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-*.png filter=lfs diff=lfs merge=lfs -text -\# filter=lfs diff=lfs merge=lfs -text -safe filter=lfs diff=lfs merge=lfs -text -to filter=lfs diff=lfs merge=lfs -text -even filter=lfs diff=lfs merge=lfs -text -if filter=lfs diff=lfs merge=lfs -text -*.jpg filter=lfs diff=lfs merge=lfs -text -*.jpeg filter=lfs diff=lfs merge=lfs -text -add, filter=lfs diff=lfs merge=lfs -text -already filter=lfs diff=lfs merge=lfs -text -tracked filter=lfs diff=lfs merge=lfs -text -data/nova-v1.parquet filter=lfs diff=lfs merge=lfs -text diff --git a/Nova.py b/Nova.py new file mode 100644 index 0000000000000000000000000000000000000000..a94fe1f01955d17f9e5b0d593a14368805685474 --- /dev/null +++ b/Nova.py @@ -0,0 +1,92 @@ +import os +import pandas as pd +from datasets import GeneratorBasedBuilder, SplitGenerator, Split, Value, Features, Image, DatasetInfo, Sequence + + +class NovaDataset(GeneratorBasedBuilder): + def _info(self): + return DatasetInfo( + description="NOVA benchmark: anomaly localization and clinical reasoning in brain MRI.", + features=Features({ + "image": Image(), + "filename": Value("string"), + "caption": Value("string"), + "case_id": Value("string"), + "scan_id": Value("string"), + "title": Value("string"), + "publication_date": Value("string"), + "clinical_history": Value("string"), + "differential_diagnosis": Value("string"), + "final_diagnosis": Value("string"), + "link": Value("string"), + "bbox_gold": Sequence({ + "x": Value("float32"), + "y": Value("float32"), + "width": Value("float32"), + "height": Value("float32"), + }), + "bbox_raters": Sequence({ + "rater": Value("string"), + "x": Value("float32"), + "y": Value("float32"), + "width": Value("float32"), + "height": Value("float32"), + }), }), + supervised_keys=None, + ) + + def _split_generators(self, dl_manager): + archive_path = dl_manager.download_and_extract("test/Images.zip") + image_files = list(dl_manager.iter_files(archive_path)) + + # Download CSVs + csvs = { + "captions": dl_manager.download("captions.csv"), + "bboxes_gold": dl_manager.download("bboxes_gold.csv"), + "bboxes_raters": dl_manager.download("bboxes_raters.csv"), + "case_metadata": dl_manager.download("case_metadata.csv"), + } + + return [ + SplitGenerator( + name=Split.TEST, + gen_kwargs={"image_files": image_files, "csvs": csvs}, + ) + ] + + def _generate_examples(self, image_files, csvs): + captions = pd.read_csv(csvs["captions"]) + gold = pd.read_csv(csvs["bboxes_gold"]) + raters = pd.read_csv(csvs["bboxes_raters"]) + meta = pd.read_csv(csvs["case_metadata"]) + + captions["case_id"] = captions["filename"].str.extract(r"^(case\d+)_") + + gold_grouped = gold.groupby("filename").apply( + lambda df: df[["x", "y", "width", "height"]].astype(float).to_dict(orient="records") + ).to_dict() + + raters_grouped = raters.groupby("filename").apply( + lambda df: df[["rater", "x", "y", "width", "height"]].astype({"x": float, "y": float, "width": float, "height": float}).to_dict(orient="records") + ).to_dict() + meta = meta.set_index("case_id").to_dict("index") + + images_map = {os.path.basename(f): f for f in image_files} + + for idx, row in captions.iterrows(): + filename = row["filename"] + case_id = row["case_id"] + record = { + "image": images_map.get(filename, None), + "filename": filename, + "caption": row["caption"], + "case_id": case_id, + "scan_id": str(row["scan_id"]), + "bbox_gold": gold_grouped.get(filename, []), + "bbox_raters": raters_grouped.get(filename, []), + } + if case_id in meta: + record.update(meta[case_id]) + else: + record.update({k: "" for k in ["title", "publication_date", "clinical_history", "differential_diagnosis", "final_diagnosis", "link"]}) + yield idx, record diff --git a/README.md b/README.md index 67986ed00c5b3237fcb1fd70c84e6aec04387242..f4abf99f1cdef5d3d724e98f199fa524c492f69a 100644 --- a/README.md +++ b/README.md @@ -33,7 +33,7 @@ tags: - +
NOVA LogoNOVA Logo

NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRI
@@ -41,7 +41,7 @@ tags: > **An open-world generalization benchmark under clinical distribution shift** [![License: CC BY-NC-SA 4.0](https://img.shields.io/badge/License-CC--BY--NC--SA%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/) -[Dataset on ๐Ÿค— Hugging Face](https://huggingface.co/datasets/c-i-ber/Nova) +[Dataset on ๐Ÿค— Hugging Face](https://huggingface.co/datasets/Ano-2090/Nova) *For academic, non-commercial use only* --- @@ -50,6 +50,11 @@ tags: If you find this dataset useful in your work, please consider citing it: +
+ ๐Ÿ“– Click to show citation (โš ๏ธ **Double-blind review warning**: If you are a reviewer, please **do not expand** this section if anonymity must be preserved.) + + + ```bibtex @article{bercea2025nova, title={NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRI}, @@ -60,8 +65,9 @@ If you find this dataset useful in your work, please consider citing it: } ``` +
+ [Paper](https://huggingface.co/papers/2505.14064) -[Evaluation Scripts](https://huggingface.co/c-i-ber/Nova_Evaluation) --- @@ -69,17 +75,17 @@ If you find this dataset useful in your work, please consider citing it: ```python from datasets import load_dataset -ds = load_dataset("parquet", data_files=f"hf://datasets/c-i-ber/Nova/data/nova-v1.parquet", split="train") +dataset = load_dataset("Ano-2090/Nova") ``` ## ๐Ÿงช Try it out in Colab You can explore the NOVA dataset directly in your browser using the interactive notebook below: -[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://huggingface.co/datasets/c-i-ber/Nova/blob/main/notebooks/nova_demo.ipynb) +[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://huggingface.co/datasets/Ano-2090/Nova/blob/main/example.ipynb) Or open the notebook directly: -๐Ÿ‘‰ [example.ipynb](https://huggingface.co/datasets/c-i-ber/Nova/blob/main/notebooks/nova-demo.ipynb) +๐Ÿ‘‰ [example.ipynb](https://huggingface.co/datasets/Ano-2090/Nova/blob/main/example.ipynb) --- ## ๐Ÿ’ก Motivation diff --git a/build_parquet.py b/build_parquet.py deleted file mode 100644 index 281322b5bc8e7548438811bdd9ec21028f045da7..0000000000000000000000000000000000000000 --- a/build_parquet.py +++ /dev/null @@ -1,172 +0,0 @@ -#!/usr/bin/env python3 -import os -import pandas as pd -import pyarrow as pa -import pyarrow.parquet as pq - -# โ€”โ€” Inputs (adjust if paths differ) -CAPTIONS_CSV = "captions.csv" -META_CSV = "case_metadata.csv" -GOLD_CSV = "bboxes_gold.csv" -RATERS_CSV = "bboxes_raters.csv" -IMAGES_DIR = "images" - -OUT_DIR = "data" -OUT_PARQUET = os.path.join(OUT_DIR, "nova-v1.parquet") - -os.makedirs(OUT_DIR, exist_ok=True) - -# โ€”โ€” Load CSVs -captions = pd.read_csv(CAPTIONS_CSV) -meta = pd.read_csv(META_CSV) -gold = pd.read_csv(GOLD_CSV) -raters = pd.read_csv(RATERS_CSV) - -# โ€”โ€” Normalize key columns as strings -for df in (captions, meta, gold, raters): - if "case_id" in df.columns: - df["case_id"] = df["case_id"].astype(str) - if "scan_id" in df.columns: - df["scan_id"] = df["scan_id"].astype(str) - if "filename" in df.columns: - df["filename"] = df["filename"].astype(str) - -# โ€”โ€” Build the master list of images (from captions) -# If you prefer, you can take the union of filenames from all CSVs. -images = captions[["filename", "case_id", "scan_id", "caption"]].copy() -images["caption"] = images["caption"].fillna("").astype(str) - -images.rename(columns={"caption": "caption_text"}, inplace=True) - -# Add image_path and split -images["image_path"] = IMAGES_DIR + "/" + images["filename"] -images["split"] = "test" - -# โ€”โ€” Prepare per-case metadata as a dict so we can merge by case_id -meta_cols = [ - "title", "publication_date", "clinical_history", - "differential_diagnosis", "final_diagnosis", "link" -] -meta = meta[["case_id"] + meta_cols].copy() - -# Merge per-image with per-case meta (left join by case_id) -df = images.merge(meta, on="case_id", how="left") - -# โ€”โ€” Prepare gold bboxes -# Required columns: