Image Classification
Transformers
Safetensors
timm
timm_wrapper
image-regression
layout-complexity
finebooks
vision
Generated from Trainer
Instructions to use davanstrien/finebooks-complexity-regressor-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davanstrien/finebooks-complexity-regressor-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="davanstrien/finebooks-complexity-regressor-v1") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("davanstrien/finebooks-complexity-regressor-v1") model = AutoModelForImageClassification.from_pretrained("davanstrien/finebooks-complexity-regressor-v1") - timm
How to use davanstrien/finebooks-complexity-regressor-v1 with timm:
import timm model = timm.create_model("hf_hub:davanstrien/finebooks-complexity-regressor-v1", pretrained=True) - Notebooks
- Google Colab
- Kaggle
finebooks-complexity-regressor-v1
This model is a fine-tuned version of davanstrien/finebooks-page-router-v0 on the davanstrien/finebooks-complexity-distil dataset. It achieves the following results on the evaluation set:
- Loss: 0.0106
- Mse: 0.0106
- Mae: 0.0620
- Spearman: 0.6373
- Pearson: 0.8084
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: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 20.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | Spearman | Pearson |
|---|---|---|---|---|---|---|---|
| 0.0111 | 1.0 | 57 | 0.0122 | 0.0122 | 0.0705 | 0.5960 | 0.7759 |
| 0.0128 | 2.0 | 114 | 0.0174 | 0.0174 | 0.0794 | 0.5442 | 0.7385 |
| 0.0111 | 3.0 | 171 | 0.0144 | 0.0144 | 0.0764 | 0.6134 | 0.7944 |
| 0.0098 | 4.0 | 228 | 0.0115 | 0.0115 | 0.0640 | 0.6142 | 0.7920 |
| 0.0083 | 5.0 | 285 | 0.0132 | 0.0132 | 0.0693 | 0.5966 | 0.7756 |
| 0.0077 | 6.0 | 342 | 0.0119 | 0.0119 | 0.0663 | 0.6100 | 0.8131 |
| 0.0073 | 7.0 | 399 | 0.0116 | 0.0116 | 0.0683 | 0.6199 | 0.7883 |
| 0.0046 | 8.0 | 456 | 0.0147 | 0.0147 | 0.0680 | 0.5732 | 0.7318 |
| 0.0055 | 9.0 | 513 | 0.0126 | 0.0126 | 0.0640 | 0.6126 | 0.7768 |
| 0.0069 | 10.0 | 570 | 0.0162 | 0.0162 | 0.0730 | 0.5754 | 0.7412 |
| 0.0048 | 11.0 | 627 | 0.0138 | 0.0138 | 0.0661 | 0.6069 | 0.7479 |
| 0.0036 | 12.0 | 684 | 0.0110 | 0.0110 | 0.0649 | 0.6282 | 0.8009 |
| 0.0038 | 13.0 | 741 | 0.0107 | 0.0107 | 0.0627 | 0.6278 | 0.8061 |
| 0.0029 | 14.0 | 798 | 0.0106 | 0.0106 | 0.0620 | 0.6373 | 0.8084 |
| 0.0023 | 15.0 | 855 | 0.0128 | 0.0128 | 0.0648 | 0.6122 | 0.7656 |
| 0.0015 | 16.0 | 912 | 0.0133 | 0.0133 | 0.0649 | 0.6129 | 0.7599 |
| 0.0015 | 17.0 | 969 | 0.0126 | 0.0126 | 0.0647 | 0.6137 | 0.7714 |
| 0.0015 | 18.0 | 1026 | 0.0129 | 0.0129 | 0.0646 | 0.6170 | 0.7643 |
| 0.0024 | 19.0 | 1083 | 0.0130 | 0.0130 | 0.0646 | 0.6176 | 0.7637 |
| 0.0021 | 20.0 | 1140 | 0.0130 | 0.0130 | 0.0646 | 0.6185 | 0.7639 |
Framework versions
- Transformers 5.12.1
- Pytorch 2.12.1+cu130
- Datasets 5.0.0
- Tokenizers 0.22.2
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Model tree for davanstrien/finebooks-complexity-regressor-v1
Base model
timm/convnextv2_pico.fcmae_ft_in1k Finetuned
davanstrien/finebooks-page-router-v0