README.md

metadata

tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: ditmodel
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: train
          split: train
          args: train
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9425649095200629

ditmodel

This model was trained from scratch on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.1359
• Accuracy: 0.9426
• Weighted f1: 0.9426
• Micro f1: 0.9426
• Macro f1: 0.9386
• Weighted recall: 0.9426
• Micro recall: 0.9426
• Macro recall: 0.9404
• Weighted precision: 0.9440
• Micro precision: 0.9426
• Macro precision: 0.9382

Model description

More information needed

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: 5e-05
• 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

Training Loss	Epoch	Step	Validation Loss	Accuracy	Weighted f1	Micro f1	Macro f1	Weighted recall	Micro recall	Macro recall	Weighted precision	Micro precision	Macro precision
0.3093	0.98	38	0.2252	0.8891	0.8879	0.8891	0.8820	0.8891	0.8891	0.8738	0.8952	0.8891	0.8994
0.2278	1.99	77	0.1648	0.9292	0.9292	0.9292	0.9220	0.9292	0.9292	0.9221	0.9310	0.9292	0.9241
0.2066	2.94	114	0.1359	0.9426	0.9426	0.9426	0.9386	0.9426	0.9426	0.9404	0.9440	0.9426	0.9382

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.6.1
• Tokenizers 0.15.1