README.md

metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: Chess_Images
    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: 0.9333333333333333

Chess_Images

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: 0.2460
• Accuracy: 0.9333

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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	2	0.3365	0.9333
No log	2.0	4	0.3018	0.9333
No log	3.0	6	0.3443	0.9667
No log	4.0	8	0.2189	1.0
0.213	5.0	10	0.3188	0.9667
0.213	6.0	12	0.2903	0.9333
0.213	7.0	14	0.3398	0.9
0.213	8.0	16	0.3879	0.8667
0.213	9.0	18	0.3023	0.9333
0.2116	10.0	20	0.1857	1.0
0.2116	11.0	22	0.2737	0.9667
0.2116	12.0	24	0.2675	1.0
0.2116	13.0	26	0.2817	0.9333
0.2116	14.0	28	0.4394	0.8667
0.1837	15.0	30	0.3167	0.9
0.1837	16.0	32	0.2795	0.9333
0.1837	17.0	34	0.2315	0.9333
0.1837	18.0	36	0.2266	0.9667
0.1837	19.0	38	0.3199	0.9333
0.1726	20.0	40	0.2553	0.9667
0.1726	21.0	42	0.3804	0.9
0.1726	22.0	44	0.2118	0.9667
0.1726	23.0	46	0.1784	1.0
0.1726	24.0	48	0.2098	0.9667
0.1529	25.0	50	0.1676	1.0
0.1529	26.0	52	0.2980	0.9
0.1529	27.0	54	0.2726	0.9667
0.1529	28.0	56	0.1756	1.0
0.1529	29.0	58	0.2266	0.9667
0.1335	30.0	60	0.3161	0.9333
0.1335	31.0	62	0.2872	0.9333
0.1335	32.0	64	0.2030	1.0
0.1335	33.0	66	0.2297	0.9333
0.1335	34.0	68	0.2876	0.9333
0.1228	35.0	70	0.1432	1.0
0.1228	36.0	72	0.2194	0.9667
0.1228	37.0	74	0.1387	1.0
0.1228	38.0	76	0.1381	1.0
0.1228	39.0	78	0.1540	1.0
0.1324	40.0	80	0.3075	0.8667
0.1324	41.0	82	0.1892	1.0
0.1324	42.0	84	0.1487	1.0
0.1324	43.0	86	0.1515	1.0
0.1324	44.0	88	0.2617	0.9333
0.136	45.0	90	0.1719	0.9667
0.136	46.0	92	0.2501	0.9
0.136	47.0	94	0.1618	1.0
0.136	48.0	96	0.2175	0.9667
0.136	49.0	98	0.2039	0.9667
0.1226	50.0	100	0.2460	0.9333

Framework versions

• Transformers 4.38.2
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2