到网上，这里下载即可使用
from datasets import load_dataset
# /home/huhao/TensorFlow2.0_ResNet/dataset
# /home/huhao/dataset
import numpy as np
from datasets import load_metric
# 这个是数据集加载的路径
scene = load_dataset("/home/huhao/script/dataset")
dataset = scene['train']
scene = dataset.train_test_split(test_size=0.2)
 
labels = scene["train"].features["label"].names
label2id, id2label = dict(), dict()
for i, label in enumerate(labels):
    label2id[label] = str(i)
    id2label[str(i)] = label
 
from transformers import AutoFeatureExtractor
# google/vit-base-patch16-224-in21k
feature_extractor = AutoFeatureExtractor.from_pretrained("HaoHu/vit-base-patch16-224-in21k-classify-4scence")
 
from torchvision.transforms import RandomResizedCrop, Compose, Normalize, ToTensor
 
normalize = Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
_transforms = Compose([RandomResizedCrop(feature_extractor.size), ToTensor(), normalize])
 
def transforms(examples):
    examples["pixel_values"] = [_transforms(img.convert("RGB")) for img in examples["image"]]
    del examples["image"]
    return examples
 
 
 
scene = scene.with_transform(transforms)
from transformers import DefaultDataCollator
data_collator = DefaultDataCollator()
from transformers import AutoModelForImageClassification, TrainingArguments, Trainer
 
 
 
training_args = TrainingArguments(
    output_dir="./results",
    overwrite_output_dir = 'True',
    per_device_train_batch_size=16,
    evaluation_strategy="steps",
    num_train_epochs=4,
    save_steps=100,
    eval_steps=100,
    logging_steps=10,
    learning_rate=2e-4,
    save_total_limit=2,
    remove_unused_columns=False,
    load_best_model_at_end=False,
    save_strategy='no',
)
 
model = AutoModelForImageClassification.from_pretrained(
    "HaoHu/vit-base-patch16-224-in21k-classify-4scence",
    num_labels=len(labels),
    id2label=id2label,
    label2id=label2id,
)
 
 
 
def compute_metric(eval_pred):
    metric = load_metric("f1")
    logits,labels = eval_pred
    print(len(logits),len(labels))
    predictions = np.argmax(logits,axis=-1)
    print('对测试集进行评估')
    print('labels')
    print(labels)
    print('predictions')
    print(predictions)
    
    return metric.compute(predictions = predictions,references = labels,average='macro')
 
trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    eval_dataset=scene["test"],
    tokenizer=feature_extractor,
    compute_metrics=compute_metric,
 
)
compute_metrics = trainer.evaluate()
# {'eval_loss': 0.04495017230510712, 'eval_accuracy': 0.9943181818181818, 'eval_runtime': 30.8715, 'eval_samples_per_second': 11.402, 'eval_steps_per_second': 1.425}
print('输出最后的结果eval_f1：')
print(compute_metrics['eval_f1'])
from doctest import Example
from transformers import AutoFeatureExtractor, AutoModelForImageClassification, ImageClassificationPipeline
import os
extractor = AutoFeatureExtractor.from_pretrained("HaoHu/vit-base-patch16-224-in21k-classify-4scence")
model = AutoModelForImageClassification.from_pretrained("HaoHu/vit-base-patch16-224-in21k-classify-4scence")
from transformers import pipeline
 
#generator = ImageClassificationPipeline(model=model, tokenizer=extractor)
vision_classifier = pipeline(task="image-classification",model = model,feature_extractor = extractor)
result_dict = {'City_road':0,'fog':1,'rain':2,'snow':3}
val_path = '/home/huhao/script/val/' 
all_img = os.listdir(val_path)
for img in all_img:
    tmp_score = 0
    end_label = ''
    img_path = os.path.join(val_path,img)
    score_list = vision_classifier(img_path)
    for sample in score_list:
        score = sample['score']
        label = sample['label']
        if tmp_score < score:
            tmp_score = score
            end_label = label
    print(result_dict[end_label])