metadata
license: apache-2.0
base_model: microsoft/swinv2-base-patch4-window8-256
tags:
- pytoroch
- Swinv2ForImageClassification
- food-classification
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
- f1
model-index:
- name: Swin-V2-base-Food
results: []
Swin-V2-base-Food
This model is a fine-tuned version of microsoft/swinv2-base-patch4-window8-256 on the ItsNotRohit/Food121-224 dataset. It achieves the following results on the evaluation set:
- Loss: 0.7099
- Accuracy: 0.8160
- Recall: 0.8160
- Precision: 0.8168
- F1: 0.8159
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: 16
- eval_batch_size: 128
- seed: 17769929
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 20000
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 |
|---|---|---|---|---|---|---|---|
| 1.5169 | 0.33 | 2000 | 1.2680 | 0.6746 | 0.6746 | 0.7019 | 0.6737 |
| 1.2362 | 0.66 | 4000 | 1.0759 | 0.7169 | 0.7169 | 0.7411 | 0.7178 |
| 1.1076 | 0.99 | 6000 | 0.9757 | 0.7437 | 0.7437 | 0.7593 | 0.7430 |
| 0.9163 | 1.32 | 8000 | 0.9123 | 0.7623 | 0.7623 | 0.7737 | 0.7628 |
| 0.8291 | 1.65 | 10000 | 0.8397 | 0.7807 | 0.7807 | 0.7874 | 0.7796 |
| 0.7949 | 1.98 | 12000 | 0.7724 | 0.7965 | 0.7965 | 0.8014 | 0.7965 |
| 0.6455 | 2.31 | 14000 | 0.7458 | 0.8030 | 0.8030 | 0.8069 | 0.8031 |
| 0.6332 | 2.64 | 16000 | 0.7222 | 0.8110 | 0.8110 | 0.8122 | 0.8106 |
| 0.6132 | 2.98 | 18000 | 0.7021 | 0.8154 | 0.8154 | 0.8170 | 0.8155 |
| 0.57 | 3.31 | 20000 | 0.7099 | 0.8160 | 0.8160 | 0.8168 | 0.8159 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0