File size: 2,300 Bytes
949cb22 99530e5 949cb22 99530e5 949cb22 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
---
license: apache-2.0
base_model: facebook/convnextv2-base-22k-384
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: convnext-base-wd1e-8-4e-5-erasing
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.95
---
<!-- 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. -->
# convnext-base-wd1e-8-4e-5-erasing
This model is a fine-tuned version of [facebook/convnextv2-base-22k-384](https://huggingface.co/facebook/convnextv2-base-22k-384) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2174
- Accuracy: 0.95
## 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: 4e-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: cosine
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6036 | 1.0 | 1099 | 0.3737 | 0.8930 |
| 0.4997 | 2.0 | 2198 | 0.2730 | 0.9249 |
| 0.3663 | 3.0 | 3297 | 0.2666 | 0.9260 |
| 0.3176 | 4.0 | 4396 | 0.2380 | 0.9392 |
| 0.305 | 5.0 | 5495 | 0.2458 | 0.9372 |
| 0.2489 | 6.0 | 6594 | 0.2389 | 0.9459 |
| 0.2356 | 7.0 | 7693 | 0.2424 | 0.9451 |
| 0.1678 | 8.0 | 8792 | 0.2369 | 0.9439 |
| 0.1565 | 9.0 | 9891 | 0.2323 | 0.9499 |
| 0.1643 | 10.0 | 10990 | 0.2306 | 0.9503 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|