filename, x, y, width, height -gold_cols = ["filename", "x", "y", "width", "height"] -gold_use = gold[["filename", "x", "y", "width", "height"]].copy() -gold_use["source"] = "gold" -gold_use["label"] = "" # no label provided; keep empty string - -# โ€”โ€” Prepare rater bboxes -# Required columns: filename, rater, x, y, width, height -r_cols = ["filename", "rater", "x", "y", "width", "height"] -raters_use = raters[r_cols].copy() -raters_use.rename(columns={"rater": "source"}, inplace=True) -raters_use["label"] = "" - -# โ€”โ€” Stack gold + raters, then group per filename into list[struct] -all_boxes = pd.concat([gold_use, raters_use], ignore_index=True) - -def pack_boxes(group: pd.DataFrame): - # Convert rows -> list of dicts with fixed keys - return [ - { - "x": float(row["x"]), - "y": float(row["y"]), - "width": float(row["width"]), - "height": float(row["height"]), - "source": str(row["source"]), - "label": str(row["label"]), - } - for _, row in group.iterrows() - ] - -boxes_per_file = ( - all_boxes - .groupby("filename", sort=False, group_keys=False) - .apply(pack_boxes) - .rename("bboxes") - .reset_index() -) - -# Attach bboxes to df by filename; missing -> empty list -df = df.merge(boxes_per_file, on="filename", how="left") -df["bboxes"] = df["bboxes"].apply(lambda v: v if isinstance(v, list) else []) - -# โ€”โ€” Build a nested Arrow schema for clean typing -bbox_struct = pa.struct([ - ("x", pa.float64()), - ("y", pa.float64()), - ("width", pa.float64()), - ("height", pa.float64()), - ("source", pa.string()), - ("label", pa.string()), -]) - -meta_struct = pa.struct([ - ("title", pa.string()), - ("publication_date", pa.string()), # keep as-is (e.g., 30.03.2022); can normalize later - ("clinical_history", pa.string()), - ("differential_diagnosis", pa.string()), - ("final_diagnosis", pa.string()), - ("link", pa.string()), -]) - -schema = pa.schema([ - ("image_path", pa.string()), - ("filename", pa.string()), - ("split", pa.string()), - ("case_id", pa.string()), - ("scan_id", pa.string()), - ("caption_text", pa.string()), - ("bboxes", pa.list_(bbox_struct)), - ("meta", meta_struct), -]) -for col in ["title", "publication_date", "clinical_history", "differential_diagnosis", "final_diagnosis", "link"]: - if col in df.columns: - df[col] = df[col].fillna("").astype(str) -# โ€”โ€” Build Arrow arrays column by column -def to_arrow_array_list_of_struct(list_of_dicts, struct_type): - # list_of_dicts: python list of dicts with keys matching struct_type - # returns: pa.Array(list) - if not list_of_dicts: - # represent empty with empty list - return pa.array([], type=pa.list_(struct_type)) - # we will build a ListArray from a chunk; handle per row later - raise RuntimeError("This helper is row-wise; we will build below with pa.array on the full column.") - -# Build meta struct column -meta_dicts = df[meta_cols].to_dict(orient="records") -meta_array = pa.array(meta_dicts, type=meta_struct) - -# Build bboxes list column -# Flatten per row using pa.array with explicit type -bboxes_array = pa.array(df["bboxes"].tolist(), type=pa.list_(bbox_struct)) - -# Build the rest as Arrow arrays -table = pa.Table.from_arrays( - [ - pa.array(df["image_path"].tolist(), type=pa.string()), - pa.array(df["filename"].tolist(), type=pa.string()), - pa.array(df["split"].tolist(), type=pa.string()), - pa.array(df["case_id"].tolist(), type=pa.string()), - pa.array(df["scan_id"].tolist(), type=pa.string()), - pa.array(df["caption_text"].tolist(), type=pa.string()), - bboxes_array, - meta_array, - ], - schema=schema -) - -# โ€”โ€” Optional: sanity checks -# - ensure files referenced actually exist -missing = [p for p in df["image_path"] if not os.path.exists(p)] -if missing: - print(f"[WARN] {len(missing)} image paths do not exist locally. First few:", missing[:5]) - -# โ€”โ€” Write Parquet -pq.write_table(table, OUT_PARQUET) -print(f"Wrote {OUT_PARQUET} with {table.num_rows} rows.") - diff --git a/croissant.json b/croissant.json deleted file