metadata
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: msi-resnet-18
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: 0.6217151244059268
- name: F1
type: f1
value: 0.5152478617168957
- name: Precision
type: precision
value: 0.5801734570391287
- name: Recall
type: recall
value: 0.4633910592025775
msi-resnet-18
This model was trained from scratch on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.6730
- Accuracy: 0.6217
- F1: 0.5152
- Precision: 0.5802
- Recall: 0.4634
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: 1e-06
- 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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.5705 | 1.0 | 2015 | 0.6879 | 0.5897 | 0.4460 | 0.5384 | 0.3807 |
| 0.5309 | 2.0 | 4031 | 0.6788 | 0.6091 | 0.4859 | 0.5657 | 0.4258 |
| 0.5263 | 3.0 | 6047 | 0.7020 | 0.6036 | 0.4322 | 0.5709 | 0.3477 |
| 0.496 | 4.0 | 8060 | 0.6730 | 0.6217 | 0.5152 | 0.5802 | 0.4634 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0