metadata

license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_1x_deit_tiny_adamax_lr0001_fold3
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.6976744186046512

hushem_1x_deit_tiny_adamax_lr0001_fold3

This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.8641
Accuracy: 0.6977

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: 0.0001
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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.67	1	1.6041	0.2558
No log	2.0	3	1.2890	0.3953
No log	2.67	4	1.2944	0.3023
No log	4.0	6	1.2013	0.4186
No log	4.67	7	1.1135	0.4186
No log	6.0	9	1.0796	0.5349
1.2559	6.67	10	1.0570	0.5581
1.2559	8.0	12	1.1038	0.4884
1.2559	8.67	13	1.0764	0.4884
1.2559	10.0	15	0.9749	0.5349
1.2559	10.67	16	0.9354	0.5581
1.2559	12.0	18	0.9274	0.6279
1.2559	12.67	19	0.9435	0.6512
0.4315	14.0	21	0.9225	0.6512
0.4315	14.67	22	0.9168	0.6279
0.4315	16.0	24	0.8830	0.6279
0.4315	16.67	25	0.8956	0.6512
0.4315	18.0	27	0.9038	0.6744
0.4315	18.67	28	0.8913	0.6744
0.058	20.0	30	0.8683	0.6512
0.058	20.67	31	0.8553	0.6744
0.058	22.0	33	0.8508	0.6977
0.058	22.67	34	0.8546	0.6977
0.058	24.0	36	0.8627	0.6977
0.058	24.67	37	0.8639	0.6977
0.058	26.0	39	0.8636	0.7209
0.0086	26.67	40	0.8627	0.7209
0.0086	28.0	42	0.8622	0.7209
0.0086	28.67	43	0.8622	0.6977
0.0086	30.0	45	0.8629	0.6977
0.0086	30.67	46	0.8632	0.6977
0.0086	32.0	48	0.8638	0.6977
0.0086	32.67	49	0.8640	0.6977
0.004	33.33	50	0.8641	0.6977

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1