vuiseng9/swin-base-food101-int8-structured43-15eph

jpqd-swin-b-15eph-r1.00-s2e5-mock-main-merge-pr2

This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224 on the food101 dataset. It achieves the following results on the evaluation set:

• Loss: 0.2970
• Accuracy: 0.9144

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: 128
• 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: 15.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
3.8787	0.42	500	3.9971	0.7163
0.8429	0.84	1000	0.6450	0.8678
0.8561	1.27	1500	0.4160	0.8945
0.5777	1.69	2000	0.3664	0.9006
12.3601	2.11	2500	12.0328	0.9023
49.0606	2.54	3000	48.5000	0.8526
75.3173	2.96	3500	75.5341	0.6942
93.6153	3.38	4000	93.3091	0.5929
103.5744	3.8	4500	103.1211	0.5846
107.7701	4.23	5000	108.0755	0.5398
109.5736	4.65	5500	108.7624	0.5855
1.8028	5.07	6000	1.0960	0.8179
1.2549	5.49	6500	0.6560	0.8695
0.7199	5.92	7000	0.5619	0.8769
0.8874	6.34	7500	0.5151	0.8859
0.7429	6.76	8000	0.4830	0.8898
0.6759	7.19	8500	0.4681	0.8926
0.5352	7.61	9000	0.4360	0.8956
0.6021	8.03	9500	0.4202	0.8979
0.5617	8.45	10000	0.3940	0.9003
0.7235	8.88	10500	0.3915	0.9000
0.5323	9.3	11000	0.3793	0.9017
0.589	9.72	11500	0.3670	0.9051
0.425	10.14	12000	0.3615	0.9059
0.7103	10.57	12500	0.3479	0.9070
0.6251	10.99	13000	0.3472	0.9073
0.623	11.41	13500	0.3353	0.9088
0.6012	11.83	14000	0.3292	0.9098
0.4984	12.26	14500	0.3230	0.9112
0.4763	12.68	15000	0.3158	0.9109
0.3209	13.1	15500	0.3120	0.9123
0.4854	13.52	16000	0.3057	0.9126
0.5472	13.95	16500	0.3032	0.9134
0.3264	14.37	17000	0.3013	0.9134
0.4136	14.79	17500	0.2977	0.9141

Framework versions

• Transformers 4.26.1
• Pytorch 1.13.1+cu117
• Datasets 2.10.1
• Tokenizers 0.13.2