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resnet-50-finetuned-imageclds
NadaElmasry/resnet-50-finetuned-imageclds
resnet-50-finetuned-imageclds is a image classification model from NadaElmasry. 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. --
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At a glance
- Task
- Image Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- resnet
- Access
- Public
- Created
- Apr 28, 2023
- Updated
- Apr 28, 2023
- SHA
- 0ea951dc
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. -->
resnet-50-finetuned-imageclds
This model is a fine-tuned version of microsoft/resnet-50 on the image_folder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0044
- Accuracy: 0.9992
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.177 | 1.0 | 241 | 0.0830 | 0.9942 |
| 0.0597 | 2.0 | 482 | 0.0107 | 0.9982 |
| 0.0387 | 3.0 | 723 | 0.0068 | 0.9988 |
| 0.0381 | 4.0 | 964 | 0.0044 | 0.9992 |
| 0.0361 | 5.0 | 1205 | 0.0040 | 0.9991 |
Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
- Task
- Image Classification
- Library
- transformers
- Type
- resnet
- License
- apache-2.0
- Created
- Apr 28, 2023
- Updated
- Apr 28, 2023