File size: 3,753 Bytes
213956a 6e115f9 213956a 6e115f9 213956a 6e115f9 213956a 6e115f9 213956a 6e115f9 213956a |
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 |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: output_dir
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: 0.5875
---
<!-- 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. -->
# output_dir
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: 1.2119
- Accuracy: 0.5875
## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: reduce_lr_on_plateau
- num_epochs: 41
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.8 | 2 | 2.0638 | 0.1562 |
| No log | 2.0 | 5 | 2.0353 | 0.2 |
| No log | 2.8 | 7 | 1.9965 | 0.2687 |
| 1.9968 | 4.0 | 10 | 1.9289 | 0.3937 |
| 1.9968 | 4.8 | 12 | 1.8942 | 0.3125 |
| 1.9968 | 6.0 | 15 | 1.8054 | 0.4562 |
| 1.9968 | 6.8 | 17 | 1.7626 | 0.4313 |
| 1.7555 | 8.0 | 20 | 1.7078 | 0.4562 |
| 1.7555 | 8.8 | 22 | 1.6608 | 0.45 |
| 1.7555 | 10.0 | 25 | 1.6121 | 0.425 |
| 1.7555 | 10.8 | 27 | 1.5759 | 0.4813 |
| 1.5214 | 12.0 | 30 | 1.5340 | 0.4562 |
| 1.5214 | 12.8 | 32 | 1.5006 | 0.5062 |
| 1.5214 | 14.0 | 35 | 1.4956 | 0.4313 |
| 1.5214 | 14.8 | 37 | 1.4418 | 0.5125 |
| 1.3342 | 16.0 | 40 | 1.4236 | 0.525 |
| 1.3342 | 16.8 | 42 | 1.3784 | 0.55 |
| 1.3342 | 18.0 | 45 | 1.4367 | 0.4938 |
| 1.3342 | 18.8 | 47 | 1.3665 | 0.525 |
| 1.1553 | 20.0 | 50 | 1.3867 | 0.4813 |
| 1.1553 | 20.8 | 52 | 1.3536 | 0.5312 |
| 1.1553 | 22.0 | 55 | 1.3391 | 0.5125 |
| 1.1553 | 22.8 | 57 | 1.2930 | 0.5563 |
| 0.9972 | 24.0 | 60 | 1.2894 | 0.5375 |
| 0.9972 | 24.8 | 62 | 1.2802 | 0.5625 |
| 0.9972 | 26.0 | 65 | 1.2671 | 0.5687 |
| 0.9972 | 26.8 | 67 | 1.2491 | 0.5625 |
| 0.838 | 28.0 | 70 | 1.2907 | 0.5437 |
| 0.838 | 28.8 | 72 | 1.2806 | 0.5563 |
| 0.838 | 30.0 | 75 | 1.2228 | 0.5687 |
| 0.838 | 30.8 | 77 | 1.2485 | 0.575 |
| 0.7226 | 32.0 | 80 | 1.2777 | 0.5437 |
| 0.7226 | 32.8 | 82 | 1.2106 | 0.6 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|