chihiro-classifier

<!-- This model card was customized based on training logs and evaluation metrics. -->

chihiro-classifier-vit

This model is a fine-tuned version of google/vit-base-patch16-224 trained on a small, custom binary classification dataset consisting of images labeled either "chihiro" or "not chihiro" (from Studio Ghibli films).

It was trained using PyTorch with transfer learning on a dataset of approximately 148 images.

Model description

The model classifies images into one of two categories: Chihiro or Not Chihiro. It uses a Vision Transformer (ViT) backbone with a custom classification head for binary output. Data augmentation was used during training to improve generalization. Techniques included random horizontal flip, rotation (30°), color jitter, and random resized crop.

Intended uses & limitations

Intended Uses:

• Student computer vision project

Limitations:

• Small dataset may limit real-world performance
• Not robust to domain shift or artistic variation
• Not intended for production deployment

Training and evaluation data

• Custom image dataset of Chihiro vs. non-Chihiro characters
• Loaded using Hugging Face's imagefolder format
• Split: 80% train, 10% validation, 10% test
• Augmentation applied during training; deterministic preprocessing during eval

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
• optimizer: Adam
• num_epochs: 12

Training results

Final Test Evaluation

• Test Loss: 0.3677
• Test Accuracy: 0.7333

🧪 Zero-Shot CLIP Comparison

Evaluated using openai/clip-vit-base-patch32 with no fine-tuning:

• Zero-shot Accuracy: 86.67%
• Precision: 0.8909
• Recall: 0.8667

Framework versions

• Transformers: not used (custom PyTorch)
• PyTorch: 2.x
• Datasets: 2.x
• Tokenizers: N/A