Downloads · 30 days
12
1% of all-time downloads
vit-base-patch16-224-in21k-shiba-inu-detector
domluna/vit-base-patch16-224-in21k-shiba-inu-detector
vit-base-patch16-224-in21k-shiba-inu-detector is a image classification model from domluna. 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
1.5K
Public
Repo size
1.7 GB
Likes
1
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
- Apr 17, 2022
- Updated
- Apr 25, 2022
- SHA
- 30179188
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-base-patch16-224-in21k-shiba-inu-detector
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on dataset with 4 dog types including Shiba Inu.
It achieves the following results on the evaluation set:
- Loss: 0.6511
- Accuracy: 1.0
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.94 | 4 | 1.3875 | 0.1667 |
| No log | 1.94 | 8 | 1.2712 | 0.7833 |
| 1.4176 | 2.94 | 12 | 1.0972 | 0.9 |
| 1.4176 | 3.94 | 16 | 0.9365 | 0.95 |
| 1.0144 | 4.94 | 20 | 0.7836 | 0.9833 |
| 1.0144 | 5.94 | 24 | 0.6511 | 1.0 |
| 1.0144 | 6.94 | 28 | 0.5329 | 1.0 |
| 0.6329 | 7.94 | 32 | 0.4403 | 1.0 |
| 0.6329 | 8.94 | 36 | 0.3777 | 1.0 |
| 0.3821 | 9.94 | 40 | 0.3273 | 1.0 |
| 0.3821 | 10.94 | 44 | 0.2886 | 1.0 |
| 0.3821 | 11.94 | 48 | 0.2622 | 1.0 |
| 0.2655 | 12.94 | 52 | 0.2397 | 1.0 |
| 0.2655 | 13.94 | 56 | 0.2250 | 1.0 |
| 0.202 | 14.94 | 60 | 0.2152 | 1.0 |
| 0.202 | 15.94 | 64 | 0.2074 | 1.0 |
| 0.202 | 16.94 | 68 | 0.2003 | 1.0 |
| 0.1785 | 17.94 | 72 | 0.1960 | 1.0 |
| 0.1785 | 18.94 | 76 | 0.1936 | 1.0 |
| 0.1618 | 19.94 | 80 | 0.1930 | 1.0 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Apr 17, 2022
- Updated
- Apr 25, 2022