Librarian Bot: Add base_model information to model

7f0b5b2 over 2 years ago

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language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - f1
  - recall
  - precision
base_model: google/vit-base-patch16-224-in21k
model-index:
  - name: vit-base-patch16-224-in21k_Human_Activity_Recognition
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - type: accuracy
            value: 0.8380952380952381
            name: Accuracy

vit-base-patch16-224-in21k_Human_Activity_Recognition

This model is a fine-tuned version of google/vit-base-patch16-224-in21k.

It achieves the following results on the evaluation set:

Loss: 0.7403
Accuracy: 0.8381
F1
- Weighted: 0.8388
- Micro: 0.8381
- Macro: 0.8394
Recall
- Weighted: 0.8381
- Micro: 0.8381
- Macro: 0.8390
Precision
- Weighted: 0.8421
- Micro: 0.8381
- Macro: 0.8424

Model description

This is a multiclass image classification model of humans doing different activities.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Human%20Activity%20Recognition/ViT-Human%20Action_Recogniton.ipynb

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology. You are welcome to test and experiment with this model, but it is at your own risk/peril.

Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/meetnagadia/human-action-recognition-har-dataset

Sample Images From Dataset:

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Weighted F1	Micro F1	Macro F1	Weighted Recall	Micro Recall	Macro Recall	Weighted Precision	Micro Precision	Macro Precision
1.0814	1.0	630	0.7368	0.7794	0.7795	0.7794	0.7798	0.7794	0.7794	0.7797	0.7896	0.7794	0.7896
0.5149	2.0	1260	0.6439	0.8060	0.8049	0.8060	0.8036	0.8060	0.8060	0.8051	0.8136	0.8060	0.8130
0.3023	3.0	1890	0.7026	0.8254	0.8272	0.8254	0.8278	0.8254	0.8254	0.8256	0.8335	0.8254	0.8345
0.0507	4.0	2520	0.7414	0.8317	0.8342	0.8317	0.8348	0.8317	0.8317	0.8321	0.8427	0.8317	0.8438
0.0128	5.0	3150	0.7403	0.8381	0.8388	0.8381	0.8394	0.8381	0.8381	0.8390	0.8421	0.8381	0.8424

Framework versions

Transformers 4.25.1
Pytorch 1.12.1
Datasets 2.8.0
Tokenizers 0.12.1