metadata
license: apache-2.0
base_model: microsoft/resnet-152
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Resnet152-5e-5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7614314115308151
Resnet152-5e-5
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: 0.8255
- Accuracy: 0.7614
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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 3.2352 | 1.0 | 275 | 2.9196 | 0.1984 |
| 2.5896 | 2.0 | 550 | 1.9631 | 0.4736 |
| 1.8864 | 3.0 | 825 | 1.3420 | 0.6231 |
| 1.5969 | 4.0 | 1100 | 1.1232 | 0.6918 |
| 1.465 | 5.0 | 1375 | 0.9717 | 0.7213 |
| 1.371 | 6.0 | 1650 | 0.9014 | 0.7483 |
| 1.2795 | 7.0 | 1925 | 0.8566 | 0.7491 |
| 1.2448 | 8.0 | 2200 | 0.8272 | 0.7594 |
| 1.2234 | 9.0 | 2475 | 0.8145 | 0.7630 |
| 1.2143 | 10.0 | 2750 | 0.8255 | 0.7614 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2