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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
- lewtun/dog_food
metrics:
- accuracy
model-index:
- name: resnet-18-finetuned-dogfood
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: lewtun/dog_food
type: lewtun/dog_food
args: lewtun--dog_food
metrics:
- name: Accuracy
type: accuracy
value: 0.896
- task:
type: image-classification
name: Image Classification
dataset:
name: lewtun/dog_food
type: lewtun/dog_food
config: lewtun--dog_food
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.8466666666666667
verified: true
- name: Precision Macro
type: precision
value: 0.8850127293141284
verified: true
- name: Precision Micro
type: precision
value: 0.8466666666666667
verified: true
- name: Precision Weighted
type: precision
value: 0.8939157698241645
verified: true
- name: Recall Macro
type: recall
value: 0.8555113273379528
verified: true
- name: Recall Micro
type: recall
value: 0.8466666666666667
verified: true
- name: Recall Weighted
type: recall
value: 0.8466666666666667
verified: true
- name: F1 Macro
type: f1
value: 0.8431399312051647
verified: true
- name: F1 Micro
type: f1
value: 0.8466666666666667
verified: true
- name: F1 Weighted
type: f1
value: 0.8430272582865614
verified: true
- name: loss
type: loss
value: 0.3633290231227875
verified: true
- name: matthews_correlation
type: matthews_correlation
value: 0.7973101366252381
verified: true
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# resnet-18-finetuned-dogfood
This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the lewtun/dog_food dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2991
- Accuracy: 0.896
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.846 | 1.0 | 16 | 0.2662 | 0.9156 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|