Vit Mushroom Classifier

Classes

Amanita_muscaria
Amanita_phalloides
Armillaria_mellea
Cerioporus_squamosus
Chlorophyllum_brunneum
Clitocybe_nuda
Coprinellus_micaceus
Coprinus_comatus
Flammulina_velutipes
Gliophorus_psittacinus
Hygrophoropsis_aurantiaca
Hypholoma_lateritium
Stereum_hirsutum
Suillus_luteus
Tricholomopsis_rutilans
Tylopilus_felleus

Dataset

The image dataset combines a Kaggle mushroom image dataset with additional GBIF image records for the selected species.

Final image scope:

• Total images: 11,200
• Classes: 16
• Images per class: 700
• Train images: 7,839
• Validation images: 1,680
• Test images: 1,681
• Split: stratified 70% / 15% / 15%

Training Procedure

The original ImageNet classifier head was replaced with a new 16-class head. Training used two phases:

1. Train the new classifier head.
2. Fine-tune the last 4 ViT encoder blocks.

Main configuration:

• Batch size: 16
• Head training epochs: 1
• Fine-tuning epochs: 4
• Head learning rate: 3e-4
• Fine-tuning learning rate: 2e-5
• Weight decay: 0.01
• Random crop scale: 0.85-1.0
• Color jitter: 0.1
• Best model metric: macro F1
• Early stopping: enabled, patience 2

Validation improved during fine-tuning:

Test Results

Final test set results:

Bootstrap 95% confidence intervals:

Lowest per-class F1 scores:

Common confusion patterns included:

• Suillus_luteus predicted as Tylopilus_felleus
• Coprinus_comatus predicted as Chlorophyllum_brunneum
• Several confusions involving Flammulina_velutipes, Gliophorus_psittacinus, and Hygrophoropsis_aurantiaca

Known Limitations

• The model only predicts among the 16 trained species.
• It does not know whether an image belongs to an unseen species.
• It may make confident mistakes on visually similar species.
• Some exact duplicate image hashes were found across splits: 55 duplicate rows in 27 hash groups, with 19 groups crossing train/validation/test splits. This may slightly overestimate test performance.
• The dataset combines images from different sources, so source-specific visual patterns may influence predictions.
• The model must not be used for mushroom consumption, toxicity, or foraging decisions.

Intended Use

Suitable uses:

• Educational image classification demos
• Candidate species ranking among the 16 known classes
• Comparison against structured ecological/context models
• Prototype user interfaces for mushroom image classification

Unsuitable uses:

• Real-world mushroom identification without expert review
• Edibility or toxicity classification
• Safety-critical decisions
• Open-world species recognition beyond the 16 labels

Summary

The final ViT model achieved strong and stable test performance: about 91.6% macro F1 and 97.8% top-3 accuracy on a balanced 16-class test set. The result is promising for educational candidate prediction, but a deduplicated re-split and re-evaluation would be recommended before treating the score as final.