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vit-base-clothing-leafs-example-full-simple
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset. It achieves the following results on the evaluation set:
- Loss: 0.9954
- Accuracy: 0.7155
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: 3e-05
- train_batch_size: 32
- 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 |
|---|---|---|---|---|
| 1.9495 | 0.14 | 1000 | 1.4553 | 0.6307 |
| 1.3079 | 0.28 | 2000 | 1.2347 | 0.6677 |
| 1.178 | 0.41 | 3000 | 1.1607 | 0.6758 |
| 1.1324 | 0.55 | 4000 | 1.1307 | 0.6824 |
| 1.0928 | 0.69 | 5000 | 1.0956 | 0.6909 |
| 1.0679 | 0.83 | 6000 | 1.0790 | 0.6912 |
| 1.0488 | 0.97 | 7000 | 1.0486 | 0.7014 |
| 0.9548 | 1.11 | 8000 | 1.0449 | 0.7016 |
| 0.9352 | 1.24 | 9000 | 1.0348 | 0.7042 |
| 0.9164 | 1.38 | 10000 | 1.0340 | 0.7034 |
| 0.9267 | 1.52 | 11000 | 1.0178 | 0.7089 |
| 0.9058 | 1.66 | 12000 | 1.0160 | 0.7063 |
| 0.9028 | 1.8 | 13000 | 1.0084 | 0.7111 |
| 0.9093 | 1.94 | 14000 | 1.0009 | 0.7136 |
| 0.8346 | 2.07 | 15000 | 1.0152 | 0.7117 |
| 0.7897 | 2.21 | 16000 | 1.0072 | 0.7141 |
| 0.7869 | 2.35 | 17000 | 1.0088 | 0.7083 |
| 0.7853 | 2.49 | 18000 | 0.9981 | 0.7162 |
| 0.7732 | 2.63 | 19000 | 1.0030 | 0.7149 |
| 0.779 | 2.77 | 20000 | 0.9954 | 0.7155 |
| 0.7655 | 2.9 | 21000 | 0.9972 | 0.7179 |
| 0.74 | 3.04 | 22000 | 1.0114 | 0.7138 |
| 0.6824 | 3.18 | 23000 | 1.0171 | 0.7130 |
| 0.68 | 3.32 | 24000 | 1.0111 | 0.7178 |
| 0.6787 | 3.46 | 25000 | 1.0124 | 0.7151 |
| 0.6808 | 3.6 | 26000 | 1.0181 | 0.7150 |
| 0.6561 | 3.73 | 27000 | 1.0144 | 0.7168 |
| 0.6611 | 3.87 | 28000 | 1.0154 | 0.7155 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
