metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-101-finetuned_resnet101-cnn-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: 0.9228571428571428
resnet-101-finetuned_resnet101-cnn-autotags
This model is a fine-tuned version of microsoft/resnet-101 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.2099
- Accuracy: 0.9229
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: 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.0434 | 0.99 | 65 | 1.6045 | 0.4829 |
| 0.9013 | 1.99 | 130 | 0.6946 | 0.7648 |
| 0.7097 | 2.99 | 195 | 0.4928 | 0.8295 |
| 0.4386 | 3.99 | 260 | 0.3632 | 0.8610 |
| 0.4261 | 4.99 | 325 | 0.3269 | 0.8838 |
| 0.3181 | 5.99 | 390 | 0.2790 | 0.9010 |
| 0.2349 | 6.99 | 455 | 0.2377 | 0.9190 |
| 0.1615 | 7.99 | 520 | 0.2416 | 0.9114 |
| 0.1146 | 8.99 | 585 | 0.2162 | 0.9219 |
| 0.1254 | 9.99 | 650 | 0.2099 | 0.9229 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2