README.md

metadata

base_model: microsoft/dit-base
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: dit-base-Classifier_CM05
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 1

dit-base-Classifier_CM05

This model is a fine-tuned version of microsoft/dit-base on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.0653
• Accuracy: 1.0
• Weighted f1: 1.0
• Micro f1: 1.0
• Macro f1: 1.0
• Weighted recall: 1.0
• Micro recall: 1.0
• Macro recall: 1.0
• Weighted precision: 1.0
• Micro precision: 1.0
• Macro precision: 1.0

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: 18

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
0.5553	1.0	1	2.7914	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.5553	2.0	2	2.4681	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.5553	3.0	3	1.8688	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.5553	4.0	4	1.3606	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.5553	5.0	5	0.9827	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.5553	6.0	6	0.7992	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.5553	7.0	7	0.5435	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.3458	8.0	8	0.3466	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.3458	9.0	9	0.2157	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.3458	10.0	10	0.1521	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.3458	11.0	11	0.1251	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.3458	12.0	12	0.1059	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.3458	13.0	13	0.0910	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.3458	14.0	14	0.0807	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.3458	15.0	15	0.0739	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.1206	16.0	16	0.0693	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.1206	17.0	17	0.0666	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.1206	18.0	18	0.0653	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.0+cu121
• Datasets 2.19.2
• Tokenizers 0.19.1