learning_rate: 0.0003
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
4.3903	0.012	150	4.2290	0.0146	0.0134	0.0146	0.0083
3.846	0.024	300	3.6356	0.0838	0.0946	0.0838	0.0592
3.3778	0.036	450	3.0217	0.1558	0.2091	0.1558	0.1167
2.9266	0.048	600	2.6267	0.1918	0.2768	0.1918	0.1498
2.7657	0.06	750	2.3280	0.2335	0.3684	0.2335	0.1946
2.6257	0.072	900	2.1951	0.2758	0.3689	0.2758	0.2381
2.4699	0.084	1050	2.3175	0.2424	0.3960	0.2424	0.2101
2.5352	0.096	1200	2.2917	0.2533	0.3728	0.2533	0.2284
2.4032	0.108	1350	2.4920	0.251	0.3818	0.251	0.2225
2.332	0.12	1500	2.3880	0.2639	0.3638	0.2639	0.2324
2.3968	0.132	1650	2.4804	0.2687	0.3862	0.2687	0.2531
2.3922	0.144	1800	2.3411	0.2886	0.4126	0.2886	0.2600
2.3328	0.156	1950	2.2690	0.3191	0.3973	0.3191	0.2896
2.3191	0.168	2100	2.1504	0.3387	0.4172	0.3387	0.3161
2.1208	0.18	2250	2.1226	0.3369	0.4232	0.3369	0.3154
2.2256	0.192	2400	2.0580	0.3629	0.4330	0.3629	0.3372
2.1618	0.204	2550	2.0567	0.3585	0.4509	0.3585	0.3360
2.2237	0.216	2700	2.3808	0.3446	0.4299	0.3446	0.3254
2.0754	0.228	2850	2.2442	0.3718	0.4656	0.3718	0.3529
1.9684	0.24	3000	2.1301	0.3848	0.4569	0.3848	0.3590
2.082	0.252	3150	2.0963	0.3734	0.4557	0.3734	0.3533
2.0737	0.264	3300	2.2619	0.3621	0.4506	0.3621	0.3443
2.0049	0.276	3450	2.3372	0.3748	0.4527	0.3748	0.3542
1.9876	0.288	3600	2.0522	0.4025	0.4759	0.4025	0.3818
1.9218	0.3	3750	2.1785	0.4002	0.4704	0.4002	0.3863
1.9899	0.312	3900	2.3298	0.4059	0.4758	0.4059	0.3852
1.9478	0.324	4050	2.0669	0.4245	0.4732	0.4245	0.4033
1.9293	0.336	4200	2.1866	0.4154	0.4885	0.4154	0.3986
1.8939	0.348	4350	2.1652	0.4159	0.4788	0.4159	0.3944
1.8356	0.36	4500	2.1702	0.4245	0.4706	0.4245	0.4002
1.8724	0.372	4650	2.1267	0.4282	0.4825	0.4282	0.4089
1.7633	0.384	4800	2.1603	0.4262	0.4896	0.4262	0.4065
1.8592	0.396	4950	2.0575	0.4393	0.4837	0.4393	0.4187
1.7407	0.408	5100	2.0839	0.4336	0.5017	0.4336	0.4223

Framework versions

Transformers 4.57.3
Pytorch 2.9.1+cu128
Datasets 4.4.2
Tokenizers 0.22.1

thethinkmachine/student-resnet18-tiny-imagenet

student

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

thethinkmachine/
student-resnet18-tiny-imagenet