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my_awesome_food_model_v2
sciarrilli/my_awesome_food_model_v2
my_awesome_food_model_v2 is a image classification model from sciarrilli. Use it when you need a label for an image. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image Classification
- Library
- transformers
- Model type
- vit
- Access
- Public
- Created
- Jul 12, 2024
- Updated
- Jul 12, 2024
- SHA
- 70c5b39e
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. -->
my_awesome_food_model_v2
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8053
- Accuracy: 0.8083
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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 4.4448 | 0.9932 | 110 | 4.4236 | 0.0914 |
| 3.8312 | 1.9955 | 221 | 3.8007 | 0.4096 |
| 3.1568 | 2.9977 | 332 | 3.1221 | 0.5435 |
| 2.4967 | 4.0 | 443 | 2.4920 | 0.6308 |
| 2.0432 | 4.9932 | 553 | 2.0252 | 0.6825 |
| 1.6512 | 5.9955 | 664 | 1.6771 | 0.7184 |
| 1.388 | 6.9977 | 775 | 1.4464 | 0.7367 |
| 1.1677 | 8.0 | 886 | 1.2782 | 0.7533 |
| 1.0307 | 8.9932 | 996 | 1.1741 | 0.7625 |
| 0.9156 | 9.9955 | 1107 | 1.0900 | 0.7741 |
| 0.8283 | 10.9977 | 1218 | 1.0295 | 0.7771 |
| 0.8078 | 12.0 | 1329 | 0.9949 | 0.7776 |
| 0.7643 | 12.9932 | 1439 | 0.9656 | 0.7817 |
| 0.6578 | 13.9955 | 1550 | 0.9274 | 0.7868 |
| 0.611 | 14.9977 | 1661 | 0.9051 | 0.7921 |
| 0.6016 | 16.0 | 1772 | 0.9009 | 0.7912 |
| 0.5652 | 16.9932 | 1882 | 0.8772 | 0.7963 |
| 0.5492 | 17.9955 | 1993 | 0.8559 | 0.7992 |
| 0.5054 | 18.9977 | 2104 | 0.8734 | 0.7956 |
| 0.5351 | 20.0 | 2215 | 0.8617 | 0.7999 |
| 0.4949 | 20.9932 | 2325 | 0.8487 | 0.8013 |
| 0.4701 | 21.9955 | 2436 | 0.8437 | 0.8013 |
| 0.4576 | 22.9977 | 2547 | 0.8430 | 0.8008 |
| 0.4573 | 24.0 | 2658 | 0.8195 | 0.8071 |
| 0.4399 | 24.9932 | 2768 | 0.8206 | 0.8071 |
| 0.424 | 25.9955 | 2879 | 0.8212 | 0.8068 |
| 0.4031 | 26.9977 | 2990 | 0.8202 | 0.8069 |
| 0.4031 | 28.0 | 3101 | 0.8173 | 0.8080 |
| 0.407 | 28.9932 | 3211 | 0.8051 | 0.8069 |
| 0.4194 | 29.7968 | 3300 | 0.8053 | 0.8083 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- Created
- Jul 12, 2024
- Updated
- Jul 12, 2024