Downloads · 30 days
11
12% of all-time downloads
vit-emotion-classifier
vissutagunawan/vit-emotion-classifier
vit-emotion-classifier is a image classification model from vissutagunawan. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
91
Public
Parameters
85.8M
24.7 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors343 MB · 100%
At a glance
- Task
- Image Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- vit
- Access
- Public
- Created
- Feb 24, 2025
- Updated
- Feb 24, 2025
- SHA
- 04eb5c8a
Base models
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
vit-emotion-classifier
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: 1.3506
- Accuracy: 0.525
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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 25
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 20 | 2.0656 | 0.1938 |
| No log | 2.0 | 40 | 2.0408 | 0.2625 |
| No log | 3.0 | 60 | 1.9845 | 0.275 |
| No log | 4.0 | 80 | 1.8774 | 0.35 |
| 1.9717 | 5.0 | 100 | 1.7409 | 0.45 |
| 1.9717 | 6.0 | 120 | 1.6349 | 0.4437 |
| 1.9717 | 7.0 | 140 | 1.5541 | 0.4437 |
| 1.9717 | 8.0 | 160 | 1.5007 | 0.5188 |
| 1.9717 | 9.0 | 180 | 1.4531 | 0.525 |
| 1.4968 | 10.0 | 200 | 1.4263 | 0.5312 |
| 1.4968 | 11.0 | 220 | 1.3975 | 0.5188 |
| 1.4968 | 12.0 | 240 | 1.3915 | 0.525 |
| 1.4968 | 13.0 | 260 | 1.3270 | 0.5375 |
| 1.4968 | 14.0 | 280 | 1.3360 | 0.575 |
| 1.2146 | 15.0 | 300 | 1.3185 | 0.5437 |
| 1.2146 | 16.0 | 320 | 1.3288 | 0.55 |
| 1.2146 | 17.0 | 340 | 1.3262 | 0.5563 |
| 1.2146 | 18.0 | 360 | 1.3142 | 0.55 |
| 1.2146 | 19.0 | 380 | 1.2982 | 0.5625 |
| 1.0644 | 20.0 | 400 | 1.2704 | 0.5625 |
| 1.0644 | 21.0 | 420 | 1.2862 | 0.55 |
| 1.0644 | 22.0 | 440 | 1.2941 | 0.55 |
| 1.0644 | 23.0 | 460 | 1.2876 | 0.5312 |
| 1.0644 | 24.0 | 480 | 1.3066 | 0.5625 |
| 1.0161 | 25.0 | 500 | 1.2734 | 0.55 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Feb 24, 2025
- Updated
- Feb 24, 2025