fix comicsans detector save

.ipynb_checkpoints/README-checkpoint.md

@@ -1,69 +0,0 @@
-# Comic Sans Detector
-
-This repository contains a fine-tuned ResNet-18 model, specifically trained to detect whether an image contains Comic Sans font. It is a fine-tuning of a previously fine-tuned font classification model, based on the ResNet-18 foundation model.
-
-## Repository Contents
-
-- **`comic-detector.ipynb`**: A notebook that demonstrates the training and evaluation process for the Comic Sans detector using the fine-tuned ResNet-18 model.
-- **`image-format-generalizer.ipynb`**: A utility notebook for preparing and normalizing image datasets, ensuring consistent formatting across `/data` folders.
-
-## Dataset Structure (Not Included)
-
-The dataset used for training and evaluation should follow this structure:
-```
-/data
-├── comic/
-│   ├── image1.jpg
-│   ├── image2.png
-│   └── ...
-├── not-comic/
-│   ├── image1.jpg
-│   ├── image2.png
-│   └── ...
-```
-- **`comic/`**: Contains images labeled as featuring Comic Sans font.
-- **`not-comic/`**: Contains images labeled as not featuring Comic Sans font.
-
-⚠️ The dataset itself is not included in this repository. You must prepare and structure your dataset as described.
-
-## How to Use
-
-### 1. Clone the Repository
-```bash
-git clone https://huggingface.co/your-username/comic-sans-detector
-cd comic-sans-detector
-```
-
-### 2. Prepare the Dataset
-Ensure your dataset is properly structured under a `/data` directory with `comic/` and `not-comic/` folders.
-
-### 3. Run the Training Notebook
-Open `comic-detector.ipynb` in Jupyter Notebook or an equivalent environment to retrain the model or evaluate it.
-
-### 4. Format Images (Optional)
-If your dataset images are not in a consistent format, use `image-format-generalizer.ipynb` to preprocess them.
-
-## Model Usage
-
-The fine-tuned model can be deployed directly via the Hugging Face Inference API. Once uploaded, the model can be used to classify whether an image contains Comic Sans font.
-
-Example API usage (replace `your-username/comic-sans-detector` with your repository name):
-```python
-from transformers import pipeline
-
-classifier = pipeline("image-classification", model="your-username/comic-sans-detector")
-result = classifier("path/to/image.jpg")
-print(result)
-```
-
-## Fine-Tuning Process
-
-This model was fine-tuned on a previously fine-tuned font classification model, which itself was based on the ResNet-18 foundation model. The fine-tuning process was conducted using a custom dataset with two classes: `comic` and `not-comic`.
-
-## Acknowledgments
-
-This project is based on the original font identifier repository by [gaborcselle](https://huggingface.co/gaborcselle/font-identifier).
-
-## License
-
-Include your preferred license here (e.g., MIT, Apache 2.0, etc.).

.ipynb_checkpoints/image-format-generalizer-checkpoint.ipynb

@@ -1,84 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d3fca531-0f68-4951-b168-db8ad2d25971",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from PIL import Image, UnidentifiedImageError\n",
-    "import os\n",
-    "import pillow_avif  # AVIF support for Pillow\n",
-    "\n",
-    "# Define paths to folders\n",
-    "data_dir = \"./data\"\n",
-    "folders = [\"comic\", \"not-comic\"]  # Both folders to process\n",
-    "output_format = \"png\"  # Target image format\n",
-    "\n",
-    "# Function to clean, convert, and rename images\n",
-    "def process_images(data_dir, folders, output_format):\n",
-    "    for folder in folders:\n",
-    "        folder_path = os.path.join(data_dir, folder)\n",
-    "        print(f\"Processing folder: {folder_path}\")\n",
-    "\n",
-    "        # Ensure the folder exists\n",
-    "        if not os.path.exists(folder_path):\n",
-    "            print(f\"Folder {folder_path} does not exist. Skipping.\")\n",
-    "            continue\n",
-    "\n",
-    "        # Sort files to preserve order and avoid overwriting\n",
-    "        image_count = 1  # Start numbering images\n",
-    "        for filename in sorted(os.listdir(folder_path)):\n",
-    "            file_path = os.path.join(folder_path, filename)\n",
-    "            if \"test_sample.png\" in filename:  # Ignore test_sample.png\n",
-    "                continue\n",
-    "\n",
-    "            try:\n",
-    "                # Open and convert image (supports AVIF and others)\n",
-    "                with Image.open(file_path) as img:\n",
-    "                    img = img.convert(\"RGB\")  # Ensure compatible format\n",
-    "                    new_filename = f\"{str(image_count).zfill(4)}.{output_format}\"\n",
-    "                    new_file_path = os.path.join(folder_path, new_filename)\n",
-    "\n",
-    "                    # Save as new file\n",
-    "                    img.save(new_file_path, format=output_format.upper())\n",
-    "                    print(f\"Converted: {filename} -> {new_filename}\")\n",
-    "\n",
-    "                    # Remove old file if different\n",
-    "                    if file_path != new_file_path:\n",
-    "                        os.remove(file_path)\n",
-    "\n",
-    "                    image_count += 1\n",
-    "\n",
-    "            except (UnidentifiedImageError, IOError) as e:\n",
-    "                print(f\"Invalid or unreadable file: {filename} ({e}). Deleting.\")\n",
-    "                os.remove(file_path)  # Delete invalid files\n",
-    "\n",
-    "# Run the processing function\n",
-    "process_images(data_dir, folders, output_format)\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

