JoshuaKelleyDs/quickdraw-ViT-base-finetune
By JoshuaKelleyDs · ↓ 10 · ♥ 0 · Updated 2024-05-16
What's in this repo
This page lists the files in JoshuaKelleyDs/quickdraw-ViT-base-finetune, including model.onnx.
- 1 ONNX models (.onnx files)
- 11 files, including 6 .json, 1 .md, 1 .safetensors, 1 .onnx, 1 .bin
- Last updated 2024-05-16
ONNX files
| File | Size | Download |
|---|---|---|
| onnx/model.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
quickdraw-ViT-base-finetune
This model is a fine-tuned version of WinKawaks/vit-tiny-patch16-224 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8260
- Accuracy: 0.7892
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: 0.0008
- train_batch_size: 512
- eval_batch_size: 512
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3104 | 0.5688 | 5000 | 1.2637 | 0.6826 |
| 1.1479 | 1.1377 | 10000 | 1.1421 | 0.7096 |
| 1.0236 | 1.7065 | 15000 | 1.0128 | 0.7404 |
| 0.9206 | 2.2753 | 20000 | 0.9457 | 0.7577 |
| 0.8878 | 2.8441 | 25000 | 0.9111 | 0.7652 |
| 0.8107 | 3.4130 | 30000 | 0.8754 | 0.7749 |
| 0.7874 | 3.9818 | 35000 | 0.8436 | 0.7827 |
| 0.7064 | 4.5506 | 40000 | 0.8360 | 0.7869 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1
- Datasets 2.19.1
- Tokenizers 0.19.1
Model info
Created: 2024-05-16
Updated: 2024-05-16
View on Hugging Face