yujiepan/internal.swin-base-food101-int8-structured38.01

swin-food101-jpqd-1to2r1.5-epo10-finetuned-student

This model is a fine-tuned version of skylord/swin-finetuned-food101 on the food101 dataset. It achieves the following results on the evaluation set:

• Loss: 0.2391
• Accuracy: 0.9184

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
• num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.3011	0.42	500	0.1951	0.9124
0.2613	0.84	1000	0.1897	0.9139
100.1552	1.27	1500	99.5975	0.7445
162.0751	1.69	2000	162.5020	0.3512
1.061	2.11	2500	0.7523	0.8550
0.9728	2.54	3000	0.5263	0.8767
0.5851	2.96	3500	0.4599	0.8892
0.4668	3.38	4000	0.4064	0.8938
0.6967	3.8	4500	0.3814	0.8986
0.4928	4.23	5000	0.3522	0.9036
0.4893	4.65	5500	0.3562	0.9026
0.5421	5.07	6000	0.3182	0.9049
0.4405	5.49	6500	0.3112	0.9071
0.4423	5.92	7000	0.3012	0.9092
0.4143	6.34	7500	0.2958	0.9095
0.4997	6.76	8000	0.2796	0.9126
0.2448	7.19	8500	0.2747	0.9124
0.4468	7.61	9000	0.2699	0.9144
0.4163	8.03	9500	0.2583	0.9166
0.3651	8.45	10000	0.2567	0.9165
0.3946	8.88	10500	0.2489	0.9176
0.3196	9.3	11000	0.2444	0.9180
0.312	9.72	11500	0.2402	0.9172

Framework versions

• Transformers 4.26.0
• Pytorch 1.13.1+cu116
• Datasets 2.8.0
• Tokenizers 0.13.2