metadata
license: apache-2.0
base_model: microsoft/resnet-101
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-101-finetuned-CivilEng11k-newDS
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: 0.9932203389830508
resnet-101-finetuned-CivilEng11k-newDS
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: 0.4541
- Accuracy: 0.9932
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
- gradient_accumulation_steps: 20
- total_train_batch_size: 640
- 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 |
|---|---|---|---|---|
| No log | 0.54 | 1 | 1.0986 | 0.4136 |
| No log | 1.62 | 3 | 1.0295 | 0.4339 |
| No log | 2.7 | 5 | 0.8537 | 0.4339 |
| No log | 3.78 | 7 | 0.6785 | 0.4441 |
| No log | 4.86 | 9 | 0.6141 | 0.6576 |
| No log | 5.95 | 11 | 0.5794 | 0.7559 |
| No log | 6.49 | 12 | 0.5616 | 0.8034 |
| No log | 7.57 | 14 | 0.5304 | 0.8475 |
| No log | 8.65 | 16 | 0.4964 | 0.9492 |
| No log | 9.73 | 18 | 0.4680 | 0.9864 |
| 0.6919 | 10.81 | 20 | 0.4541 | 0.9932 |
Framework versions
- Transformers 4.37.2
- Pytorch 1.12.1
- Datasets 2.18.0
- Tokenizers 0.15.1