README.md

metadata

license: apache-2.0
base_model: microsoft/resnet-50
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: resnet-50-finetuned-FBark
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.9699498746867168
          - name: Recall
            type: recall
            value: 0.9778787878787879
          - name: F1
            type: f1
            value: 0.9734665458141067
          - name: Accuracy
            type: accuracy
            value: 0.9719626168224299

resnet-50-finetuned-FBark

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: 0.1079
• Precision: 0.9699
• Recall: 0.9779
• F1: 0.9735
• Accuracy: 0.9720

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: 0.0001
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 35

Training results

Framework versions

• Transformers 4.39.3
• Pytorch 2.2.0+cpu
• Datasets 2.19.0
• Tokenizers 0.15.1