File size: 2,383 Bytes
c45e8df 5a9c25c c45e8df 5d78d3f c45e8df 5d78d3f c45e8df 5d78d3f c45e8df |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
---
license: apache-2.0
base_model: microsoft/swinv2-base-patch4-window12-192-22k
tags:
- image-classification
- vision
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: SWIN_finetuned_frozen_v5_cont
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SWIN_finetuned_frozen_v5_cont
This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window12-192-22k](https://huggingface.co/microsoft/swinv2-base-patch4-window12-192-22k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1793
- Accuracy: 0.7000
## 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: 0.0004
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 0.6306 | 1.0 | 2625 | 0.6565 | 1.9169 |
| 0.5977 | 2.0 | 5250 | 0.6614 | 1.9120 |
| 0.5472 | 3.0 | 7875 | 0.6635 | 1.9410 |
| 0.513 | 4.0 | 10500 | 0.6679 | 1.9818 |
| 0.4714 | 5.0 | 13125 | 0.6711 | 1.9428 |
| 0.4378 | 6.0 | 15750 | 0.6727 | 2.0051 |
| 0.4105 | 7.0 | 18375 | 0.6778 | 1.9917 |
| 0.3837 | 8.0 | 21000 | 0.6792 | 2.0413 |
| 0.3602 | 9.0 | 23625 | 0.6870 | 2.0738 |
| 0.336 | 10.0 | 26250 | 2.0872 | 0.6876 |
| 0.3057 | 11.0 | 28875 | 2.1163 | 0.6894 |
| 0.2856 | 12.0 | 31500 | 2.1105 | 0.6936 |
| 0.2704 | 13.0 | 34125 | 2.1685 | 0.6965 |
| 0.2503 | 14.0 | 36750 | 2.1627 | 0.6994 |
| 0.2362 | 15.0 | 39375 | 2.1793 | 0.7000 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.13.3
|