This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on the bengali_food_images dataset. It achieves the following results on the evaluation set:

Loss: 0.2321
Accuracy: 0.9457

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: 0.0002
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.7162	0.09	100	1.4225	0.7079
1.2286	0.17	200	0.9461	0.7935
1.0323	0.26	300	0.7366	0.8356
0.8678	0.34	400	0.6211	0.8628
0.7849	0.43	500	0.5354	0.8655
0.7105	0.51	600	0.4793	0.8899
0.6198	0.6	700	0.4319	0.9090
0.6276	0.68	800	0.4022	0.8981
0.5411	0.77	900	0.3816	0.9117
0.4984	0.85	1000	0.3824	0.9022
0.5665	0.94	1100	0.3460	0.9212
0.5741	1.02	1200	0.3336	0.9158
0.4039	1.11	1300	0.3204	0.9130
0.4347	1.19	1400	0.3038	0.9307
0.3639	1.28	1500	0.2955	0.9253
0.4282	1.36	1600	0.2948	0.9293
0.4375	1.45	1700	0.2868	0.9212
0.3063	1.53	1800	0.2861	0.9334
0.3549	1.62	1900	0.2826	0.9293
0.4326	1.71	2000	0.2698	0.9348
0.3697	1.79	2100	0.2602	0.9280
0.3155	1.88	2200	0.2523	0.9361
0.3348	1.96	2300	0.2506	0.9470
0.3854	2.05	2400	0.2565	0.9321
0.3951	2.13	2500	0.2482	0.9402
0.3531	2.22	2600	0.2455	0.9402
0.3643	2.3	2700	0.2513	0.9375
0.3393	2.39	2800	0.2492	0.9429
0.3635	2.47	2900	0.2394	0.9402
0.3624	2.56	3000	0.2425	0.9389
0.3608	2.64	3100	0.2390	0.9457
0.3215	2.73	3200	0.2483	0.9321
0.2971	2.81	3300	0.2455	0.9402
0.3838	2.9	3400	0.2363	0.9470
0.3036	2.98	3500	0.2422	0.9402
0.401	3.07	3600	0.2398	0.9429
0.3458	3.15	3700	0.2517	0.9429
0.2908	3.24	3800	0.2423	0.9457
0.3016	3.32	3900	0.2402	0.9443
0.2961	3.41	4000	0.2414	0.9457
0.3822	3.5	4100	0.2413	0.9416
0.2596	3.58	4200	0.2356	0.9457
0.3064	3.67	4300	0.2324	0.9497
0.3059	3.75	4400	0.2321	0.9457
0.42	3.84	4500	0.2556	0.9402
0.2959	3.92	4600	0.2491	0.9416

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2

Zannatul/microsoft_swinv2-tiny-patch4-window8-256-batch_16_epoch_4_classes_24_final_withAug

microsoft_swinv2-tiny-patch4-window8-256-batch_16_epoch_4_classes_24_final_withAug

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Zannatul/
microsoft_swinv2-tiny-patch4-window8-256-batch_16_epoch_4_classes_24_final_withAug