swinv2-tiny-patch4-window8-256-RD-aptos19
This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 144573075075950992480149202324684800.0000
- Accuracy: 0.6739
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: 0.00015
- train_batch_size: 16
- eval_batch_size: 16
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.86 | 3 | 144573075075950992480149202324684800.0000 | 0.4565 |
| No log | 2.0 | 7 | 144573075075950992480149202324684800.0000 | 0.4565 |
| 141735823463928302525633790371430400.0000 | 2.86 | 10 | 144573075075950992480149202324684800.0000 | 0.4565 |
| 141735823463928302525633790371430400.0000 | 4.0 | 14 | 144573075075950992480149202324684800.0000 | 0.4565 |
| 141735823463928302525633790371430400.0000 | 4.86 | 17 | 144573075075950992480149202324684800.0000 | 0.4565 |
| 148386187888478135085935683952443392.0000 | 6.0 | 21 | 144573075075950992480149202324684800.0000 | 0.4565 |
| 148386187888478135085935683952443392.0000 | 6.86 | 24 | 144573075075950992480149202324684800.0000 | 0.4783 |
| 148386187888478135085935683952443392.0000 | 8.0 | 28 | 144573075075950992480149202324684800.0000 | 0.4565 |
| 166674646480500797315403436963921920.0000 | 8.86 | 31 | 144573075075950992480149202324684800.0000 | 0.4565 |
| 166674646480500797315403436963921920.0000 | 10.0 | 35 | 144573075075950992480149202324684800.0000 | 0.4565 |
| 166674646480500797315403436963921920.0000 | 10.86 | 38 | 144573075075950992480149202324684800.0000 | 0.4565 |
| 123031678471642034838718731348082688.0000 | 12.0 | 42 | 144573075075950992480149202324684800.0000 | 0.5217 |
| 123031678471642034838718731348082688.0000 | 12.86 | 45 | 144573075075950992480149202324684800.0000 | 0.6087 |
| 123031678471642034838718731348082688.0000 | 14.0 | 49 | 144573075075950992480149202324684800.0000 | 0.5435 |
| 160439944687765812243898756589682688.0000 | 14.86 | 52 | 144573075075950992480149202324684800.0000 | 0.6522 |
| 160439944687765812243898756589682688.0000 | 16.0 | 56 | 144573075075950992480149202324684800.0000 | 0.5870 |
| 160439944687765812243898756589682688.0000 | 16.86 | 59 | 144573075075950992480149202324684800.0000 | 0.5652 |
| 151295747083019479456202288017702912.0000 | 18.0 | 63 | 144573075075950992480149202324684800.0000 | 0.6087 |
| 151295747083019479456202288017702912.0000 | 18.86 | 66 | 144573075075950992480149202324684800.0000 | 0.6304 |
| 142151454404478133240649521934893056.0000 | 20.0 | 70 | 144573075075950992480149202324684800.0000 | 0.6522 |
| 142151454404478133240649521934893056.0000 | 20.86 | 73 | 144573075075950992480149202324684800.0000 | 0.6739 |
| 142151454404478133240649521934893056.0000 | 22.0 | 77 | 144573075075950992480149202324684800.0000 | 0.6739 |
| 137163724661555136131785556085440512.0000 | 22.86 | 80 | 144573075075950992480149202324684800.0000 | 0.6304 |
| 137163724661555136131785556085440512.0000 | 24.0 | 84 | 144573075075950992480149202324684800.0000 | 0.6304 |
| 137163724661555136131785556085440512.0000 | 24.86 | 87 | 144573075075950992480149202324684800.0000 | 0.6739 |
| 137163692970290119358004074442129408.0000 | 26.0 | 91 | 144573075075950992480149202324684800.0000 | 0.6304 |
| 137163692970290119358004074442129408.0000 | 26.86 | 94 | 144573075075950992480149202324684800.0000 | 0.6522 |
| 137163692970290119358004074442129408.0000 | 28.0 | 98 | 144573075075950992480149202324684800.0000 | 0.6522 |
| 155452183253577798361253309096919040.0000 | 28.86 | 101 | 144573075075950992480149202324684800.0000 | 0.6739 |
| 155452183253577798361253309096919040.0000 | 30.0 | 105 | 144573075075950992480149202324684800.0000 | 0.6522 |
| 155452183253577798361253309096919040.0000 | 30.86 | 108 | 144573075075950992480149202324684800.0000 | 0.6522 |
| 139657557841751617912436057366855680.0000 | 32.0 | 112 | 144573075075950992480149202324684800.0000 | 0.6522 |
| 139657557841751617912436057366855680.0000 | 32.86 | 115 | 144573075075950992480149202324684800.0000 | 0.6522 |
| 139657557841751617912436057366855680.0000 | 34.0 | 119 | 144573075075950992480149202324684800.0000 | 0.6304 |
| 141735791772663285751852308728119296.0000 | 34.29 | 120 | 144573075075950992480149202324684800.0000 | 0.6304 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0