87
This model is a fine-tuned version of microsoft/resnet-50 on the cifar100 dataset. It achieves the following results on the evaluation set:
- Loss: 48.9016
- Accuracy: 0.0161
- Dt Accuracy: 0.0161
- Df Accuracy: 0.016
- Unlearn Overall Accuracy: 0
- Unlearn Time: None
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: 128
- eval_batch_size: 256
- seed: 87
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Overall Accuracy | Unlearn Overall Accuracy | Time |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 63 | 2.9177 | 0.502 | 0 | 0 | None |
| No log | 2.0 | 126 | 6.9817 | 0.157 | 0 | 0 | None |
| No log | 3.0 | 189 | 13.1543 | 0.024 | 0 | 0 | None |
| No log | 4.0 | 252 | 18.8865 | 0.0177 | 0 | 0 | None |
| No log | 5.0 | 315 | 24.4513 | 0.0173 | 0 | 0 | None |
| No log | 6.0 | 378 | 21.8488 | 0.017 | 0 | 0 | None |
| No log | 7.0 | 441 | 29.6726 | 0.0155 | 0 | 0 | None |
| 0.0 | 8.0 | 504 | 38.4382 | 0.0155 | 0 | 0 | None |
| 0.0 | 9.0 | 567 | 36.0011 | 0.0175 | 0 | 0 | None |
| 0.0 | 10.0 | 630 | 31.3724 | 0.0177 | 0 | 0 | None |
| 0.0 | 11.0 | 693 | 36.3950 | 0.0163 | 0 | 0 | None |
| 0.0 | 12.0 | 756 | 40.0759 | 0.015 | 0 | 0 | None |
| 0.0 | 13.0 | 819 | 42.2231 | 0.0147 | 0 | 0 | None |
| 0.0 | 14.0 | 882 | 39.2282 | 0.0152 | 0 | 0 | None |
| 0.0 | 15.0 | 945 | 46.4044 | 0.0152 | 0 | 0 | None |
| 0.0 | 16.0 | 1008 | 43.4441 | 0.016 | 0 | 0 | None |
| 0.0 | 17.0 | 1071 | 45.0638 | 0.0155 | 0 | 0 | None |
| 0.0 | 18.0 | 1134 | 48.9457 | 0.0155 | 0 | 0 | None |
| 0.0 | 19.0 | 1197 | 46.4940 | 0.0158 | 0 | 0 | None |
| 0.0 | 20.0 | 1260 | 48.9016 | 0.016 | 0 | 0 | None |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2