metadata

license: apache-2.0
tags:
  - image-classification
  - vision
  - generated_from_trainer
datasets:
  - food101
metrics:
  - accuracy
model-index:
  - name: swin-food101-jpqd-1to2r1.5-epo10-finetuned-student
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: food101
          type: food101
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9183762376237624

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