File size: 3,240 Bytes
a00b9dd |
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 |
---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: test_trainer
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: 0.45
---
<!-- 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. -->
# test_trainer
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7380
- Accuracy: 0.45
## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 10 | 2.0828 | 0.1688 |
| No log | 2.0 | 20 | 2.0820 | 0.1688 |
| No log | 3.0 | 30 | 2.0807 | 0.175 |
| No log | 4.0 | 40 | 2.0789 | 0.1875 |
| No log | 5.0 | 50 | 2.0763 | 0.1938 |
| No log | 6.0 | 60 | 2.0733 | 0.1875 |
| No log | 7.0 | 70 | 2.0697 | 0.1875 |
| No log | 8.0 | 80 | 2.0656 | 0.1875 |
| No log | 9.0 | 90 | 2.0605 | 0.2125 |
| No log | 10.0 | 100 | 2.0540 | 0.2313 |
| No log | 11.0 | 110 | 2.0462 | 0.2625 |
| No log | 12.0 | 120 | 2.0369 | 0.2687 |
| No log | 13.0 | 130 | 2.0259 | 0.2687 |
| No log | 14.0 | 140 | 2.0117 | 0.2687 |
| No log | 15.0 | 150 | 1.9947 | 0.3125 |
| No log | 16.0 | 160 | 1.9763 | 0.2938 |
| No log | 17.0 | 170 | 1.9547 | 0.3125 |
| No log | 18.0 | 180 | 1.9313 | 0.325 |
| No log | 19.0 | 190 | 1.9075 | 0.35 |
| No log | 20.0 | 200 | 1.8817 | 0.3563 |
| No log | 21.0 | 210 | 1.8535 | 0.3812 |
| No log | 22.0 | 220 | 1.8244 | 0.4062 |
| No log | 23.0 | 230 | 1.7954 | 0.4188 |
| No log | 24.0 | 240 | 1.7664 | 0.4375 |
| No log | 25.0 | 250 | 1.7380 | 0.45 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|