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---
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- code
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: my__model
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: 0.44188861985472155
pipeline_tag: image-classification
---
<!-- 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. -->
# my__model
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
with specialised focus on kneeosteoarthritis data.
It achieves the following results on the evaluation set:
- Loss: 1.3439
- Accuracy: 0.4419
## Model description
model built to refine the classification with specialised focus on kneeosteoarthritis data.
for medical data related to similar domains can use the same to finetune further.
## Intended uses & limitations
More information needed
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- 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: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3665 | 1.0 | 104 | 1.3439 | 0.4419 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1 |