Beit Base Patch16 224 Pt22k Ft22k Finetuned FER2013 0.0001

🚀 beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-0.0001

This model is a fine - tuned variant of microsoft/beit-base-patch16-224-pt22k-ft22k on the image_folder dataset. It offers valuable performance in image classification tasks, achieving notable results on the evaluation set.

🚀 Quick Start

This model can be used for image classification tasks. You can load it using the Hugging Face Transformers library. Here is a simple example:

from transformers import AutoModelForImageClassification, AutoFeatureExtractor
import torch
from PIL import Image
import requests

# Load the model and feature extractor
model = AutoModelForImageClassification.from_pretrained('your_model_name')
feature_extractor = AutoFeatureExtractor.from_pretrained('your_model_name')

# Load an image
url = 'https://example.com/image.jpg'
image = Image.open(requests.get(url, stream=True).raw)

# Preprocess the image
inputs = feature_extractor(images=image, return_tensors="pt")

# Make a prediction
with torch.no_grad():
    logits = model(**inputs).logits

# Get the predicted class
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])

📚 Documentation

Model description

This model is a fine - tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the image_folder dataset. It achieves the following results on the evaluation set:

• Loss: 0.8601
• Accuracy: 0.6855

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 3

Training results

Framework versions

• Transformers 4.20.1
• Pytorch 1.11.0
• Datasets 2.1.0
• Tokenizers 0.12.1

📄 License

This model is released under the Apache 2.0 license.

📦 Model Information