File size: 4,357 Bytes
b560d29 1e625c4 b560d29 1e625c4 b560d29 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 |
---
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-U8-40d
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: 0.8431372549019608
---
<!-- 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-U8-40d
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: 0.6495
- Accuracy: 0.8431
## 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: 6e-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: 0.05
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3419 | 1.0 | 20 | 1.2998 | 0.4706 |
| 1.1313 | 2.0 | 40 | 1.0832 | 0.5686 |
| 0.7969 | 3.0 | 60 | 0.8094 | 0.6667 |
| 0.5063 | 4.0 | 80 | 0.6573 | 0.7843 |
| 0.3367 | 5.0 | 100 | 0.6389 | 0.7647 |
| 0.242 | 6.0 | 120 | 0.6879 | 0.7451 |
| 0.1881 | 7.0 | 140 | 0.7940 | 0.7059 |
| 0.1561 | 8.0 | 160 | 0.8030 | 0.7647 |
| 0.1557 | 9.0 | 180 | 0.7004 | 0.8235 |
| 0.1154 | 10.0 | 200 | 0.6495 | 0.8431 |
| 0.1469 | 11.0 | 220 | 1.1388 | 0.7059 |
| 0.0898 | 12.0 | 240 | 0.7967 | 0.7647 |
| 0.0719 | 13.0 | 260 | 0.8934 | 0.8039 |
| 0.0739 | 14.0 | 280 | 0.8476 | 0.7647 |
| 0.0823 | 15.0 | 300 | 0.9692 | 0.7647 |
| 0.0828 | 16.0 | 320 | 0.9385 | 0.7843 |
| 0.0761 | 17.0 | 340 | 1.1684 | 0.7255 |
| 0.0597 | 18.0 | 360 | 0.9414 | 0.7647 |
| 0.0727 | 19.0 | 380 | 1.0201 | 0.7059 |
| 0.0507 | 20.0 | 400 | 0.8563 | 0.8039 |
| 0.0587 | 21.0 | 420 | 0.8476 | 0.7843 |
| 0.0608 | 22.0 | 440 | 0.9399 | 0.8039 |
| 0.055 | 23.0 | 460 | 0.8820 | 0.7451 |
| 0.0619 | 24.0 | 480 | 1.0460 | 0.7647 |
| 0.0615 | 25.0 | 500 | 0.9392 | 0.8235 |
| 0.0455 | 26.0 | 520 | 0.9267 | 0.8235 |
| 0.0567 | 27.0 | 540 | 0.9784 | 0.7843 |
| 0.032 | 28.0 | 560 | 1.1541 | 0.7647 |
| 0.0276 | 29.0 | 580 | 0.8865 | 0.7843 |
| 0.0368 | 30.0 | 600 | 1.0848 | 0.8039 |
| 0.0342 | 31.0 | 620 | 0.9638 | 0.8039 |
| 0.037 | 32.0 | 640 | 0.9616 | 0.8039 |
| 0.0371 | 33.0 | 660 | 1.0073 | 0.8039 |
| 0.0371 | 34.0 | 680 | 1.0494 | 0.8039 |
| 0.0359 | 35.0 | 700 | 1.1287 | 0.7843 |
| 0.0255 | 36.0 | 720 | 1.1831 | 0.7647 |
| 0.0269 | 37.0 | 740 | 1.1610 | 0.7843 |
| 0.0292 | 38.0 | 760 | 1.1842 | 0.7843 |
| 0.0161 | 39.0 | 780 | 1.1092 | 0.8039 |
| 0.0333 | 40.0 | 800 | 1.1186 | 0.8039 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|