File size: 3,927 Bytes
d4ac38a |
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: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-finetuned-cifar10
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9844
---
<!-- 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. -->
# vit-base-patch16-224-finetuned-cifar10
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0564
- Accuracy: 0.9844
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.4597 | 0.03 | 10 | 2.2902 | 0.1662 |
| 2.1429 | 0.06 | 20 | 1.7855 | 0.5086 |
| 1.6466 | 0.09 | 30 | 1.0829 | 0.8484 |
| 0.9962 | 0.11 | 40 | 0.4978 | 0.9288 |
| 0.6127 | 0.14 | 50 | 0.2717 | 0.9508 |
| 0.4544 | 0.17 | 60 | 0.1942 | 0.9588 |
| 0.4352 | 0.2 | 70 | 0.1504 | 0.9672 |
| 0.374 | 0.23 | 80 | 0.1221 | 0.9718 |
| 0.3261 | 0.26 | 90 | 0.1057 | 0.9772 |
| 0.34 | 0.28 | 100 | 0.0943 | 0.979 |
| 0.284 | 0.31 | 110 | 0.0958 | 0.9754 |
| 0.3151 | 0.34 | 120 | 0.0866 | 0.9776 |
| 0.3004 | 0.37 | 130 | 0.0838 | 0.9788 |
| 0.3334 | 0.4 | 140 | 0.0798 | 0.9806 |
| 0.3018 | 0.43 | 150 | 0.0800 | 0.9778 |
| 0.2957 | 0.45 | 160 | 0.0749 | 0.9808 |
| 0.2952 | 0.48 | 170 | 0.0704 | 0.9814 |
| 0.3084 | 0.51 | 180 | 0.0720 | 0.9812 |
| 0.3015 | 0.54 | 190 | 0.0708 | 0.983 |
| 0.2763 | 0.57 | 200 | 0.0672 | 0.9832 |
| 0.3376 | 0.6 | 210 | 0.0700 | 0.982 |
| 0.285 | 0.63 | 220 | 0.0657 | 0.9828 |
| 0.2857 | 0.65 | 230 | 0.0629 | 0.9836 |
| 0.2644 | 0.68 | 240 | 0.0612 | 0.9842 |
| 0.2461 | 0.71 | 250 | 0.0601 | 0.9836 |
| 0.2802 | 0.74 | 260 | 0.0589 | 0.9842 |
| 0.2481 | 0.77 | 270 | 0.0604 | 0.9838 |
| 0.2641 | 0.8 | 280 | 0.0591 | 0.9846 |
| 0.2737 | 0.82 | 290 | 0.0581 | 0.9842 |
| 0.2391 | 0.85 | 300 | 0.0565 | 0.9852 |
| 0.2283 | 0.88 | 310 | 0.0558 | 0.986 |
| 0.2626 | 0.91 | 320 | 0.0559 | 0.9852 |
| 0.2325 | 0.94 | 330 | 0.0563 | 0.9846 |
| 0.2459 | 0.97 | 340 | 0.0565 | 0.9846 |
| 0.2474 | 1.0 | 350 | 0.0564 | 0.9844 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.1
- Datasets 2.14.6
- Tokenizers 0.14.1
|