mode 100644 index 5257565c39a149dd3419aae7e1ee69652e409559..0000000000000000000000000000000000000000 --- a/croissant.json +++ /dev/null @@ -1,127 +0,0 @@ -{ - "@context": { - "@language": "en", - "@vocab": "https://schema.org/", - "cr": "http://mlcommons.org/croissant/", - "dct": "http://purl.org/dc/terms/", - "recordSet": "cr:recordSet", - "field": "cr:field", - "fileSet": "cr:fileSet", - "fileObject": "cr:fileObject", - "dataType": { "@id": "cr:dataType", "@type": "@vocab" }, - "source": "cr:source", - "extract": "cr:extract", - "subField": "cr:subField", - "repeated": "cr:repeated", - "key": "cr:key", - "path": "cr:path", - "column": "cr:column", - "conformsTo": "dct:conformsTo", - "citeAs": "cr:citeAs" - }, - "@type": ["Dataset"], - "name": "NOVA", - "description": "Annotated brain MRI cases with radiology captions, gold + multi-rater bounding boxes, and clinical metadata.", - "url": "https://huggingface.co/datasets/c-i-ber/Nova", - "license": "https://creativecommons.org/licenses/by/4.0/", - "version": "1.0.0", - "citeAs": "Bercea C. et al., NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRI, 2025.", - "conformsTo": "http://mlcommons.org/croissant/1.1", - - "distribution": [ - { - "@type": "cr:FileObject", - "@id": "local-parquet", - "name": "NOVA Parquet", - "contentUrl": "https://huggingface.co/datasets/c-i-ber/Nova/resolve/main/data/nova-v1.parquet", - "encodingFormat": "application/x-parquet", - "sha256": "71880c12b710b8c82100da51d574f13312cc4811fb5b20073ef4f5d98d22a319" - } - ], - - "recordSet": [ - { - "@type": "cr:RecordSet", - "@id": "records", - "name": "records", - "description": "One row per image.", - "key": { "@id": "records/filename" }, - "field": [ - { - "@type": "cr:Field", - "@id": "records/image_path", - "dataType": "Text", - "source": { "fileSet": { "@id": "local-parquet" }, "extract": { "column": "image_path" } } - }, - { - "@type": "cr:Field", - "@id": "records/filename", - "dataType": "Text", - "source": { "fileSet": { "@id": "local-parquet" }, "extract": { "column": "filename" } } - }, - { - "@type": "cr:Field", - "@id": "records/split", - "dataType": "Text", - "source": { "fileSet": { "@id": "local-parquet" }, "extract": { "column": "split" } } - }, - { - "@type": "cr:Field", - "@id": "records/case_id", - "dataType": "Text", - "source": { "fileSet": { "@id": "local-parquet" }, "extract": { "column": "case_id" } } - }, - { - "@type": "cr:Field", - "@id": "records/scan_id", - "dataType": "Text", - "source": { "fileSet": { "@id": "local-parquet" }, "extract": { "column": "scan_id" } } - }, - { - "@type": "cr:Field", - "@id": "records/caption_text", - "dataType": "Text", - "description": "Radiology caption.", - "source": { "fileSet": { "@id": "local-parquet" }, "extract": { "column": "caption_text" } } - }, - { - "@type": "cr:Field", - "@id": "records/image", - "dataType": "ImageObject", - "source": { "fileSet": { "@id": "local-parquet" }, "extract": { "column": "image" } } - }, - { - "@type": "cr:Field", - "@id": "records/bboxes", - "dataType": "StructuredValue", - "description": "Bounding boxes in pixel coords with source (gold/raters).", - "repeated": true, - "source": { "fileSet": { "@id": "local-parquet" }, "extract": { "column": "bboxes" } }, - "subField": [ - { "@type": "cr:Field", "@id": "records/bboxes/x", "dataType": "Float" }, - { "@type": "cr:Field", "@id": "records/bboxes/y", "dataType": "Float" }, - { "@type": "cr:Field", "@id": "records/bboxes/width", "dataType": "Float" }, - { "@type": "cr:Field", "@id": "records/bboxes/height", "dataType": "Float" }, - { "@type": "cr:Field", "@id": "records/bboxes/source", "dataType": "Text" }, - { "@type": "cr:Field", "@id": "records/bboxes/label", "dataType": "Text" } - ] - }, - { - "@type": "cr:Field", - "@id": "records/meta", - "dataType": "StructuredValue", - "description": "Per-case metadata (Eurorad).", - "source": { "fileSet": { "@id": "local-parquet" }, "extract": { "column": "meta" } }, - "subField": [ - { "@type": "cr:Field", "@id": "records/meta/title", "dataType": "Text" }, - { "@type": "cr:Field", "@id": "records/meta/publication_date", "dataType": "Text" }, - { "@type": "cr:Field", "@id": "records/meta/clinical_history", "dataType": "Text" }, - { "@type": "cr:Field", "@id": "records/meta/differential_diagnosis", "dataType": "Text" }, - { "@type": "cr:Field", "@id": "records/meta/final_diagnosis", "dataType": "Text" }, - { "@type": "cr:Field", "@id": "records/meta/link", "dataType": "URL" } - ] - } - ] - } - ] -} \ No newline at end of file diff --git a/data/nova-v1.parquet b/data/nova-v1.parquet deleted file mode 100644 index f58e285d10673090708ba7ded6b2da0878ecc494..0000000000000000000000000000000000000000 --- a/data/nova-v1.parquet +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:71880c12b710b8c82100da51d574f13312cc4811fb5b20073ef4f5d98d22a319 -size 199088 diff --git a/dataset_description.json b/dataset_description.json new file mode 100644 index 0000000000000000000000000000000000000000..be78169c50fb838f706777305d2e9b9a9a68235b --- /dev/null +++ b/dataset_description.json @@ -0,0 +1,433 @@ +{ + "@context": { + "@language": "en", + "@vocab": "https://schema.org/", + "citeAs": "cr:citeAs", + "column": "cr:column", + "conformsTo": "dct:conformsTo", + "cr": "http://mlcommons.org/croissant/", + "rai": "http://mlcommons.org/croissant/RAI/", + "data": { + "@id": "cr:data", + "@type": "@json" + }, + "dataType": { + "@id": "cr:dataType", + "@type": "@vocab" + }, + "dct": "http://purl.org/dc/terms/", + "examples": { + "@id": "cr:examples", + "@type": "@json" + }, + "extract": "cr:extract", + "field": "cr:field", + "fileProperty": "cr:fileProperty", + "fileObject": "cr:fileObject", + "fileSet": "cr:fileSet", + "format": "cr:format", + "includes": "cr:includes", + "isLiveDataset": "cr:isLiveDataset", + "jsonPath": "cr:jsonPath", + "key": "cr:key", + "md5": "cr:md5", + "parentField": "cr:parentField", + "path": "cr:path", + "recordSet": "cr:recordSet", + "references": "cr:references", + "regex": "cr:regex", + "repeated": "cr:repeated", + "replace": "cr:replace", + "sc": "https://schema.org/", + "separator": "cr:separator", + "source": "cr:source", + "subField": "cr:subField", + "transform": "cr:transform" + }, + "@type": "sc:Dataset", + "name": "NOVA", + "description": "NOVA benchmark: anomaly localization and clinical reasoning in brain MRI.", + "conformsTo": "http://mlcommons.org/croissant/1.0", + "license": "https://creativecommons.org/licenses/by-nc-sa/4.0/", + "url": "https://huggingface.co/datasets/Ano-2090/Nova", + "distribution": [ + { + "@type": "cr:FileObject", + "@id": "captions-clean", + "name": "captions-clean", + "description": "CSV file with NOVA image captions and metadata.", + "contentUrl": "captions.csv", + "encodingFormat": "text/csv", + "sha256": "d696a6259d9a7c8023ab48462521b26b9af6571ca8bc22daf7c3dff13a42ff28" + }, + { + "@type": "cr:FileObject", + "@id": "bboxes-gold", + "name": "bboxes-gold", + "description": "CSV with gold-standard bounding boxes annotated by radiologists.", + "contentUrl": "bboxes_gold.csv", + "encodingFormat": "text/csv", + "sha256": "6cb93de17bef13b28913e73b3b2060c660c314cc88805156c486b6657d77dcc2" + }, + { + "@type": "cr:FileObject", + "@id": "bboxes-raters", + "name": "bboxes-raters", + "description": "CSV with bounding boxes annotated by independent raters.", + "contentUrl": "bboxes_raters.csv", + "encodingFormat": "text/csv", + "sha256": "56df18b177a77e8a6587babc7220732cfb38629cec56fde8ca05fef76c10d59a" + }, + { + "@type": "cr:FileObject", + "@id": "case-metadata", + "name": "case-metadata", + "description": "CSV with clinical case metadata, including diagnosis and history.", + "contentUrl": "case_metadata.csv", + "encodingFormat": "text/csv", + "sha256": "42b34bb6673564de73a921e731de4819a3029bfbf563f2755d45ed98de9760d1" + } + ], + "recordSet": [ + { + "@type": "cr:RecordSet", + "@id": "captions", + "name": "captions", + "description": "Captions