initial commit

README.md

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----
-license: mit
----
+---
+library_name: transformers
+tags:
+- page
+- classification
+base_model:
+- google/vit-base-patch16-224
+pipeline_tag: image-classification
+license: mit
+---
+
+# Image classification using fine-tuned ViT - for historical :bowtie: documents sorting
+
+### Goal: solve a task of archive page images sorting (for their further content-based processing)
+
+**Scope:** Processing of images, training and evaluation of ViT model,
+input file/directory processing, class 🏷️ (category) results of top
+N predictions output, predictions summarizing into a tabular format, 
+HF 😊 hub support for the model
+
+## Model description 📇
+
+🔲 Fine-tuned model repository:  vit-historical-page [^1] 🔗
+
+🔳 Base model repository: google's vit-base-patch16-224 [^2] 🔗
+
+### Data 📜
+
+Training set of the model: **8950** images 
+
+### Categories 🏷️
+
+|      Label️ |  Ratio  | Description                                                                  |
+|------------:|:-------:|:-----------------------------------------------------------------------------|
+|    **DRAW** | 	11.89% | **📈 - drawings, maps, paintings with text**                                 |
+|  **DRAW_L** | 	8.17%  | **📈📏 - drawings ... with a table legend or inside tabular layout / forms** |
+| **LINE_HW** |  5.99%  | **✏️📏 - handwritten text lines inside tabular layout / forms**              |
+|  **LINE_P** | 	6.06%  | **📏 - printed text lines inside tabular layout / forms**                    |
+|  **LINE_T** | 	13.39% | **📏 - machine typed text lines inside tabular layout / forms**              |
+|   **PHOTO** | 	10.21% | **🌄 - photos with text**                                                    |
+| **PHOTO_L** |  7.86%  | **🌄📏 - photos inside tabular layout / forms or with a tabular annotation** |
+|    **TEXT** | 	8.58%  | **📰 - mixed types of printed and handwritten texts**                        |
+| **TEXT_HW** |  7.36%  | **✏️📄 - only handwritten text**                                             |
+|  **TEXT_P** | 	6.95%  | **📄 - only printed text**                                                   |
+|  **TEXT_T** | 	13.53% | **📄 - only machine typed text**                                             |
+
+Evaluation set (same proportions):	**995** images 
+
+#### Data preprocessing 
+
+During training the following transforms were applied randomly with a 50% chance:
+
+* transforms.ColorJitter(brightness 0.5)
+* transforms.ColorJitter(contrast 0.5)
+* transforms.ColorJitter(saturation 0.5)
+* transforms.ColorJitter(hue 0.5)
+* transforms.Lambda(lambda img: ImageEnhance.Sharpness(img).enhance(random.uniform(0.5, 1.5)))
+* transforms.Lambda(lambda img: img.filter(ImageFilter.GaussianBlur(radius=random.uniform(0, 2))))
+
+### Training Hyperparameters
+        
+* eval_strategy "epoch"
+* save_strategy "epoch"
+* learning_rate 5e-5
+* per_device_train_batch_size 8
+* per_device_eval_batch_size 8
+* num_train_epochs 3
+* warmup_ratio 0.1
+* logging_steps 10
+* load_best_model_at_end True
+* metric_for_best_model "accuracy"      
+
+### Results 📊
+
+Evaluation set's accuracy (**Top-3**):  **99.6%** 
+
+![TOP-3 confusion matrix - trained ViT](https://github.com/K4TEL/ltp-ocr/blob/transformer/result/plots/20250209-1526_conf_mat.png?raw=true)
+
+Evaluation set's accuracy (**Top-1**):  **97.3%** 
+
+![TOP-1 confusion matrix - trained ViT](https://github.com/K4TEL/ltp-ocr/blob/transformer/result/plots/20250218-1523_conf_mat.png?raw=true)
+
+#### Result tables
+
+- Manually ✍ **checked** evaluation dataset results (TOP-3): [model_TOP-3_EVAL.csv](https://github.com/K4TEL/ltp-ocr/blob/transformer/result/tables/20250209-1534_model_1119_3_TOP-3_EVAL.csv) 🔗
+
+- Manually ✍ **checked** evaluation dataset results (TOP-1): [model_TOP-1_EVAL.csv](https://github.com/K4TEL/ltp-ocr/blob/transformer/result/tables/20250218-1519_model_1119_3_TOP-1_EVAL.csv) 🔗
+
+#### Table columns
+
+- **FILE** - name of the file
+- **PAGE** - number of the page
+- **CLASS-N** - label of the category 🏷️, guess TOP-N 
+- **SCORE-N** - score of the category 🏷️, guess TOP-N
+- **TRUE** - actual label of the category 🏷️
+
+### Contacts 📧
+
+For support write to 📧 [email protected] 📧
+
+Official repository: UFAL [^3]
+
+### Acknowledgements 🙏
+
+- **Developed by** UFAL [^5] 👥
+- **Funded by** ATRIUM [^4]  💰
+- **Shared by** ATRIUM [^4] & UFAL [^5]
+- **Model type:** fine-tuned ViT [^2] with a 224x224 resolution size
+
+**©️ 2022 UFAL & ATRIUM**
+
+[^1]: https://huggingface.co/k4tel/vit-historical-page
+[^2]: https://huggingface.co/google/vit-base-patch16-224
+[^3]: https://github.com/ufal/atrium-page-classification
+[^4]: https://atrium-research.eu/
+[^5]: https://ufal.mff.cuni.cz/home-page

config.json

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+{
+  "_name_or_path": "k4tel/vit-historical-page",
+  "architectures": [
+    "ViTForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "encoder_stride": 16,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6",
+    "7": "LABEL_7",
+    "8": "LABEL_8",
+    "9": "LABEL_9",
+    "10": "LABEL_10"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_10": 10,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6,
+    "LABEL_7": 7,
+    "LABEL_8": 8,
+    "LABEL_9": 9
+  },
+  "layer_norm_eps": 1e-12,
+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "patch_size": 16,
+  "problem_type": "multi_label_classification",
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.3"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:83902150a34254d747f405f8472cae4ade65c356e9cae0d1f9caa72554175689
+size 343251660

preprocessor_config.json

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+{
+  "do_convert_rgb": null,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "ViTImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}