.jupyter/desktop-workspaces/default-37a8.jupyterlab-workspace

@@ -1 +0,0 @@
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config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "gaborcselle/font-identifier",
+  "_name_or_path": "comic-sans-detector",
   "architectures": [
     "ResNetForImageClassification"
   ],
@@ -20,106 +20,12 @@
     512
   ],
   "id2label": {
-    "0": "Agbalumo-Regular",
-    "1": "AlfaSlabOne-Regular",
-    "2": "ArchitectsDaughter-Regular",
-    "3": "Arial",
-    "4": "Arial Black",
-    "5": "Arial Bold",
-    "6": "Arial Bold Italic",
-    "7": "Avenir",
-    "8": "Bangers-Regular",
-    "9": "BlackOpsOne-Regular",
-    "10": "Courier",
-    "11": "Georgia",
-    "12": "Helvetica",
-    "13": "IBMPlexSans-Regular",
-    "14": "Inter-Regular",
-    "15": "KaushanScript-Regular",
-    "16": "Lato-Regular",
-    "17": "Lobster-Regular",
-    "18": "Lora-Regular",
-    "19": "Merriweather-Regular",
-    "20": "Niconne-Regular",
-    "21": "OpenSans-Bold",
-    "22": "OpenSans-Italic",
-    "23": "OpenSans-Light",
-    "24": "Pacifico-Regular",
-    "25": "PixelifySans-Regular",
-    "26": "PlayfairDisplay-Regular",
-    "27": "Poppins-Regular",
-    "28": "Rakkas-Regular",
-    "29": "Roboto-Regular",
-    "30": "RobotoMono-Regular",
-    "31": "RobotoSlab-Regular",
-    "32": "Rubik-Regular",
-    "33": "SpaceMono-Regular",
-    "34": "Tahoma",
-    "35": "Tahoma Bold",
-    "36": "Times New Roman",
-    "37": "Times New Roman Bold",
-    "38": "Times New Roman Bold Italic",
-    "39": "Times New Roman Italic",
-    "40": "TitilliumWeb-Regular",
-    "41": "Trebuchet MS",
-    "42": "Trebuchet MS Bold",
-    "43": "Trebuchet MS Bold Italic",
-    "44": "Trebuchet MS Italic",
-    "45": "Verdana",
-    "46": "Verdana Bold",
-    "47": "Verdana Bold Italic",
-    "48": "Verdana Italic"
+    "0": "comic",
+    "1": "not-comic"
   },
   "label2id": {
-    "Agbalumo-Regular": "0",
-    "AlfaSlabOne-Regular": "1",
-    "ArchitectsDaughter-Regular": "2",
-    "Arial": "3",
-    "Arial Black": "4",
-    "Arial Bold": "5",
-    "Arial Bold Italic": "6",
-    "Avenir": "7",
-    "Bangers-Regular": "8",
-    "BlackOpsOne-Regular": "9",
-    "Courier": "10",
-    "Georgia": "11",
-    "Helvetica": "12",
-    "IBMPlexSans-Regular": "13",
-    "Inter-Regular": "14",
-    "KaushanScript-Regular": "15",
-    "Lato-Regular": "16",
-    "Lobster-Regular": "17",
-    "Lora-Regular": "18",
-    "Merriweather-Regular": "19",
-    "Niconne-Regular": "20",
-    "OpenSans-Bold": "21",
-    "OpenSans-Italic": "22",
-    "OpenSans-Light": "23",
-    "Pacifico-Regular": "24",
-    "PixelifySans-Regular": "25",
-    "PlayfairDisplay-Regular": "26",
-    "Poppins-Regular": "27",
-    "Rakkas-Regular": "28",
-    "Roboto-Regular": "29",
-    "RobotoMono-Regular": "30",
-    "RobotoSlab-Regular": "31",
-    "Rubik-Regular": "32",
-    "SpaceMono-Regular": "33",
-    "Tahoma": "34",
-    "Tahoma Bold": "35",
-    "Times New Roman": "36",
-    "Times New Roman Bold": "37",
-    "Times New Roman Bold Italic": "38",
-    "Times New Roman Italic": "39",
-    "TitilliumWeb-Regular": "40",
-    "Trebuchet MS": "41",
-    "Trebuchet MS Bold": "42",
-    "Trebuchet MS Bold Italic": "43",
-    "Trebuchet MS Italic": "44",
-    "Verdana": "45",
-    "Verdana Bold": "46",
-    "Verdana Bold Italic": "47",
-    "Verdana Italic": "48"
+    "comic": 0,
+    "not-comic": 1
   },
   "layer_type": "basic",
   "model_type": "resnet",

model.safetensors

@@ -1,3 +1,3 @@
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