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---
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-50-finetuned-student_kaggle
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: 0.4889937106918239
---
<!-- 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-50-finetuned-student_kaggle
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 34897389209777069883392.0000
- Accuracy: 0.4890
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:----------------------------:|:------:|:----:|:----------------------------:|:--------:|
| 33718936798882659565568.0000 | 0.9362 | 11 | 34897389209777069883392.0000 | 0.4890 |
| 32438469749948979085312.0000 | 1.9574 | 23 | 34897389209777069883392.0000 | 0.4890 |
| 33363246103192638849024.0000 | 2.9787 | 35 | 34897389209777069883392.0000 | 0.4890 |
| 32954207567756639862784.0000 | 4.0 | 47 | 34897389209777069883392.0000 | 0.4890 |
| 32794156842759294550016.0000 | 4.6809 | 55 | 34897389209777069883392.0000 | 0.4890 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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