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-OT-alt
    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: 0.9032258064516129

beit-base-patch16-224-OT-alt

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: 0.4274
Accuracy: 0.9032

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	0.91	5	1.7093	0.1774
1.7744	2.0	11	1.6178	0.1774
1.7744	2.91	16	1.4730	0.1774
1.5823	4.0	22	1.2754	0.1774
1.5823	4.91	27	1.1455	0.5645
1.27	6.0	33	1.0147	0.6290
1.27	6.91	38	0.9790	0.5484
1.079	8.0	44	1.0474	0.4516
1.079	8.91	49	0.8796	0.7581
1.005	10.0	55	0.7759	0.7742
0.8479	10.91	60	0.7421	0.8226
0.8479	12.0	66	0.6760	0.8548
0.7695	12.91	71	0.5933	0.8387
0.7695	14.0	77	0.6372	0.7742
0.6591	14.91	82	0.5653	0.8387
0.6591	16.0	88	0.4950	0.8710
0.5675	16.91	93	0.5040	0.8226
0.5675	18.0	99	0.4274	0.9032
0.5134	18.91	104	0.4617	0.8548
0.4418	20.0	110	0.4245	0.8871
0.4418	20.91	115	0.4922	0.8387
0.402	22.0	121	0.5112	0.8226
0.402	22.91	126	0.4696	0.8548
0.4039	24.0	132	0.4014	0.8710
0.4039	24.91	137	0.5006	0.8226
0.4216	26.0	143	0.5351	0.8548
0.4216	26.91	148	0.5203	0.8548
0.3593	28.0	154	0.4082	0.8548
0.3593	28.91	159	0.4017	0.8710
0.3638	30.0	165	0.4068	0.8871
0.3509	30.91	170	0.3991	0.8871
0.3509	32.0	176	0.3965	0.8710
0.3426	32.91	181	0.3921	0.8710
0.3426	34.0	187	0.3998	0.8710
0.3253	34.91	192	0.4102	0.8871
0.3253	36.0	198	0.4081	0.8871
0.3085	36.36	200	0.4083	0.8871

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu118
Datasets 2.16.1
Tokenizers 0.15.0