deit-base-distilled-patch16-224-hasta-65-fold2

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

• Loss: 0.8776
• Accuracy: 0.6111

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: 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: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.5714	1	1.2210	0.3611
No log	1.7143	3	1.1841	0.2778
No log	2.8571	5	1.3489	0.2778
No log	4.0	7	1.2178	0.2778
No log	4.5714	8	1.1297	0.2222
1.1666	5.7143	10	1.1211	0.3056
1.1666	6.8571	12	1.0956	0.4167
1.1666	8.0	14	1.0999	0.3056
1.1666	8.5714	15	1.1035	0.4167
1.1666	9.7143	17	1.0612	0.4167
1.1666	10.8571	19	1.0405	0.5
1.0161	12.0	21	1.0978	0.3889
1.0161	12.5714	22	1.0110	0.3889
1.0161	13.7143	24	1.0062	0.4722
1.0161	14.8571	26	0.9771	0.5556
1.0161	16.0	28	0.9988	0.5278
1.0161	16.5714	29	0.9967	0.4722
0.9177	17.7143	31	0.9998	0.4444
0.9177	18.8571	33	1.0774	0.5
0.9177	20.0	35	0.9775	0.5278
0.9177	20.5714	36	0.9918	0.5278
0.9177	21.7143	38	1.0066	0.4722
0.7319	22.8571	40	1.0559	0.4722
0.7319	24.0	42	1.0745	0.5833
0.7319	24.5714	43	1.0611	0.5278
0.7319	25.7143	45	0.9831	0.4444
0.7319	26.8571	47	1.0357	0.4444
0.7319	28.0	49	1.1501	0.5556
0.6173	28.5714	50	1.1571	0.5556
0.6173	29.7143	52	0.9706	0.5278
0.6173	30.8571	54	1.0836	0.4444
0.6173	32.0	56	0.9926	0.4722
0.6173	32.5714	57	0.9648	0.5278
0.6173	33.7143	59	1.0513	0.5833
0.5518	34.8571	61	0.9230	0.5556
0.5518	36.0	63	0.9494	0.4444
0.5518	36.5714	64	0.9941	0.4722
0.5518	37.7143	66	0.9323	0.5
0.5518	38.8571	68	0.8776	0.6111
0.512	40.0	70	0.9269	0.5556
0.512	40.5714	71	0.9188	0.5278
0.512	41.7143	73	0.9326	0.4722
0.512	42.8571	75	0.9404	0.5
0.512	44.0	77	0.9047	0.5278
0.512	44.5714	78	0.8947	0.5278
0.4374	45.7143	80	0.8965	0.5833
0.4374	46.8571	82	0.9077	0.5556
0.4374	48.0	84	0.9290	0.5
0.4374	48.5714	85	0.9194	0.5
0.4374	49.7143	87	0.8923	0.5556
0.4374	50.8571	89	0.8754	0.5556
0.3571	52.0	91	0.8767	0.5833
0.3571	52.5714	92	0.8808	0.5556
0.3571	53.7143	94	0.8939	0.4722
0.3571	54.8571	96	0.9078	0.4722
0.3571	56.0	98	0.9170	0.4722
0.3571	56.5714	99	0.9172	0.4722
0.3333	57.1429	100	0.9168	0.4722

Framework versions

• Transformers 4.41.0
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1