Downloads · 30 days
1
0% of all-time downloads
emotion_recognition
krismp/emotion_recognition
emotion_recognition is a image classification model from krismp. 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
452
Public
Repo size
2.7 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin343 MB · 100%
At a glance
- Task
- Image Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- vit
- Access
- Public
- Created
- Sep 19, 2023
- Updated
- Sep 19, 2023
- SHA
- c5b1785c
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. -->
emotion_recognition
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: 1.3469
- Accuracy: 0.175
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 10 | 2.0721 | 0.125 |
| No log | 2.0 | 20 | 2.0633 | 0.125 |
| No log | 3.0 | 30 | 2.0038 | 0.125 |
| No log | 4.0 | 40 | 1.9097 | 0.125 |
| No log | 5.0 | 50 | 1.7412 | 0.125 |
| No log | 6.0 | 60 | 1.6189 | 0.05 |
| No log | 7.0 | 70 | 1.5343 | 0.0375 |
| No log | 8.0 | 80 | 1.4746 | 0.0688 |
| No log | 9.0 | 90 | 1.4330 | 0.0938 |
| No log | 10.0 | 100 | 1.4130 | 0.15 |
| No log | 11.0 | 110 | 1.3735 | 0.1062 |
| No log | 12.0 | 120 | 1.3516 | 0.1062 |
| No log | 13.0 | 130 | 1.2838 | 0.1375 |
| No log | 14.0 | 140 | 1.3058 | 0.1187 |
| No log | 15.0 | 150 | 1.3116 | 0.1 |
| No log | 16.0 | 160 | 1.3269 | 0.1313 |
| No log | 17.0 | 170 | 1.2624 | 0.1062 |
| No log | 18.0 | 180 | 1.3285 | 0.1187 |
| No log | 19.0 | 190 | 1.3490 | 0.1437 |
| No log | 20.0 | 200 | 1.2592 | 0.1375 |
| No log | 21.0 | 210 | 1.3600 | 0.0938 |
| No log | 22.0 | 220 | 1.2835 | 0.1313 |
| No log | 23.0 | 230 | 1.2842 | 0.1375 |
| No log | 24.0 | 240 | 1.2840 | 0.1 |
| No log | 25.0 | 250 | 1.2456 | 0.1313 |
| No log | 26.0 | 260 | 1.2960 | 0.1562 |
| No log | 27.0 | 270 | 1.3208 | 0.1375 |
| No log | 28.0 | 280 | 1.3207 | 0.1375 |
| No log | 29.0 | 290 | 1.2892 | 0.175 |
| No log | 30.0 | 300 | 1.2837 | 0.1812 |
| No log | 31.0 | 310 | 1.3548 | 0.1562 |
| No log | 32.0 | 320 | 1.4371 | 0.1437 |
| No log | 33.0 | 330 | 1.4219 | 0.1562 |
| No log | 34.0 | 340 | 1.4033 | 0.1875 |
| No log | 35.0 | 350 | 1.4505 | 0.1437 |
| No log | 36.0 | 360 | 1.2975 | 0.1562 |
| No log | 37.0 | 370 | 1.3906 | 0.1562 |
| No log | 38.0 | 380 | 1.3547 | 0.1688 |
| No log | 39.0 | 390 | 1.4706 | 0.1938 |
| No log | 40.0 | 400 | 1.3595 | 0.1625 |
| No log | 41.0 | 410 | 1.4236 | 0.1625 |
| No log | 42.0 | 420 | 1.4180 | 0.1812 |
| No log | 43.0 | 430 | 1.3993 | 0.1562 |
| No log | 44.0 | 440 | 1.4066 | 0.1625 |
| No log | 45.0 | 450 | 1.3760 | 0.175 |
| No log | 46.0 | 460 | 1.4221 | 0.1812 |
| No log | 47.0 | 470 | 1.3772 | 0.1625 |
| No log | 48.0 | 480 | 1.4265 | 0.2 |
| No log | 49.0 | 490 | 1.4716 | 0.1625 |
| 0.6962 | 50.0 | 500 | 1.3917 | 0.1625 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Sep 19, 2023
- Updated
- Sep 19, 2023