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---
license: apache-2.0
base_model: microsoft/resnet-152
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-152-finetuned-cassava-leaf-disease
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: 0.7397196261682243
---
<!-- 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-152-finetuned-cassava-leaf-disease
This model is a fine-tuned version of [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7961
- Accuracy: 0.7397
## 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: 480
- eval_batch_size: 480
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 1920
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 7.309 | 0.98 | 10 | 7.0088 | 0.0028 |
| 6.9946 | 1.95 | 20 | 6.4363 | 0.0061 |
| 6.4082 | 2.93 | 30 | 5.5840 | 0.0673 |
| 5.6018 | 4.0 | 41 | 4.1884 | 0.3687 |
| 4.5652 | 4.98 | 51 | 3.3123 | 0.4640 |
| 3.6106 | 5.95 | 61 | 2.7918 | 0.5136 |
| 2.9184 | 6.93 | 71 | 2.3762 | 0.5636 |
| 2.3775 | 8.0 | 82 | 1.9163 | 0.6084 |
| 2.0119 | 8.98 | 92 | 1.7038 | 0.6299 |
| 1.7519 | 9.95 | 102 | 1.5220 | 0.6411 |
| 1.4995 | 10.93 | 112 | 1.3828 | 0.6575 |
| 1.3648 | 12.0 | 123 | 1.2715 | 0.6668 |
| 1.2357 | 12.98 | 133 | 1.2040 | 0.6692 |
| 1.1606 | 13.95 | 143 | 1.1249 | 0.6785 |
| 1.0793 | 14.93 | 153 | 1.0600 | 0.6897 |
| 1.0332 | 16.0 | 164 | 1.0160 | 0.6935 |
| 0.9724 | 16.98 | 174 | 0.9706 | 0.7047 |
| 0.9349 | 17.95 | 184 | 0.9524 | 0.7075 |
| 0.895 | 18.93 | 194 | 0.9210 | 0.7093 |
| 0.8913 | 20.0 | 205 | 0.9007 | 0.7168 |
| 0.8519 | 20.98 | 215 | 0.8672 | 0.7229 |
| 0.8434 | 21.95 | 225 | 0.8432 | 0.7252 |
| 0.8346 | 22.93 | 235 | 0.8307 | 0.7304 |
| 0.8019 | 24.0 | 246 | 0.8154 | 0.7308 |
| 0.8001 | 24.98 | 256 | 0.8121 | 0.7327 |
| 0.7813 | 25.95 | 266 | 0.8036 | 0.7341 |
| 0.7845 | 26.93 | 276 | 0.8025 | 0.7383 |
| 0.7635 | 28.0 | 287 | 0.7934 | 0.7444 |
| 0.7782 | 28.98 | 297 | 0.7910 | 0.7421 |
| 0.7634 | 29.27 | 300 | 0.7961 | 0.7397 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.1
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