README.md

metadata

tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: dit-base-Tobacco_Dataset_v3
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: data
          split: train
          args: data
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9418521177315147
language:
  - en
pipeline_tag: image-classification

dit-base-Tobacco_Dataset_v3

This model is a fine-tuned version of microsoft/dit-base on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.1958
• Accuracy: 0.9419
• F1
  • Weighted: 0.9403
  • Micro: 0.9419
  • Macro: 0.9278

Model description

For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Document%20AI/Multiclass%20Classification/Tobacco-Related%20Documents/Tobacco%20Dataset%20%26%20DiT%20Transformer%20Project_v3.ipynb

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/patrickaudriaz/tobacco3482jpg

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: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Weighted F1	Micro F1	Macro F1
1.9273	0.98	43	1.1368	0.5987	0.5462	0.5987	0.5175
1.0685	1.98	86	0.5244	0.8248	0.7939	0.8248	0.7670
0.7373	2.98	129	0.3631	0.8808	0.8610	0.8808	0.8318
0.641	3.98	172	0.2884	0.9045	0.8967	0.9045	0.8732
0.5579	4.98	215	0.2192	0.9361	0.9338	0.9361	0.9214
0.5279	5.98	258	0.2292	0.9289	0.9263	0.9289	0.9137
0.4918	6.98	301	0.2052	0.9368	0.9348	0.9368	0.9218
0.4723	7.98	344	0.1958	0.9419	0.9403	0.9419	0.9278

Framework versions

• Transformers 4.26.1
• Pytorch 1.12.1
• Datasets 2.9.0
• Tokenizers 0.12.1