
# ConvNeXt for Face Mask Detection

ConvNeXt model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao et al.  

## Training Metrics
    epoch                    =         3.54
    total_flos               = 1195651761GF
    train_loss               =       0.0079
    train_runtime            =   1:08:20.25
    train_samples_per_second =       14.075
    train_steps_per_second   =         0.22

---

## Evaluation Metrics
    epoch                   =       3.54
    eval_accuracy           =     0.9961
    eval_loss               =     0.0151
    eval_runtime            = 0:01:23.47
    eval_samples_per_second =     43.079
    eval_steps_per_second   =      5.391
{"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["Face-Mask18K"]}
transformers pytorch safetensors convnext image-classification dataset:Face-Mask18K license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #convnext #image-classification #dataset-Face-Mask18K #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 


# ConvNeXt for Face Mask Detection

ConvNeXt model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao et al.  

## Training Metrics
    epoch                    =         3.54
    total_flos               = 1195651761GF
    train_loss               =       0.0079
    train_runtime            =   1:08:20.25
    train_samples_per_second =       14.075
    train_steps_per_second   =         0.22

---

## Evaluation Metrics
    epoch                   =       3.54
    eval_accuracy           =     0.9961
    eval_loss               =     0.0151
    eval_runtime            = 0:01:23.47
    eval_samples_per_second =     43.079
    eval_steps_per_second   =      5.391
# ConvNeXt for Face Mask Detection

ConvNeXt model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao et al. ## Training Metrics
    epoch                    =         3.54
    total_flos               = 1195651761GF
    train_loss               =       0.0079
    train_runtime            =   1:08:20.25
    train_samples_per_second =       14.075
    train_steps_per_second   =         0.22

--- ## Evaluation Metrics
    epoch                   =       3.54
    eval_accuracy           =     0.9961
    eval_loss               =     0.0151
    eval_runtime            = 0:01:23.47
    eval_samples_per_second =     43.079
    eval_steps_per_second   =      5.391
TAGS
#transformers #pytorch #safetensors #convnext #image-classification #dataset-Face-Mask18K #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # ConvNeXt for Face Mask Detection

ConvNeXt model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao et al. ## Training Metrics
    epoch                    =         3.54
    total_flos               = 1195651761GF
    train_loss               =       0.0079
    train_runtime            =   1:08:20.25
    train_samples_per_second =       14.075
    train_steps_per_second   =         0.22

--- ## Evaluation Metrics
    epoch                   =       3.54
    eval_accuracy           =     0.9961
    eval_loss               =     0.0151
    eval_runtime            = 0:01:23.47
    eval_samples_per_second =     43.079
    eval_steps_per_second   =      5.391
passage: TAGS
#transformers #pytorch #safetensors #convnext #image-classification #dataset-Face-Mask18K #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# ConvNeXt for Face Mask Detection

ConvNeXt model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao et al.## Training Metrics
    epoch                    =         3.54
    total_flos               = 1195651761GF
    train_loss               =       0.0079
    train_runtime            =   1:08:20.25
    train_samples_per_second =       14.075
    train_steps_per_second   =         0.22

---## Evaluation Metrics
    epoch                   =       3.54
    eval_accuracy           =     0.9961
    eval_loss               =     0.0151
    eval_runtime            = 0:01:23.47
    eval_samples_per_second =     43.079
    eval_steps_per_second   =      5.391