|
--- |
|
library_name: transformers |
|
license: apache-2.0 |
|
base_model: microsoft/resnet-50 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: Resneteau-50-2024_09_23-batch-size32_freeze |
|
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. --> |
|
|
|
# Resneteau-50-2024_09_23-batch-size32_freeze |
|
|
|
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1906 |
|
- F1 Micro: 0.6954 |
|
- F1 Macro: 0.4462 |
|
- Accuracy: 0.1827 |
|
- Learning Rate: 0.0001 |
|
|
|
## 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 |
|
- num_epochs: 400 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Accuracy | Rate | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:------:| |
|
| No log | 1.0 | 273 | 0.2460 | 0.5802 | 0.2267 | 0.0877 | 0.001 | |
|
| 0.2786 | 2.0 | 546 | 0.2217 | 0.6412 | 0.3160 | 0.1369 | 0.001 | |
|
| 0.2786 | 3.0 | 819 | 0.2117 | 0.6596 | 0.3581 | 0.1486 | 0.001 | |
|
| 0.231 | 4.0 | 1092 | 0.2049 | 0.6674 | 0.3831 | 0.1618 | 0.001 | |
|
| 0.231 | 5.0 | 1365 | 0.2016 | 0.6707 | 0.3965 | 0.1677 | 0.001 | |
|
| 0.2206 | 6.0 | 1638 | 0.2002 | 0.6720 | 0.4076 | 0.1677 | 0.001 | |
|
| 0.2206 | 7.0 | 1911 | 0.1976 | 0.6752 | 0.4142 | 0.1746 | 0.001 | |
|
| 0.2157 | 8.0 | 2184 | 0.1971 | 0.6824 | 0.4281 | 0.1764 | 0.001 | |
|
| 0.2157 | 9.0 | 2457 | 0.1961 | 0.6845 | 0.4300 | 0.1764 | 0.001 | |
|
| 0.2127 | 10.0 | 2730 | 0.1944 | 0.6763 | 0.4264 | 0.1805 | 0.001 | |
|
| 0.2117 | 11.0 | 3003 | 0.1940 | 0.6902 | 0.4391 | 0.1781 | 0.001 | |
|
| 0.2117 | 12.0 | 3276 | 0.1945 | 0.6939 | 0.4523 | 0.1729 | 0.001 | |
|
| 0.2107 | 13.0 | 3549 | 0.1936 | 0.6908 | 0.4461 | 0.1795 | 0.001 | |
|
| 0.2107 | 14.0 | 3822 | 0.1931 | 0.6916 | 0.4424 | 0.1781 | 0.001 | |
|
| 0.2105 | 15.0 | 4095 | 0.1935 | 0.6936 | 0.4431 | 0.1809 | 0.001 | |
|
| 0.2105 | 16.0 | 4368 | 0.1931 | 0.6896 | 0.4429 | 0.1805 | 0.001 | |
|
| 0.2086 | 17.0 | 4641 | 0.1931 | 0.6953 | 0.4411 | 0.1819 | 0.001 | |
|
| 0.2086 | 18.0 | 4914 | 0.1908 | 0.6984 | 0.4490 | 0.1857 | 0.001 | |
|
| 0.2101 | 19.0 | 5187 | 0.1925 | 0.6879 | 0.4428 | 0.1812 | 0.001 | |
|
| 0.2101 | 20.0 | 5460 | 0.1913 | 0.6797 | 0.4357 | 0.1774 | 0.001 | |
|
| 0.2088 | 21.0 | 5733 | 0.1915 | 0.6958 | 0.4381 | 0.1823 | 0.001 | |
|
| 0.2084 | 22.0 | 6006 | 0.1919 | 0.7039 | 0.4535 | 0.1826 | 0.001 | |
|
| 0.2084 | 23.0 | 6279 | 0.1926 | 0.6907 | 0.4363 | 0.1798 | 0.001 | |
|
| 0.2083 | 24.0 | 6552 | 0.1919 | 0.6953 | 0.4544 | 0.1805 | 0.001 | |
|
| 0.2083 | 25.0 | 6825 | 0.1919 | 0.6962 | 0.4466 | 0.1781 | 0.0001 | |
|
| 0.2076 | 26.0 | 7098 | 0.1912 | 0.6943 | 0.4418 | 0.1823 | 0.0001 | |
|
| 0.2076 | 27.0 | 7371 | 0.1912 | 0.6972 | 0.4500 | 0.1809 | 0.0001 | |
|
| 0.2081 | 28.0 | 7644 | 0.1915 | 0.6944 | 0.4454 | 0.1857 | 0.0001 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.44.2 |
|
- Pytorch 2.4.1+cu121 |
|
- Datasets 3.0.0 |
|
- Tokenizers 0.19.1 |
|
|
