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resnet-50
jerryteps/resnet-50
resnet-50 is a image classification model from jerryteps. 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
- resnet
- Access
- Public
- Created
- Nov 6, 2023
- Updated
- Nov 12, 2023
- SHA
- 36fbcd85
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
This model was trained from scratch on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.1947
- Accuracy: 0.5408
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.5588 | 1.0 | 252 | 1.4406 | 0.4558 |
| 1.4831 | 2.0 | 505 | 1.3683 | 0.4790 |
| 1.4776 | 3.0 | 757 | 1.3199 | 0.4937 |
| 1.4246 | 4.0 | 1010 | 1.2881 | 0.5068 |
| 1.4102 | 5.0 | 1262 | 1.2469 | 0.5247 |
| 1.3806 | 6.0 | 1515 | 1.2276 | 0.5258 |
| 1.3861 | 7.0 | 1767 | 1.2121 | 0.5411 |
| 1.3791 | 8.0 | 2020 | 1.2075 | 0.5433 |
| 1.3683 | 9.0 | 2272 | 1.2011 | 0.5422 |
| 1.4119 | 9.98 | 2520 | 1.1947 | 0.5408 |
Framework versions
- Transformers 4.30.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
- Task
- Image Classification
- Library
- transformers
- Type
- resnet
- Created
- Nov 6, 2023
- Updated
- Nov 12, 2023