metadata
license: apache-2.0
base_model: microsoft/resnet-101
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-101-CivilEng11k_3Classes
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: 1
resnet-101-CivilEng11k_3Classes
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: 0.0587
- Accuracy: 1.0
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: 0.0002
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.0885 | 1.0 | 37 | 0.8955 | 0.4305 |
| 0.6832 | 2.0 | 74 | 0.4990 | 0.8475 |
| 0.2591 | 3.0 | 111 | 0.0587 | 1.0 |
| 0.024 | 4.0 | 148 | 0.0026 | 1.0 |
| 0.005 | 5.0 | 185 | 0.0007 | 1.0 |
| 0.0121 | 6.0 | 222 | 0.0005 | 1.0 |
| 0.0214 | 7.0 | 259 | 0.0003 | 1.0 |
| 0.0035 | 8.0 | 296 | 0.0002 | 1.0 |
| 0.0026 | 9.0 | 333 | 0.0002 | 1.0 |
| 0.0054 | 10.0 | 370 | 0.0002 | 1.0 |
Framework versions
- Transformers 4.37.2
- Pytorch 1.12.1
- Datasets 2.18.0
- Tokenizers 0.15.1