and identifiers for NOVA images.", + "field": [ + { + "@type": "cr:Field", + "@id": "captions/filename", + "name": "filename", + "dataType": "sc:Text", + "source": { + "fileObject": { + "@id": "captions-clean" + }, + "extract": { + "column": "filename" + } + } + }, + { + "@type": "cr:Field", + "@id": "captions/caption", + "name": "caption", + "dataType": "sc:Text", + "source": { + "fileObject": { + "@id": "captions-clean" + }, + "extract": { + "column": "caption" + } + } + }, + { + "@type": "cr:Field", + "@id": "captions/case_id", + "name": "case_id", + "dataType": "sc:Text", + "source": { + "fileObject": { + "@id": "captions-clean" + }, + "extract": { + "column": "case_id" + } + } + }, + { + "@type": "cr:Field", + "@id": "captions/scan_id", + "name": "scan_id", + "dataType": "sc:Text", + "source": { + "fileObject": { + "@id": "captions-clean" + }, + "extract": { + "column": "scan_id" + } + } + } + ] + }, + { + "@type": "cr:RecordSet", + "@id": "bboxes_gold", + "name": "bboxes_gold", + "description": "Gold-standard bounding boxes per image.", + "field": [ + { + "@type": "cr:Field", + "@id": "bboxes_gold/filename", + "name": "filename", + "dataType": "sc:Text", + "source": { + "fileObject": { + "@id": "bboxes-gold" + }, + "extract": { + "column": "filename" + } + } + }, + { + "@type": "cr:Field", + "@id": "bboxes_gold/x", + "name": "x", + "dataType": "sc:Integer", + "source": { + "fileObject": { + "@id": "bboxes-gold" + }, + "extract": { + "column": "x" + } + } + }, + { + "@type": "cr:Field", + "@id": "bboxes_gold/y", + "name": "y", + "dataType": "sc:Integer", + "source": { + "fileObject": { + "@id": "bboxes-gold" + }, + "extract": { + "column": "y" + } + } + }, + { + "@type": "cr:Field", + "@id": "bboxes_gold/width", + "name": "width", + "dataType": "sc:Integer", + "source": { + "fileObject": { + "@id": "bboxes-gold" + }, + "extract": { + "column": "width" + } + } + }, + { + "@type": "cr:Field", + "@id": "bboxes_gold/height", + "name": "height", + "dataType": "sc:Integer", + "source": { + "fileObject": { + "@id": "bboxes-gold" + }, + "extract": { + "column": "height" + } + } + } + ] + }, + { + "@type": "cr:RecordSet", + "@id": "bboxes_raters", + "name": "bboxes_raters", + "description": "Bounding boxes by other human raters.", + "field": [ + { + "@type": "cr:Field", + "@id": "bboxes_raters/filename", + "name": "filename", + "dataType": "sc:Text", + "source": { + "fileObject": { + "@id": "bboxes-raters" + }, + "extract": { + "column": "filename" + } + } + }, + { + "@type": "cr:Field", + "@id": "bboxes_raters/rater", + "name": "rater", + "dataType": "sc:Text", + "source": { + "fileObject": { + "@id": "bboxes-raters" + }, + "extract": { + "column": "rater" + } + } + }, + { + "@type": "cr:Field", + "@id": "bboxes_raters/x", + "name": "x", + "dataType": "sc:Integer", + "source": { + "fileObject": { + "@id": "bboxes-raters" + }, + "extract": { + "column": "x" + } + } + }, + { + "@type": "cr:Field", + "@id": "bboxes_raters/y", + "name": "y", + "dataType": "sc:Integer", + "source": { + "fileObject": { + "@id": "bboxes-raters" + }, + "extract": { + "column": "y" + } + } + }, + { + "@type": "cr:Field", + "@id": "bboxes_raters/width", + "name": "width", + "dataType": "sc:Integer", + "source": { + "fileObject": { + "@id": "bboxes-raters" + }, + "extract": { + "column": "width" + } + } + }, + { + "@type": "cr:Field", + "@id": "bboxes_raters/height", + "name": "height", + "dataType": "sc:Integer", + "source": { + "fileObject": { + "@id": "bboxes-raters" + }, + "extract": { + "column": "height" + } + } + } + ] + }, + { + "@type": "cr:RecordSet", + "@id": "case_metadata", + "name": "case_metadata", + "description": "Case metadata including diagnosis and clinical history.", + "field": [ + { + "@type": "cr:Field", + "@id": "case_metadata/case_id", + "name": "case_id", + "dataType": "sc:Text", + "source": { + "fileObject": { + "@id": "case-metadata" + }, + "extract": { + "column": "case_id" + } + } + }, + { + "@type": "cr:Field", + "@id": "case_metadata/title", + "name": "title", + "dataType": "sc:Text", + "source": { + "fileObject": { + "@id": "case-metadata" + }, + "extract": { + "column": "title" + } + } + }, + { + "@type": "cr:Field", + "@id": "case_metadata/publication_date", + "name": "publication_date", + "dataType": "sc:Text", + "source": { + "fileObject": { + "@id": "case-metadata" + }, + "extract": { + "column": "publication_date" + } + } + }, + { + "@type": "cr:Field", + "@id": "case_metadata/clinical_history", + "name": "clinical_history", + "dataType": "sc:Text", + "source": { + "fileObject": { + "@id": "case-metadata" + }, + "extract": { + "column": "clinical_history" + } + } + }, + { + "@type": "cr:Field", + "@id": "case_metadata/differential_diagnosis", + "name": "differential_diagnosis", + "dataType": "sc:Text", + "source": { + "fileObject": { + "@id": "case-metadata" + }, + "extract": { + "column": "differential_diagnosis" + } + } + }, + { + "@type": "cr:Field", + "@id": "case_metadata/final_diagnosis", + "name": "final_diagnosis", + "dataType": "sc:Text", + "source": { + "fileObject": { + "@id": "case-metadata" + }, + "extract": { + "column": "final_diagnosis" + } + } + }, + { + "@type": "cr:Field", + "@id": "case_metadata/link", + "name": "link", + "dataType": "sc:Text", + "source": { + "fileObject": { + "@id": "case-metadata" + }, + "extract": { + "column": "link" + } + } + } + ] + } + ] +} \ No newline at end of file diff --git a/example.ipynb b/example.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b3ea8f87b039f3eb29bbe7c4381d95ab52f7fe74 --- /dev/null +++ b/example.ipynb @@ -0,0 +1,189 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ๐Ÿง  NOVA Benchmark: Extreme Stress-Test for Out-of-Distribution Detection in Brain MRI\n", + "\n", + "Welcome to the NOVA dataset โ€” a carefully curated, evaluation-only benchmark designed to push the limits of machine learning models in real-world clinical scenarios. With over **900 brain MRI scans**, **281 rare pathologies**, and **rich clinical metadata**, NOVA goes beyond traditional anomaly detection.\n", + "\n", + "This notebook walks you through how to:\n", + "\n", + "- Load the NOVA dataset directly from Hugging Face ๐Ÿค—\n", + "- Access images, captions, and diagnostic metadata\n", + "- Visualize expert-annotated bounding boxes (gold standard and raters)\n", + "- Explore one of the most challenging testbeds for generalization and reasoning under uncertainty\n", + "\n", + "> โš ๏ธ This benchmark is intended **only for evaluation**. No training should be performed on NOVA.\n", + "\n", + "๐Ÿ“˜ For more details, visit the [dataset page on Hugging Face](https://huggingface.co/datasets/Ano-2090/Nova).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "matplotlib.use('agg')\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as patches\n", + "from datasets import load_dataset\n", + "import random" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load dataset\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds = load_dataset(\"Ano-2090/Nova\", trust_remote_code=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Select a random example\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "example = random.choice(ds[\"test\"])\n", + "image = example[\"image\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create figure and display image\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, figsize=(8, 8))\n", + "ax.imshow(image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot gold standard bounding boxes (gold)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bbox = example[\"bbox_gold\"]\n", + "for x, y, w, h in zip(bbox[\"x\"], bbox[\"y\"], bbox[\"width\"], bbox[\"height\"]):\n", + " rect = patches.Rectangle((x, y), w, h, linewidth=2, edgecolor=\"gold\", facecolor=\"none\")\n", + " ax.add_patch(rect)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot rater bounding boxes (turquoise, salmon) with