smids_3x_deit_tiny_rms_0001_fold5

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

• Loss: 0.9250
• Accuracy: 0.8933

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: 0.0001
• 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
0.4046	1.0	225	0.3353	0.855
0.2238	2.0	450	0.2977	0.8967
0.2029	3.0	675	0.3292	0.8717
0.1861	4.0	900	0.3918	0.8633
0.097	5.0	1125	0.5728	0.87
0.0763	6.0	1350	0.3602	0.8867
0.1211	7.0	1575	0.3953	0.9067
0.0628	8.0	1800	0.5619	0.8917
0.1484	9.0	2025	0.5750	0.88
0.0452	10.0	2250	0.6659	0.89
0.0229	11.0	2475	0.6256	0.8933
0.0617	12.0	2700	0.7075	0.87
0.0553	13.0	2925	0.6972	0.8983
0.0308	14.0	3150	0.6494	0.8983
0.0312	15.0	3375	0.6866	0.9
0.011	16.0	3600	0.7253	0.895
0.0983	17.0	3825	0.7035	0.8933
0.0451	18.0	4050	0.8265	0.8933
0.0418	19.0	4275	0.8696	0.8767
0.0469	20.0	4500	0.6273	0.9133
0.0203	21.0	4725	0.7939	0.895
0.0102	22.0	4950	0.7226	0.8967
0.0005	23.0	5175	0.8708	0.8933
0.0229	24.0	5400	0.9025	0.89
0.0344	25.0	5625	0.7685	0.9033
0.0016	26.0	5850	0.7805	0.9067
0.0048	27.0	6075	0.7684	0.9033
0.0028	28.0	6300	0.8595	0.8933
0.0098	29.0	6525	0.8847	0.8983
0.0002	30.0	6750	0.8488	0.8917
0.0	31.0	6975	0.9022	0.8883
0.0	32.0	7200	0.8024	0.895
0.0047	33.0	7425	0.8208	0.8933
0.0001	34.0	7650	0.9019	0.9017
0.0033	35.0	7875	0.8774	0.8883
0.0	36.0	8100	0.8642	0.885
0.0189	37.0	8325	0.8309	0.8983
0.0	38.0	8550	0.9322	0.89
0.0	39.0	8775	0.9453	0.8933
0.0	40.0	9000	0.9411	0.89
0.0	41.0	9225	0.9468	0.8917
0.0	42.0	9450	0.9584	0.8967
0.003	43.0	9675	0.9469	0.8917
0.0	44.0	9900	0.9339	0.8917
0.0	45.0	10125	0.9259	0.89
0.0	46.0	10350	0.9294	0.8917
0.0	47.0	10575	0.9214	0.8917
0.0	48.0	10800	0.9235	0.8917
0.0	49.0	11025	0.9243	0.8933
0.0	50.0	11250	0.9250	0.8933

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.1+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2