metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-152-finetuned_resnet152-adam-optimizere-2-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: 0.8980952380952381
resnet-152-finetuned_resnet152-adam-optimizere-2-autotags
This model is a fine-tuned version of microsoft/resnet-152 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.4368
- Accuracy: 0.8981
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: 0.01
- 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 |
|---|---|---|---|---|
| 1.4424 | 0.99 | 65 | 1.7123 | 0.56 |
| 1.6053 | 1.99 | 130 | 2.0613 | 0.3152 |
| 1.3795 | 2.99 | 195 | 1.3791 | 0.5552 |
| 0.9701 | 3.99 | 260 | 0.9195 | 0.7038 |
| 0.8258 | 4.99 | 325 | 0.9107 | 0.7067 |
| 0.7619 | 5.99 | 390 | 0.9915 | 0.6867 |
| 0.6241 | 6.99 | 455 | 0.7895 | 0.76 |
| 0.497 | 7.99 | 520 | 0.6616 | 0.8038 |
| 0.4709 | 8.99 | 585 | 0.5282 | 0.8543 |
| 0.394 | 9.99 | 650 | 0.5447 | 0.8429 |
| 0.343 | 10.99 | 715 | 0.5108 | 0.8486 |
| 0.3482 | 11.99 | 780 | 0.5224 | 0.8505 |
| 0.2576 | 12.99 | 845 | 0.4796 | 0.8743 |
| 0.1837 | 13.99 | 910 | 0.5008 | 0.8571 |
| 0.1904 | 14.99 | 975 | 0.4366 | 0.8790 |
| 0.1458 | 15.99 | 1040 | 0.4320 | 0.8990 |
| 0.1575 | 16.99 | 1105 | 0.4059 | 0.8952 |
| 0.0992 | 17.99 | 1170 | 0.4362 | 0.8952 |
| 0.0858 | 18.99 | 1235 | 0.4210 | 0.8971 |
| 0.0704 | 19.99 | 1300 | 0.4368 | 0.8981 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2