File size: 3,001 Bytes
488c9ed 2ab5de0 488c9ed afdf0a7 488c9ed afdf0a7 ca2d649 488c9ed 2ab5de0 031c9c8 ca2d649 488c9ed 2ab5de0 ca2d649 488c9ed db630f1 ca2d649 afdf0a7 ca2d649 afdf0a7 488c9ed |
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 86 87 88 89 90 91 92 93 94 95 96 97 |
---
library_name: transformers
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: cat_dog_classifier_with_small_datasest
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
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. -->
# cat_dog_classifier_with_small_datasest
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1369
- 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 70 | 0.5422 | 0.8571 |
| No log | 2.0 | 140 | 0.5221 | 0.8786 |
| No log | 3.0 | 210 | 0.4977 | 0.8571 |
| No log | 4.0 | 280 | 0.4617 | 0.8786 |
| No log | 5.0 | 350 | 0.3932 | 0.9143 |
| No log | 6.0 | 420 | 0.3411 | 0.9143 |
| No log | 7.0 | 490 | 0.2884 | 0.9143 |
| 0.4971 | 8.0 | 560 | 0.2429 | 0.9286 |
| 0.4971 | 9.0 | 630 | 0.2151 | 0.9429 |
| 0.4971 | 10.0 | 700 | 0.1962 | 0.9286 |
| 0.4971 | 11.0 | 770 | 0.1727 | 0.9357 |
| 0.4971 | 12.0 | 840 | 0.1676 | 0.95 |
| 0.4971 | 13.0 | 910 | 0.1764 | 0.9286 |
| 0.4971 | 14.0 | 980 | 0.1565 | 0.9429 |
| 0.2878 | 15.0 | 1050 | 0.1578 | 0.9429 |
| 0.2878 | 16.0 | 1120 | 0.1577 | 0.9429 |
| 0.2878 | 17.0 | 1190 | 0.1393 | 0.9429 |
| 0.2878 | 18.0 | 1260 | 0.1472 | 0.9429 |
| 0.2878 | 19.0 | 1330 | 0.1315 | 0.95 |
| 0.2878 | 20.0 | 1400 | 0.1369 | 0.95 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
|