metadata

license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_10x_beit_large_adamax_00001_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: 0.93

smids_10x_beit_large_adamax_00001_fold3

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

Loss: 0.7342
Accuracy: 0.93

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: 1e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
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
0.1234	1.0	750	0.2380	0.9133
0.0658	2.0	1500	0.2732	0.9317
0.0204	3.0	2250	0.3498	0.9217
0.0213	4.0	3000	0.4104	0.925
0.0054	5.0	3750	0.4509	0.9317
0.0002	6.0	4500	0.5343	0.9233
0.0104	7.0	5250	0.5450	0.9267
0.0001	8.0	6000	0.6214	0.9217
0.0002	9.0	6750	0.5669	0.9333
0.0	10.0	7500	0.5842	0.9233
0.0003	11.0	8250	0.5405	0.9267
0.0007	12.0	9000	0.6365	0.9233
0.0	13.0	9750	0.6437	0.9267
0.0006	14.0	10500	0.6868	0.92
0.0	15.0	11250	0.6484	0.93
0.0	16.0	12000	0.6945	0.925
0.0	17.0	12750	0.6473	0.925
0.0	18.0	13500	0.7329	0.9233
0.0	19.0	14250	0.6697	0.9283
0.0	20.0	15000	0.7054	0.9317
0.0	21.0	15750	0.7229	0.9267
0.0001	22.0	16500	0.6657	0.9267
0.0	23.0	17250	0.6845	0.925
0.0	24.0	18000	0.7071	0.9233
0.0	25.0	18750	0.7119	0.9267
0.0	26.0	19500	0.7250	0.9283
0.0	27.0	20250	0.7491	0.93
0.0	28.0	21000	0.7325	0.9267
0.0	29.0	21750	0.7225	0.93
0.0	30.0	22500	0.7702	0.93
0.0	31.0	23250	0.7702	0.93
0.0	32.0	24000	0.7279	0.93
0.0	33.0	24750	0.7215	0.9283
0.0	34.0	25500	0.7215	0.9267
0.0	35.0	26250	0.7456	0.9267
0.0	36.0	27000	0.7430	0.9267
0.0	37.0	27750	0.7363	0.9283
0.0	38.0	28500	0.7489	0.93
0.0	39.0	29250	0.7854	0.9267
0.0	40.0	30000	0.7378	0.9283
0.0	41.0	30750	0.7334	0.93
0.0	42.0	31500	0.7235	0.9333
0.0	43.0	32250	0.7203	0.93
0.0	44.0	33000	0.7319	0.9267
0.0	45.0	33750	0.7326	0.93
0.0	46.0	34500	0.7443	0.93
0.0	47.0	35250	0.7511	0.93
0.0	48.0	36000	0.7575	0.93
0.0	49.0	36750	0.7357	0.93
0.0	50.0	37500	0.7342	0.93

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2