vit-base-codenames
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the codenames-pictures dataset. It achieves the following results on the evaluation set:
- Loss: 0.7722
- Accuracy: 0.4643
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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
2.5428 | 0.16 | 100 | 2.4585 | 0.4106 |
1.498 | 0.31 | 200 | 1.5259 | 0.4449 |
1.2131 | 0.47 | 300 | 1.1431 | 0.4652 |
1.0505 | 0.63 | 400 | 1.0880 | 0.4485 |
0.9738 | 0.78 | 500 | 1.0141 | 0.4599 |
1.0137 | 0.94 | 600 | 0.9250 | 0.4670 |
0.932 | 1.1 | 700 | 0.9285 | 0.4731 |
0.9456 | 1.25 | 800 | 0.8803 | 0.4678 |
0.7922 | 1.41 | 900 | 0.8776 | 0.5084 |
0.8335 | 1.56 | 1000 | 0.8627 | 0.4811 |
0.7652 | 1.72 | 1100 | 0.8779 | 0.4952 |
0.8312 | 1.88 | 1200 | 0.8086 | 0.4872 |
0.7136 | 2.03 | 1300 | 0.8158 | 0.4590 |
0.7662 | 2.19 | 1400 | 0.8138 | 0.4661 |
0.7175 | 2.35 | 1500 | 0.8079 | 0.4775 |
0.7614 | 2.5 | 1600 | 0.8031 | 0.4802 |
0.7665 | 2.66 | 1700 | 0.8083 | 0.4467 |
0.7557 | 2.82 | 1800 | 0.8003 | 0.4819 |
0.7678 | 2.97 | 1900 | 0.8159 | 0.5013 |
0.697 | 3.13 | 2000 | 0.7845 | 0.4855 |
0.7211 | 3.29 | 2100 | 0.7942 | 0.4670 |
0.7307 | 3.44 | 2200 | 0.7908 | 0.4740 |
0.7122 | 3.6 | 2300 | 0.7920 | 0.4502 |
0.7597 | 3.76 | 2400 | 0.7722 | 0.4643 |
0.7165 | 3.91 | 2500 | 0.7784 | 0.4819 |
0.7238 | 4.07 | 2600 | 0.7896 | 0.4687 |
0.7047 | 4.23 | 2700 | 0.7849 | 0.4617 |
0.7122 | 4.38 | 2800 | 0.7963 | 0.4881 |
0.7574 | 4.54 | 2900 | 0.8179 | 0.4934 |
0.7987 | 4.69 | 3000 | 0.8060 | 0.4344 |
0.7517 | 4.85 | 3100 | 0.8018 | 0.4537 |
0.7402 | 5.01 | 3200 | 0.8076 | 0.4784 |
0.7223 | 5.16 | 3300 | 0.8026 | 0.4405 |
0.7417 | 5.32 | 3400 | 0.8005 | 0.4185 |
0.7236 | 5.48 | 3500 | 0.7964 | 0.4238 |
0.7114 | 5.63 | 3600 | 0.7989 | 0.3991 |
0.7237 | 5.79 | 3700 | 0.8229 | 0.4070 |
0.7203 | 5.95 | 3800 | 0.7989 | 0.4740 |
0.7081 | 6.1 | 3900 | 0.8194 | 0.4211 |
0.6839 | 6.26 | 4000 | 0.8013 | 0.4300 |
0.6832 | 6.42 | 4100 | 0.8065 | 0.3789 |
0.7093 | 6.57 | 4200 | 0.8014 | 0.3930 |
0.7037 | 6.73 | 4300 | 0.8170 | 0.4185 |
0.6999 | 6.89 | 4400 | 0.8318 | 0.4123 |
0.7166 | 7.04 | 4500 | 0.8254 | 0.4256 |
0.6838 | 7.2 | 4600 | 0.8204 | 0.3903 |
0.7135 | 7.36 | 4700 | 0.8385 | 0.3612 |
0.6999 | 7.51 | 4800 | 0.8406 | 0.3568 |
0.7007 | 7.67 | 4900 | 0.8353 | 0.3674 |
0.6892 | 7.82 | 5000 | 0.8318 | 0.3339 |
0.7036 | 7.98 | 5100 | 0.8310 | 0.3198 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.1
- Datasets 2.12.0
- Tokenizers 0.15.1
- Downloads last month
- 172