File size: 2,852 Bytes
c474c89 | 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 | ---
library_name: transformers
license: apache-2.0
base_model: google/vit-large-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: wool-classifier-finetuned
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7777777777777778
- name: F1
type: f1
value: 0.7681561135293505
- name: Precision
type: precision
value: 0.7982514741774002
- name: Recall
type: recall
value: 0.7777777777777778
---
<!-- 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. -->
# wool-classifier-finetuned
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6767
- Accuracy: 0.7778
- F1: 0.7682
- Precision: 0.7983
- Recall: 0.7778
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.7426 | 1.0 | 45 | 0.7618 | 0.7284 | 0.7285 | 0.7981 | 0.7284 |
| 0.6744 | 2.0 | 90 | 0.8640 | 0.7284 | 0.7064 | 0.7190 | 0.7284 |
| 0.4237 | 3.0 | 135 | 0.6118 | 0.8148 | 0.8115 | 0.8309 | 0.8148 |
| 0.473 | 4.0 | 180 | 0.6418 | 0.8025 | 0.7843 | 0.8481 | 0.8025 |
| 0.3436 | 5.0 | 225 | 0.4420 | 0.8765 | 0.8606 | 0.8928 | 0.8765 |
| 0.2142 | 6.0 | 270 | 0.7575 | 0.7654 | 0.7508 | 0.8080 | 0.7654 |
| 0.2729 | 7.0 | 315 | 0.6660 | 0.7901 | 0.7768 | 0.8183 | 0.7901 |
| 0.3112 | 8.0 | 360 | 0.6767 | 0.7778 | 0.7682 | 0.7983 | 0.7778 |
### Framework versions
- Transformers 4.55.4
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|