README.md

metadata

license: apache-2.0
tags:
  - image-classification
  - vision
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: outputs
    results:
      - task:
          name: Image Classification
          type: image-classification
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9107332624867163

outputs

This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the PETA dataset dataset. It achieves the following results on the evaluation set:

• Loss: 0.2170
• Accuracy: 0.9107

Model description

More information needed

How to use

You can use this model with Transformers pipeline .

from transformers import pipeline
gender_classifier = pipeline(model="NTQAI/pedestrian_gender_recognition")
image_path = "abc.jpg"

results = gender_classifier(image_path)
print(results)

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: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 1337
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5193	1.0	2000	0.3346	0.8533
0.337	2.0	4000	0.2892	0.8778
0.3771	3.0	6000	0.2493	0.8969
0.3819	4.0	8000	0.2275	0.9100
0.3581	5.0	10000	0.2170	0.9107

Framework versions

• Transformers 4.24.0.dev0
• Pytorch 1.12.1+cu113
• Datasets 2.6.1
• Tokenizers 0.13.1

Contact information

For personal communication related to this project, please contact Nha Nguyen Van (nha282@gmail.com).