File size: 2,888 Bytes
f851078 1c351b1 f851078 1c351b1 f851078 8243065 f851078 8243065 f851078 1c351b1 f851078 |
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 |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: image_classification
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: 0.5625
---
<!-- 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. -->
# image_classification
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: 1.3383
- Accuracy: 0.5625
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 80 | 1.6519 | 0.3312 |
| No log | 2.0 | 160 | 1.4509 | 0.4125 |
| No log | 3.0 | 240 | 1.3641 | 0.5062 |
| No log | 4.0 | 320 | 1.2676 | 0.5875 |
| No log | 5.0 | 400 | 1.2718 | 0.5188 |
| No log | 6.0 | 480 | 1.2250 | 0.5125 |
| 1.2828 | 7.0 | 560 | 1.1933 | 0.55 |
| 1.2828 | 8.0 | 640 | 1.1538 | 0.575 |
| 1.2828 | 9.0 | 720 | 1.2479 | 0.55 |
| 1.2828 | 10.0 | 800 | 1.2487 | 0.575 |
| 1.2828 | 11.0 | 880 | 1.2418 | 0.5938 |
| 1.2828 | 12.0 | 960 | 1.1514 | 0.6062 |
| 0.5147 | 13.0 | 1040 | 1.2563 | 0.5563 |
| 0.5147 | 14.0 | 1120 | 1.2933 | 0.5813 |
| 0.5147 | 15.0 | 1200 | 1.2857 | 0.5813 |
| 0.5147 | 16.0 | 1280 | 1.3044 | 0.575 |
| 0.5147 | 17.0 | 1360 | 1.4134 | 0.5687 |
| 0.5147 | 18.0 | 1440 | 1.3277 | 0.5875 |
| 0.2675 | 19.0 | 1520 | 1.2963 | 0.575 |
| 0.2675 | 20.0 | 1600 | 1.2049 | 0.6125 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|