How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-classification", model="evanrsl/facial_emotion_model")
pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification

processor = AutoImageProcessor.from_pretrained("evanrsl/facial_emotion_model")
model = AutoModelForImageClassification.from_pretrained("evanrsl/facial_emotion_model")
Quick Links

facial_emotion_model

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 1.2427
  • Accuracy: 0.5563

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: 5e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 40 1.8904 0.3125
No log 2.0 80 1.6093 0.4437
No log 3.0 120 1.4846 0.4813
No log 4.0 160 1.4352 0.5437
No log 5.0 200 1.3533 0.5
No log 6.0 240 1.3076 0.5188
No log 7.0 280 1.2484 0.55
No log 8.0 320 1.2073 0.5875
No log 9.0 360 1.2465 0.5687
No log 10.0 400 1.2770 0.5188

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.17.0
  • Tokenizers 0.15.2
Downloads last month
5
Safetensors
Model size
85.8M params
Tensor type
F32
·
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for evanrsl/facial_emotion_model

Finetuned
(2541)
this model

Evaluation results