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from transformers import Trainer, TrainingArguments
from datasets import load_dataset
from transformers import ViTForImageClassification, ViTFeatureExtractor
# Carregar o dataset "beans"
dataset = load_dataset("beans")
# Carregar o modelo pré-treinado e definir o número de classes corretamente (3 classes para Beans)
model = ViTForImageClassification.from_pretrained(
"google/vit-base-patch16-224-in21k",
num_labels=3 # Beans tem 3 classes
)
feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
# Preprocessamento
def preprocess_function(examples):
inputs = feature_extractor(examples["image"], return_tensors="pt") # A chave correta no Beans é "image"
inputs["labels"] = examples["labels"] # Certifique-se de que o rótulo está correto
return inputs
# Aplicando o preprocessamento ao dataset
dataset = dataset.map(preprocess_function, batched=True)
# Definir os parâmetros de treinamento
training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=64,
num_train_epochs=3,
weight_decay=0.01,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset["train"],
eval_dataset=dataset["validation"], # No Beans, o conjunto de teste é chamado de "validation"
)
# Treinar o modelo
trainer.train()
# Salvar o modelo e o feature extractor treinados
model.save_pretrained("./computer-vision-beans")
feature_extractor.save_pretrained("./computer-vision-beans")
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