Edit model card

swin-food101-jpqd-1to2r1.5-epo7-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: 0.2658
  • Accuracy: 0.9124

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: 7.0

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.2977 0.42 500 0.1949 0.9112
0.3183 0.84 1000 0.1867 0.9144
99.9552 1.27 1500 99.4882 0.7577
162.4195 1.69 2000 162.7763 0.3373
1.2272 2.11 2500 0.7333 0.8564
1.0236 2.54 3000 0.5016 0.8823
0.6472 2.96 3500 0.4337 0.8908
0.52 3.38 4000 0.3927 0.8974
0.6075 3.8 4500 0.3506 0.9011
0.5348 4.23 5000 0.3425 0.9006
0.444 4.65 5500 0.3268 0.9044
0.5787 5.07 6000 0.3020 0.9078
0.3995 5.49 6500 0.2932 0.9095
0.414 5.92 7000 0.2806 0.9104
0.4386 6.34 7500 0.2738 0.9112
0.452 6.76 8000 0.2673 0.9127

Framework versions

  • Transformers 4.26.0
  • Pytorch 1.13.1+cu116
  • Datasets 2.8.0
  • Tokenizers 0.13.2
Downloads last month
4
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Dataset used to train yujiepan/internal.swin-base-food101-int8-structured38.63

Evaluation results