File size: 2,071 Bytes
a0d5193 dcc9185 a0d5193 dcc9185 a0d5193 dcc9185 a0d5193 |
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 |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_food_model
results: []
---
<!-- 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. -->
# my_awesome_food_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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8640
- Accuracy: 0.573
## 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: 512
- eval_batch_size: 512
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| No log | 1.0 | 2 | 0.036 | 4.5210 |
| No log | 2.0 | 4 | 0.278 | 4.4151 |
| No log | 3.0 | 6 | 0.437 | 4.3629 |
| No log | 4.0 | 8 | 4.2960 | 0.547 |
| 4.3122 | 5.0 | 10 | 4.1697 | 0.589 |
| 4.3122 | 6.0 | 12 | 4.0601 | 0.568 |
| 4.3122 | 7.0 | 14 | 3.9770 | 0.521 |
| 4.3122 | 8.0 | 16 | 3.9177 | 0.539 |
| 4.3122 | 9.0 | 18 | 3.8843 | 0.545 |
| 3.9792 | 10.0 | 20 | 3.8640 | 0.573 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|