update model card README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,76 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
tags:
|
| 4 |
+
- generated_from_trainer
|
| 5 |
+
metrics:
|
| 6 |
+
- accuracy
|
| 7 |
+
model-index:
|
| 8 |
+
- name: exper4_mesum5
|
| 9 |
+
results: []
|
| 10 |
+
---
|
| 11 |
+
|
| 12 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| 13 |
+
should probably proofread and complete it, then remove this comment. -->
|
| 14 |
+
|
| 15 |
+
# exper4_mesum5
|
| 16 |
+
|
| 17 |
+
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 None dataset.
|
| 18 |
+
It achieves the following results on the evaluation set:
|
| 19 |
+
- Loss: 3.4389
|
| 20 |
+
- Accuracy: 0.1331
|
| 21 |
+
|
| 22 |
+
## Model description
|
| 23 |
+
|
| 24 |
+
More information needed
|
| 25 |
+
|
| 26 |
+
## Intended uses & limitations
|
| 27 |
+
|
| 28 |
+
More information needed
|
| 29 |
+
|
| 30 |
+
## Training and evaluation data
|
| 31 |
+
|
| 32 |
+
More information needed
|
| 33 |
+
|
| 34 |
+
## Training procedure
|
| 35 |
+
|
| 36 |
+
### Training hyperparameters
|
| 37 |
+
|
| 38 |
+
The following hyperparameters were used during training:
|
| 39 |
+
- learning_rate: 2e-05
|
| 40 |
+
- train_batch_size: 16
|
| 41 |
+
- eval_batch_size: 8
|
| 42 |
+
- seed: 42
|
| 43 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
| 44 |
+
- lr_scheduler_type: linear
|
| 45 |
+
- num_epochs: 4
|
| 46 |
+
- mixed_precision_training: Native AMP
|
| 47 |
+
|
| 48 |
+
### Training results
|
| 49 |
+
|
| 50 |
+
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|
| 51 |
+
|:-------------:|:-----:|:----:|:---------------:|:--------:|
|
| 52 |
+
| 3.3793 | 0.23 | 100 | 3.4527 | 0.1308 |
|
| 53 |
+
| 3.2492 | 0.47 | 200 | 3.4501 | 0.1331 |
|
| 54 |
+
| 3.3847 | 0.7 | 300 | 3.4500 | 0.1272 |
|
| 55 |
+
| 3.3739 | 0.93 | 400 | 3.4504 | 0.1320 |
|
| 56 |
+
| 3.4181 | 1.16 | 500 | 3.4452 | 0.1320 |
|
| 57 |
+
| 3.214 | 1.4 | 600 | 3.4503 | 0.1320 |
|
| 58 |
+
| 3.282 | 1.63 | 700 | 3.4444 | 0.1325 |
|
| 59 |
+
| 3.5308 | 1.86 | 800 | 3.4473 | 0.1337 |
|
| 60 |
+
| 3.2251 | 2.09 | 900 | 3.4415 | 0.1361 |
|
| 61 |
+
| 3.4385 | 2.33 | 1000 | 3.4408 | 0.1343 |
|
| 62 |
+
| 3.3702 | 2.56 | 1100 | 3.4406 | 0.1325 |
|
| 63 |
+
| 3.366 | 2.79 | 1200 | 3.4411 | 0.1355 |
|
| 64 |
+
| 3.2022 | 3.02 | 1300 | 3.4403 | 0.1308 |
|
| 65 |
+
| 3.2768 | 3.26 | 1400 | 3.4394 | 0.1320 |
|
| 66 |
+
| 3.3444 | 3.49 | 1500 | 3.4394 | 0.1314 |
|
| 67 |
+
| 3.2981 | 3.72 | 1600 | 3.4391 | 0.1331 |
|
| 68 |
+
| 3.3349 | 3.95 | 1700 | 3.4389 | 0.1331 |
|
| 69 |
+
|
| 70 |
+
|
| 71 |
+
### Framework versions
|
| 72 |
+
|
| 73 |
+
- Transformers 4.20.1
|
| 74 |
+
- Pytorch 1.12.0+cu113
|
| 75 |
+
- Datasets 2.3.2
|
| 76 |
+
- Tokenizers 0.12.1
|