metadata
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-50-finetuned
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: 0.199
resnet-50-finetuned
This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 2.2724
- Accuracy: 0.199
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.3021 | 0.14 | 10 | 2.2994 | 0.112 |
| 2.2929 | 0.28 | 20 | 2.2911 | 0.137 |
| 2.2875 | 0.43 | 30 | 2.2848 | 0.151 |
| 2.2824 | 0.57 | 40 | 2.2812 | 0.175 |
| 2.2792 | 0.71 | 50 | 2.2758 | 0.191 |
| 2.2766 | 0.85 | 60 | 2.2726 | 0.197 |
| 2.2765 | 0.99 | 70 | 2.2724 | 0.199 |
Framework versions
- Transformers 4.31.0
- Pytorch 1.10.1+cu111
- Datasets 2.14.6
- Tokenizers 0.13.3