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PlantDiseaseClassifier-Resnet50
SanketJadhav/PlantDiseaseClassifier-Resnet50
PlantDiseaseClassifier-Resnet50 is a image classification model from SanketJadhav. 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
- May 1, 2023
- Updated
- May 1, 2023
- SHA
- 6b20fa54
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-eurosat
This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.1382
- Accuracy: 0.9641
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 |
|---|---|---|---|---|
| 0.2976 | 1.0 | 549 | 0.1450 | 0.9636 |
| 0.3388 | 2.0 | 1098 | 0.1382 | 0.9641 |
| 0.361 | 3.0 | 1647 | 0.1432 | 0.9632 |
| 0.3163 | 4.0 | 2197 | 0.1412 | 0.9640 |
| 0.3103 | 5.0 | 2745 | 0.1391 | 0.9639 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Image Classification
- Library
- transformers
- Type
- resnet
- License
- apache-2.0
- Created
- May 1, 2023
- Updated
- May 1, 2023