File size: 3,025 Bytes
b3872cd |
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 |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: computer_parts_classifier-model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train[:722]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8137931034482758
---
<!-- 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. -->
# computer_parts_classifier-model
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: 0.5117
- Accuracy: 0.8138
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.97 | 9 | 1.0525 | 0.5517 |
| 1.0645 | 1.95 | 18 | 0.9405 | 0.6 |
| 0.9405 | 2.92 | 27 | 0.7902 | 0.7034 |
| 0.7669 | 4.0 | 37 | 0.6923 | 0.7379 |
| 0.6008 | 4.97 | 46 | 0.6152 | 0.7862 |
| 0.5142 | 5.95 | 55 | 0.5639 | 0.7931 |
| 0.394 | 6.92 | 64 | 0.5640 | 0.8 |
| 0.3649 | 8.0 | 74 | 0.5181 | 0.7862 |
| 0.279 | 8.97 | 83 | 0.5094 | 0.8345 |
| 0.2549 | 9.95 | 92 | 0.4882 | 0.8276 |
| 0.1925 | 10.92 | 101 | 0.5041 | 0.8 |
| 0.2185 | 12.0 | 111 | 0.5195 | 0.8138 |
| 0.1921 | 12.97 | 120 | 0.5170 | 0.8 |
| 0.1921 | 13.95 | 129 | 0.5846 | 0.7793 |
| 0.15 | 14.92 | 138 | 0.5217 | 0.8207 |
| 0.1798 | 16.0 | 148 | 0.5421 | 0.7862 |
| 0.1729 | 16.97 | 157 | 0.5516 | 0.8207 |
| 0.1459 | 17.95 | 166 | 0.5438 | 0.7931 |
| 0.1701 | 18.92 | 175 | 0.5043 | 0.8345 |
| 0.1487 | 19.46 | 180 | 0.5117 | 0.8138 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|