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---
datasets:
- hieupth/cad
- shreyanshu09/Block_Diagram
- krowiemlekommm/PJN_CHARTS
- corto-ai/handwritten-text
- katanaml-org/invoices-donut-data-v1
- mathieu1256/FATURA2-invoices
- AjitRawat/invoice
- HuggingFaceM4/DocumentVQA
- ajaynmopidevi/DocumentIDEFICS_QA
- daitavan/financial-documents
- Anas989898/Vision-OCR-Financial-Reports-10k
- dpdl-benchmark/places100-easy
- xirigh/people
- huggan/flowers-102-categories
- iamkaikai/amazing_logos
- iamkaikai/amazing_logos_v2
- manelreghima/companies_logos
- samp3209/logo-dataset
- salmonhumorous/logo-blip-caption
- dream-textures/textures-color-1k
- GATE-engine/describable_textures
- ppierzc/ios-app-icons
- Lancelot53/android_icon_dataset
- naxalpha/stable-icons-128
- yaneivan/memes_caption
- Bruece/office-home-clipart-caption
base_model:
- timm/resnext50_32x4d.fb_swsl_ig1b_ft_in1k
---
# fine_tuned_image_relevance_model
This model is a fine-tuned version of [resnext50_32x4d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext50_32x4d.fb_swsl_ig1b_ft_in1k) on an aggregated dataset of images that were classified as relevant (1.0) or irrelevant (0.0). It achieves the following results on the validation set:
- Loss: 0.1032
- Accuracy: 0.9936
## Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-06
- train_batch_size: 8
- valid_batch_size: 8
- seed: seed not explicitly set
- optimizer: torch.optim.AdamW(resnet_model.parameters(), lr=lr, eps=0.000001)
- lr_scheduler_type: OneCycleLR
- num_epochs: 6
## Training results
| Training Loss | Epoch | Validation Loss | Accuracy |
| :-----------: | :---: | :-------------: | :------: |
| 0.5536 | 1 | 0.3270 | 0.9856 |
| 0.3176 | 2 | 0.1720 | 0.9922 |
| 0.1887 | 3 | 0.1332 | 0.9944 |
| 0.1280 | 4 | 0.1146 | 0.9938 |
| 0.1116 | 5 | 0.1236 | 0.9938 |
| 0.1016 | 6 | 0.1032 | 0.9936 |
## Framework versions
- timm 1.0.19
- PyTorch 2.8.0+cpu
- Datasets 4.0.0 |