|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- imagefolder |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: resnet-101-finetuned_resnet101-sgd-optimizer20-autotags |
|
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: 0.8847619047619047 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# resnet-101-finetuned_resnet101-sgd-optimizer20-autotags |
|
|
|
This model is a fine-tuned version of [microsoft/resnet-101](https://huggingface.co/microsoft/resnet-101) on the imagefolder dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3318 |
|
- Accuracy: 0.8848 |
|
|
|
## 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: 0.1 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 64 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_ratio: 0.1 |
|
- num_epochs: 20 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 1.1302 | 0.99 | 65 | 1.0040 | 0.6724 | |
|
| 1.1708 | 1.99 | 130 | 1.4856 | 0.5495 | |
|
| 1.141 | 2.99 | 195 | 1.1486 | 0.6352 | |
|
| 1.0119 | 3.99 | 260 | 0.8829 | 0.7314 | |
|
| 0.8091 | 4.99 | 325 | 0.8301 | 0.7419 | |
|
| 0.7878 | 5.99 | 390 | 0.8121 | 0.7333 | |
|
| 0.6827 | 6.99 | 455 | 0.6047 | 0.7990 | |
|
| 0.5525 | 7.99 | 520 | 0.6028 | 0.8048 | |
|
| 0.5787 | 8.99 | 585 | 0.5183 | 0.8352 | |
|
| 0.4797 | 9.99 | 650 | 0.4737 | 0.8543 | |
|
| 0.4224 | 10.99 | 715 | 0.4943 | 0.8305 | |
|
| 0.4389 | 11.99 | 780 | 0.4162 | 0.8629 | |
|
| 0.4142 | 12.99 | 845 | 0.4000 | 0.8629 | |
|
| 0.3144 | 13.99 | 910 | 0.3833 | 0.8695 | |
|
| 0.2915 | 14.99 | 975 | 0.3688 | 0.8733 | |
|
| 0.3302 | 15.99 | 1040 | 0.3643 | 0.8810 | |
|
| 0.2954 | 16.99 | 1105 | 0.3446 | 0.8867 | |
|
| 0.2186 | 17.99 | 1170 | 0.3571 | 0.8905 | |
|
| 0.1812 | 18.99 | 1235 | 0.3334 | 0.8886 | |
|
| 0.1911 | 19.99 | 1300 | 0.3318 | 0.8848 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.25.1 |
|
- Pytorch 1.13.1+cu117 |
|
- Datasets 2.11.0 |
|
- Tokenizers 0.13.2 |
|
|
