metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: computer_parts_classifier-model
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train[:722]
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8137931034482758

computer_parts_classifier-model

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.5117
Accuracy: 0.8138

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: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.97	9	1.0525	0.5517
1.0645	1.95	18	0.9405	0.6
0.9405	2.92	27	0.7902	0.7034
0.7669	4.0	37	0.6923	0.7379
0.6008	4.97	46	0.6152	0.7862
0.5142	5.95	55	0.5639	0.7931
0.394	6.92	64	0.5640	0.8
0.3649	8.0	74	0.5181	0.7862
0.279	8.97	83	0.5094	0.8345
0.2549	9.95	92	0.4882	0.8276
0.1925	10.92	101	0.5041	0.8
0.2185	12.0	111	0.5195	0.8138
0.1921	12.97	120	0.5170	0.8
0.1921	13.95	129	0.5846	0.7793
0.15	14.92	138	0.5217	0.8207
0.1798	16.0	148	0.5421	0.7862
0.1729	16.97	157	0.5516	0.8207
0.1459	17.95	166	0.5438	0.7931
0.1701	18.92	175	0.5043	0.8345
0.1487	19.46	180	0.5117	0.8138

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2