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---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: YKXBCi/resnet-50-ucSat
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# YKXBCi/resnet-50-ucSat
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9091
- Train Accuracy: 0.7125
- Train Top-3-accuracy: 0.9227
- Validation Loss: 1.0869
- Validation Accuracy: 0.6562
- Validation Top-3-accuracy: 0.8924
- Epoch: 4
## 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:
- optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 275, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 2.6504 | 0.2057 | 0.3591 | 2.2693 | 0.3299 | 0.5069 | 0 |
| 1.8871 | 0.4062 | 0.6494 | 1.6561 | 0.4618 | 0.7083 | 1 |
| 1.4603 | 0.5278 | 0.7790 | 1.4162 | 0.5417 | 0.8021 | 2 |
| 1.1499 | 0.6199 | 0.8676 | 1.2030 | 0.625 | 0.8646 | 3 |
| 0.9091 | 0.7125 | 0.9227 | 1.0869 | 0.6562 | 0.8924 | 4 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.6.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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