File size: 4,933 Bytes
7e3aaab |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: meat_calssify_fresh_crop_V_0_3
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: 0.7161290322580646
---
<!-- 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. -->
# meat_calssify_fresh_crop_V_0_3
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: 1.1160
- Accuracy: 0.7161
## 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: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.09 | 1.0 | 39 | 1.0822 | 0.4710 |
| 1.0215 | 2.0 | 78 | 0.9976 | 0.5419 |
| 0.9299 | 3.0 | 117 | 0.9265 | 0.5548 |
| 0.8981 | 4.0 | 156 | 0.8926 | 0.5935 |
| 0.8256 | 5.0 | 195 | 0.9415 | 0.5548 |
| 0.7763 | 6.0 | 234 | 0.9029 | 0.5935 |
| 0.6964 | 7.0 | 273 | 0.8402 | 0.5935 |
| 0.678 | 8.0 | 312 | 0.8272 | 0.6 |
| 0.6114 | 9.0 | 351 | 0.9511 | 0.5935 |
| 0.5694 | 10.0 | 390 | 0.7493 | 0.6645 |
| 0.5335 | 11.0 | 429 | 0.8895 | 0.6452 |
| 0.4437 | 12.0 | 468 | 0.7902 | 0.6774 |
| 0.4836 | 13.0 | 507 | 0.8206 | 0.6387 |
| 0.4167 | 14.0 | 546 | 0.8594 | 0.6710 |
| 0.3775 | 15.0 | 585 | 0.8840 | 0.6645 |
| 0.3132 | 16.0 | 624 | 0.7669 | 0.6710 |
| 0.3099 | 17.0 | 663 | 0.8012 | 0.6903 |
| 0.307 | 18.0 | 702 | 0.8098 | 0.6839 |
| 0.2905 | 19.0 | 741 | 0.7889 | 0.7226 |
| 0.2854 | 20.0 | 780 | 0.8555 | 0.6968 |
| 0.1875 | 21.0 | 819 | 0.8501 | 0.7097 |
| 0.2485 | 22.0 | 858 | 0.8381 | 0.7419 |
| 0.22 | 23.0 | 897 | 1.0090 | 0.6774 |
| 0.2283 | 24.0 | 936 | 0.9999 | 0.6323 |
| 0.1934 | 25.0 | 975 | 0.9455 | 0.7097 |
| 0.1841 | 26.0 | 1014 | 0.7737 | 0.7484 |
| 0.1711 | 27.0 | 1053 | 0.8872 | 0.7355 |
| 0.1579 | 28.0 | 1092 | 1.0535 | 0.6903 |
| 0.176 | 29.0 | 1131 | 0.9783 | 0.6968 |
| 0.2307 | 30.0 | 1170 | 0.8435 | 0.7226 |
| 0.1379 | 31.0 | 1209 | 0.9598 | 0.7097 |
| 0.1181 | 32.0 | 1248 | 0.9325 | 0.7419 |
| 0.1529 | 33.0 | 1287 | 1.0973 | 0.6839 |
| 0.1252 | 34.0 | 1326 | 0.8859 | 0.7484 |
| 0.1005 | 35.0 | 1365 | 0.9212 | 0.7613 |
| 0.1446 | 36.0 | 1404 | 0.7894 | 0.7806 |
| 0.0776 | 37.0 | 1443 | 0.9259 | 0.7484 |
| 0.1067 | 38.0 | 1482 | 1.0468 | 0.7226 |
| 0.0983 | 39.0 | 1521 | 0.9468 | 0.7355 |
| 0.1155 | 40.0 | 1560 | 1.0564 | 0.7226 |
| 0.1037 | 41.0 | 1599 | 1.0964 | 0.6968 |
| 0.102 | 42.0 | 1638 | 0.9690 | 0.7290 |
| 0.0904 | 43.0 | 1677 | 0.9662 | 0.7419 |
| 0.0577 | 44.0 | 1716 | 1.2786 | 0.6645 |
| 0.1086 | 45.0 | 1755 | 1.0993 | 0.7226 |
| 0.0698 | 46.0 | 1794 | 1.1927 | 0.7032 |
| 0.0532 | 47.0 | 1833 | 0.9616 | 0.7484 |
| 0.0705 | 48.0 | 1872 | 0.7846 | 0.7806 |
| 0.0611 | 49.0 | 1911 | 0.9952 | 0.7290 |
| 0.0769 | 50.0 | 1950 | 1.1160 | 0.7161 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0
- Datasets 2.19.2
- Tokenizers 0.19.1
|