File size: 4,102 Bytes
d49d7b8 b6ccbec d49d7b8 038be82 d49d7b8 70139a9 d49d7b8 038be82 d49d7b8 |
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 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 |
---
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-tiny-patch4-window8-256-DMAE-ex
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.45652173913043476
---
<!-- 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. -->
# swinv2-tiny-patch4-window8-256-DMAE-ex
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6895
- Accuracy: 0.4565
## 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: 0.002
- 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 | 7.5017 | 0.1087 |
| No log | 2.0 | 7 | 1.6895 | 0.4565 |
| 4.88 | 2.86 | 10 | 4.1382 | 0.1087 |
| 4.88 | 4.0 | 14 | 1.5189 | 0.4565 |
| 4.88 | 4.86 | 17 | 1.4064 | 0.3261 |
| 1.7482 | 6.0 | 21 | 1.3558 | 0.4565 |
| 1.7482 | 6.86 | 24 | 1.3180 | 0.3261 |
| 1.7482 | 8.0 | 28 | 1.2378 | 0.3261 |
| 1.3281 | 8.86 | 31 | 1.3390 | 0.4565 |
| 1.3281 | 10.0 | 35 | 1.2144 | 0.4565 |
| 1.3281 | 10.86 | 38 | 1.2492 | 0.3261 |
| 1.2367 | 12.0 | 42 | 1.2685 | 0.4565 |
| 1.2367 | 12.86 | 45 | 1.2390 | 0.4565 |
| 1.2367 | 14.0 | 49 | 1.2648 | 0.3261 |
| 1.2707 | 14.86 | 52 | 1.2209 | 0.4565 |
| 1.2707 | 16.0 | 56 | 1.2628 | 0.4565 |
| 1.2707 | 16.86 | 59 | 1.2173 | 0.4565 |
| 1.2699 | 18.0 | 63 | 1.2145 | 0.4565 |
| 1.2699 | 18.86 | 66 | 1.2334 | 0.4348 |
| 1.2509 | 20.0 | 70 | 1.2693 | 0.4565 |
| 1.2509 | 20.86 | 73 | 1.2041 | 0.4565 |
| 1.2509 | 22.0 | 77 | 1.2307 | 0.3696 |
| 1.1936 | 22.86 | 80 | 1.2172 | 0.4565 |
| 1.1936 | 24.0 | 84 | 1.2109 | 0.4565 |
| 1.1936 | 24.86 | 87 | 1.2051 | 0.4565 |
| 1.1629 | 26.0 | 91 | 1.2084 | 0.4565 |
| 1.1629 | 26.86 | 94 | 1.2218 | 0.4565 |
| 1.1629 | 28.0 | 98 | 1.2294 | 0.4565 |
| 1.1606 | 28.86 | 101 | 1.2060 | 0.4565 |
| 1.1606 | 30.0 | 105 | 1.2063 | 0.4130 |
| 1.1606 | 30.86 | 108 | 1.2119 | 0.4130 |
| 1.1525 | 32.0 | 112 | 1.2073 | 0.4565 |
| 1.1525 | 32.86 | 115 | 1.1995 | 0.4565 |
| 1.1525 | 34.0 | 119 | 1.1954 | 0.4565 |
| 1.1326 | 34.29 | 120 | 1.1950 | 0.4565 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|