File size: 3,234 Bytes
a13e8b7 9d1a2e7 a13e8b7 57ce663 a13e8b7 9d1a2e7 a13e8b7 9d1a2e7 a13e8b7 |
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 |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
base_model: google/vit-base-patch16-224-in21k
model-index:
- name: vit-base-clothing-leafs-example-full-simple_highres
results: []
---
<!-- 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-clothing-leafs-example-full-simple_highres
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 beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9880
- Accuracy: 0.7166
## 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: 2.5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.0202 | 0.14 | 1000 | 1.4969 | 0.6338 |
| 1.3694 | 0.28 | 2000 | 1.2786 | 0.6647 |
| 1.2063 | 0.42 | 3000 | 1.1788 | 0.6794 |
| 1.1544 | 0.56 | 4000 | 1.1320 | 0.6856 |
| 1.1089 | 0.7 | 5000 | 1.1021 | 0.6867 |
| 1.0681 | 0.84 | 6000 | 1.0775 | 0.6935 |
| 1.0483 | 0.98 | 7000 | 1.0461 | 0.7006 |
| 0.9591 | 1.12 | 8000 | 1.0398 | 0.7022 |
| 0.9541 | 1.26 | 9000 | 1.0423 | 0.6981 |
| 0.9382 | 1.4 | 10000 | 1.0322 | 0.7014 |
| 0.9363 | 1.54 | 11000 | 1.0301 | 0.7020 |
| 0.9199 | 1.68 | 12000 | 1.0079 | 0.7106 |
| 0.919 | 1.82 | 13000 | 0.9972 | 0.7120 |
| 0.9203 | 1.96 | 14000 | 1.0011 | 0.7096 |
| 0.8377 | 2.1 | 15000 | 0.9912 | 0.7146 |
| 0.8148 | 2.24 | 16000 | 0.9991 | 0.7121 |
| 0.8153 | 2.38 | 17000 | 1.0070 | 0.7102 |
| 0.8004 | 2.52 | 18000 | 0.9979 | 0.7154 |
| 0.7937 | 2.66 | 19000 | 1.0022 | 0.7136 |
| 0.7989 | 2.8 | 20000 | 0.9880 | 0.7166 |
| 0.7953 | 2.94 | 21000 | 0.9907 | 0.7175 |
| 0.7576 | 3.08 | 22000 | 1.0013 | 0.7136 |
| 0.7018 | 3.22 | 23000 | 1.0022 | 0.7156 |
| 0.7127 | 3.36 | 24000 | 1.0080 | 0.7151 |
| 0.6989 | 3.5 | 25000 | 1.0025 | 0.7159 |
| 0.702 | 3.64 | 26000 | 1.0087 | 0.7167 |
| 0.7122 | 3.78 | 27000 | 1.0042 | 0.7159 |
| 0.6986 | 3.92 | 28000 | 1.0017 | 0.7164 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|