metadata

license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: beit-base-patch16-224-DMAE-5e-5
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8043478260869565

beit-base-patch16-224-DMAE-5e-5

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

Loss: 0.6236
Accuracy: 0.8043

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: 40

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.86	3	1.3816	0.4565
No log	2.0	7	1.3504	0.4565
1.3654	2.86	10	1.3075	0.4565
1.3654	4.0	14	1.2444	0.4565
1.3654	4.86	17	1.2089	0.4565
1.2386	6.0	21	1.1711	0.4565
1.2386	6.86	24	1.1492	0.4565
1.2386	8.0	28	1.1050	0.4783
1.132	8.86	31	1.0928	0.6522
1.132	10.0	35	1.0230	0.6739
1.132	10.86	38	0.9864	0.6522
1.015	12.0	42	1.0060	0.6739
1.015	12.86	45	0.8790	0.7174
1.015	14.0	49	0.8533	0.6739
0.9217	14.86	52	0.8203	0.6957
0.9217	16.0	56	0.8152	0.6739
0.9217	16.86	59	0.8598	0.6522
0.7989	18.0	63	0.7979	0.6957
0.7989	18.86	66	0.7555	0.6739
0.7309	20.0	70	0.7019	0.7609
0.7309	20.86	73	0.7254	0.7174
0.7309	22.0	77	0.7268	0.6739
0.6267	22.86	80	0.7096	0.6739
0.6267	24.0	84	0.7568	0.6739
0.6267	24.86	87	0.6565	0.7391
0.564	26.0	91	0.7035	0.7391
0.564	26.86	94	0.7081	0.7391
0.564	28.0	98	0.6236	0.8043
0.5625	28.86	101	0.6091	0.8043
0.5625	30.0	105	0.6523	0.7609
0.5625	30.86	108	0.6235	0.7826
0.5185	32.0	112	0.5974	0.7826
0.5185	32.86	115	0.5944	0.7826
0.5185	34.0	119	0.6005	0.7826
0.5005	34.29	120	0.6005	0.7826

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu118
Datasets 2.16.1
Tokenizers 0.15.0