Upload folder using huggingface_hub

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.avro filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

.gitignore

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+filtered_images
+images
+upload.py

ComputeCls.py

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+from transformers import pipeline
+import matplotlib.pyplot as plt
+from pathlib import Path
+from PIL import Image
+import argparse
+import shutil
+import json
+import os
+
+# Argument parser
+parser = argparse.ArgumentParser(description="Compute image classification")
+parser.add_argument("input_path", type=str, help="Path to the input directory")
+
+args = parser.parse_args()
+input_path = args.input_path
+
+checkpoint = "openai/clip-vit-large-patch14"
+detector = pipeline(model=checkpoint, task="zero-shot-image-classification", use_fast=True)
+
+# Setting up candidate labels for CLIP
+candidate_labels = ["Archaeological artifact", "Documentation sheet", "Documentation sheet with archaeological artifact", "Archaeological excavation site", "Outdoor", "Landscape", "Archaeological structure", "Floor"]
+
+# Define output .json file and target path for filtered images
+output_file = Path(f"results_pred_dataset_{os.path.basename(input_path)}.json")
+target_path = f"filtered_images/{os.path.basename(input_path)}" # Create the dir and subdirs manually
+
+if output_file.exists():
+    with output_file.open("r") as f:
+        all_results = json.load(f)
+else:
+    all_results = []
+
+files = sorted([f for f in os.listdir(input_path) if f.endswith(".jpg")])
+
+"""
+Classification
+"""
+fig = plt.figure(figsize=(9,13))
+
+# Helper funtion to create plots
+def add_image_subplot(rows, columns, subplot_idx, image, fname, prediction_score, prediction_label):
+    image = image.convert('RGB')
+    ax = fig.add_subplot(rows, columns, subplot_idx)
+    ax.imshow(image)
+    ax.set_title(f"{fname}\n{prediction_label}\n{prediction_score}", fontsize=9)
+    ax.axis("off")
+
+columns = 4
+rows = 5
+
+subplot_idx = 1
+plt_cont = 1
+cont = 0
+cont_arch = 0
+
+for fname in files:
+    try:
+        image_path = os.path.join(input_path, fname)
+
+        # Load the image
+        image = Image.open(image_path)
+
+        predictions = detector(image, candidate_labels=candidate_labels)
+
+        if subplot_idx == 21:
+            plt.tight_layout()
+            plt.savefig(f"Result_Images/{os.path.basename(input_path)}_CLIP_predictions_{(subplot_idx-1)*plt_cont}.png", dpi=300, bbox_inches="tight") # Create the Result_Images folder manually
+
+            fig = plt.figure(figsize=(9,13))
+
+            plt_cont += 1
+            subplot_idx = 1
+        
+        if subplot_idx < 21:
+            if predictions[0]['label'] == "Archaeological artifact":
+                add_image_subplot(rows, columns, subplot_idx, image, fname, predictions[0]['score'], predictions[0]['label'])
+
+                cont_arch += 1
+                subplot_idx += 1
+
+        results = {
+            "ground_truth:": str(fname),
+            "prediction_score:": float(predictions[0]['score']),
+            "prediction_label:": str(predictions[0]['label'])
+        }
+
+        all_results.append(results)
+
+        cont += 1
+    except:
+        pass
+
+number_of_images = {
+    "Original number of images:": int(len(files)),
+    "Number of images after filtering:": int(cont_arch)
+}
+
+all_results.append(number_of_images)
+
+with output_file.open("w") as f:
+    json.dump(all_results, f, indent=4)
+
+# Save last 20 images
+plt.tight_layout()
+plt.savefig(f"Result_Images/{os.path.basename(input_path)}_CLIP_predictions_{(subplot_idx-1)*plt_cont}.png", dpi=300, bbox_inches="tight")
+
+print(cont, "images appended to JSON file.")
+
+"""
+Move Images
+"""
+labeled_data = Path(f"results_pred_dataset_{os.path.basename(input_path)}.json")
+
+lst_labeled_data = []
+cont = 0
+
+with labeled_data.open("r") as f:
+    lst_labeled_data = json.load(f)
+
+source_files = sorted([f for f in os.listdir(input_path) if f.endswith(".jpg")])
+
+for i in range(len(lst_labeled_data)):
+    if lst_labeled_data[i].get('prediction_label:') == "Archaeological artifact":
+        source_file_path = os.path.join(input_path, lst_labeled_data[i]['ground_truth:'])
+        cont += 1
+        shutil.copy2(source_file_path, target_path)
+    elif not lst_labeled_data[i].get('prediction_label:'):
+        continue
+
+print("Done!", cont)

README.md

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+# Artifact Images Without Background
+
+This dataset contains images of **archaeological artifacts with the background removed**.  
+The images were generated by filtering and processing the original dataset:
+
+https://huggingface.co/datasets/archaia/dataset_v1
+
+The following sections describe the processing pipeline used to obtain the final dataset, including:
+
+1. Image filtering using **CLIP**
+2. **Object detection and background removal**
+
+---
+
+# Processing Pipeline
+
+## 1. Image Filtering with CLIP
+
+The first step filters the images from the original dataset to retain only those containing archaeological artifacts.
+
+The filtering process is implemented in [`ComputeCls.py`](ComputeCls.py).
+
+### Execution
+
+Run the following command:
+
+```bash 
+python ComputeCls.py --input_path [path_to_input_dir]
+```
+
+### Output
+
+The script performs the following operations:
+
+- Classifies images using CLIP
+- Generates a JSON file containing classification results
+- Moves images classified as artifacts to a new directory
+- Saves visualization plots of images identified as artifacts
+- This step produces a filtered subset containing only artifact-related images.
+
+## 2. Object Detection and Background Removal
+
+The second stage detects artifacts in the images and removes their backgrounds.
+
+The code is implemented in [`test_finetuned_removebg.py`](test_finetuned_removebg.py).
+
+### Execution
+
+Run the following command:
+
+```bash 
+python test_finetuned_removebg.py --input_path [path_to_input_dir] --output_path [path_to_output_dir]
+```
+
+### Processing Steps
+
+- The script iterates over each image in the input directory.
+- An object detection model detects potential artifacts in the image.
+- The detection model is a fine-tuned DETR model trained on 303 labeled images.
+Details about the training data and the model checkpoint will be provided in a separate repository.
+- Only detections labeled as "*Archaeological artifacts*" are retained.
+- The detected bounding boxes are used to crop the original images.
+- Since a single image may contain multiple artifacts, each detection is processed independently.
+- For every cropped artifact, the BRIAAI background removal model is applied to remove the background.
+
+### Output
+
+- The processed images are saved in the specified output directory.
+- If multiple artifacts are detected in the same image, each artifact is saved separately using the following naming convention: original_filename_[detection_id].png
+
+    - Example:
+        - 000000029_0.png
+        - 000000029_1.png
+        - 000000029_2.png
+
+- Each output image contains a single archaeological artifact with the background removed.
+
+# Result
+
+The final dataset consists of:
+
+- Images containing individual archaeological artifacts
+- Background removed
+- Artifacts automatically extracted from the original dataset using a pipeline based on:
+    - CLIP image filtering
+    - DETR object detection
+    - BRIAAI background removal