hushem_1x_deit_tiny_rms_001_fold4
This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0712
- Accuracy: 0.4762
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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- 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 |
| No log |
1.0 |
6 |
5.0165 |
0.2381 |
| 4.2481 |
2.0 |
12 |
3.3074 |
0.2381 |
| 4.2481 |
3.0 |
18 |
1.5288 |
0.2619 |
| 2.0024 |
4.0 |
24 |
1.5375 |
0.2381 |
| 1.6731 |
5.0 |
30 |
1.4069 |
0.2619 |
| 1.6731 |
6.0 |
36 |
1.8969 |
0.2381 |
| 1.5329 |
7.0 |
42 |
1.4811 |
0.2381 |
| 1.5329 |
8.0 |
48 |
1.4117 |
0.2619 |
| 1.475 |
9.0 |
54 |
1.4704 |
0.2619 |
| 1.4639 |
10.0 |
60 |
1.4459 |
0.2381 |
| 1.4639 |
11.0 |
66 |
1.3572 |
0.4524 |
| 1.4524 |
12.0 |
72 |
1.2630 |
0.4524 |
| 1.4524 |
13.0 |
78 |
1.2843 |
0.4524 |
| 1.4025 |
14.0 |
84 |
1.3420 |
0.2857 |
| 1.3666 |
15.0 |
90 |
1.4060 |
0.2381 |
| 1.3666 |
16.0 |
96 |
1.2621 |
0.3810 |
| 1.3178 |
17.0 |
102 |
1.2969 |
0.2857 |
| 1.3178 |
18.0 |
108 |
1.2881 |
0.3333 |
| 1.3667 |
19.0 |
114 |
1.3980 |
0.2857 |
| 1.3043 |
20.0 |
120 |
1.5195 |
0.2857 |
| 1.3043 |
21.0 |
126 |
1.1841 |
0.4048 |
| 1.2859 |
22.0 |
132 |
1.0567 |
0.5238 |
| 1.2859 |
23.0 |
138 |
1.2258 |
0.2619 |
| 1.2496 |
24.0 |
144 |
1.2372 |
0.2857 |
| 1.252 |
25.0 |
150 |
1.4386 |
0.3333 |
| 1.252 |
26.0 |
156 |
1.1416 |
0.3810 |
| 1.2296 |
27.0 |
162 |
1.0872 |
0.4286 |
| 1.2296 |
28.0 |
168 |
1.4121 |
0.2857 |
| 1.1581 |
29.0 |
174 |
1.0555 |
0.5476 |
| 1.2027 |
30.0 |
180 |
1.1296 |
0.4762 |
| 1.2027 |
31.0 |
186 |
1.2095 |
0.4048 |
| 1.1595 |
32.0 |
192 |
1.0821 |
0.4762 |
| 1.1595 |
33.0 |
198 |
1.1681 |
0.3810 |
| 1.1909 |
34.0 |
204 |
1.1147 |
0.4762 |
| 1.1121 |
35.0 |
210 |
1.0734 |
0.4048 |
| 1.1121 |
36.0 |
216 |
1.0002 |
0.5238 |
| 1.1218 |
37.0 |
222 |
1.1912 |
0.3095 |
| 1.1218 |
38.0 |
228 |
1.0883 |
0.4524 |
| 1.1024 |
39.0 |
234 |
1.1229 |
0.4286 |
| 1.0678 |
40.0 |
240 |
1.0903 |
0.4762 |
| 1.0678 |
41.0 |
246 |
1.0717 |
0.4762 |
| 1.058 |
42.0 |
252 |
1.0712 |
0.4762 |
| 1.058 |
43.0 |
258 |
1.0712 |
0.4762 |
| 1.0512 |
44.0 |
264 |
1.0712 |
0.4762 |
| 1.0743 |
45.0 |
270 |
1.0712 |
0.4762 |
| 1.0743 |
46.0 |
276 |
1.0712 |
0.4762 |
| 1.0691 |
47.0 |
282 |
1.0712 |
0.4762 |
| 1.0691 |
48.0 |
288 |
1.0712 |
0.4762 |
| 1.052 |
49.0 |
294 |
1.0712 |
0.4762 |
| 1.066 |
50.0 |
300 |
1.0712 |
0.4762 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