labels\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "colors = ['#40E0D0', '#FA8072']\n", + "raters = example[\"bbox_raters\"]\n", + "if raters:\n", + " for i in range(len(raters[\"x\"])):\n", + " rater = raters[\"rater\"][i]\n", + " x = raters[\"x\"][i]\n", + " y = raters[\"y\"][i]\n", + " w = raters[\"width\"][i]\n", + " h = raters[\"height\"][i]\n", + " rect = patches.Rectangle((x, y), w, h, linewidth=1.5, edgecolor=colors[i], facecolor=\"none\", linestyle=\"--\")\n", + " ax.add_patch(rect)\n", + " if i == 0:\n", + " ax.text(x, y - 5, rater, color=colors[i], fontsize=8, weight=\"bold\", va=\"bottom\")\n", + " else: \n", + " ax.text(x + w/2, y + h + 15, rater, color=colors[i], fontsize=8, weight=\"bold\", va=\"bottom\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualize example\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.title(f'{example[\"filename\"]} โ€” {example[\"final_diagnosis\"]}', fontsize=12)\n", + "plt.axis(\"off\")\n", + "plt.tight_layout()\n", + "display(fig)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Print other metadata \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('*-------------------------------------------------------*')\n", + "print('*-------------------------------------------------------*')\n", + "print('caption:', example[\"caption\"])\n", + "print('*-------------------------------------------------------*')\n", + "print('clinical history:', example[\"clinical_history\"])\n", + "print('*-------------------------------------------------------*')\n", + "print('differential diagnosis:', example[\"differential_diagnosis\"])\n", + "print('*-------------------------------------------------------*')\n", + "print('final diagnosis:', example[\"final_diagnosis\"])\n", + "print('*-------------------------------------------------------*')\n", + "print('*-------------------------------------------------------*')" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/images/.DS_Store b/images/.DS_Store deleted file mode 100644 index 4c47dd0b5cd92eb2573cb2b1a6e3c11f388bd0cd..0000000000000000000000000000000000000000 --- a/images/.DS_Store +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:05b3f6f9188e85e8fcd43191fea5fa2ec211705f88e40930d604e1a31b66edad -size 6148 diff --git a/images/case0001_001.png b/images/case0001_001.png deleted file mode 100644 index 630edbe48417ee24326d7b749f00bc23ca85eb94..0000000000000000000000000000000000000000 --- a/images/case0001_001.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c6030fbeb7fdf1ff768d47cd7f9b3cbdd6e6ce2ef2a80f995221b5464baf54bb -size 110619 diff --git 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"id": "0fsKoLM5sjn_" - }, - "outputs": [], - "source": [ - "# Colab Setup Cell\n", - "# Install necessary packages\n", - "!pip -q install -U datasets==3.1.0 pillow pyarrow matplotlib tqdm huggingface_hub\n", - "\n", - "from pathlib import Path\n", - "from datasets import load_dataset, Image\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.patches as patches\n", - "from PIL import Image as PILImage\n", - "from tqdm.auto import tqdm\n", - "from huggingface_hub import hf_hub_url, notebook_login, hf_hub_download # Use hf_hub_download\n", - "import os # Necessary for creating folders\n", - "\n", - "# --- HUGGING FACE AUTHENTICATION (Keep this) ---\n", - "print(\"--- Authenticating with Hugging Face ---\")\n", - "notebook_login()\n", - "\n", - "# --- Configuration ---\n", - "DATASET_ID = \"c-i-ber/Nova\"\n", - "SPLIT_NAME = \"train\"\n", - "PARQUET_FILE_URL = f\"hf://datasets/{DATASET_ID}/data/nova-v1.parquet\"\n", - "\n", - "# --- LOCAL FOLDER CONSTANT ---\n", - "LOCAL_IMAGE_DIR = \"images\" # The folder where images will be saved\n", - "\n", - "MAX_BOXES_TO_DRAW = 5\n", - "\n", - "# --- Visualization Style Constants (Unchanged) ---\n", - "BOX_COLOR = 'cyan'\n", - "TEXT_COLOR = 'black'\n", - "BOX_LABEL_BG_COLOR = 'white'\n", - "TITLE_FONT_SIZE = 12\n", - "METADATA_SECTION_TITLE_SIZE = 12\n", - "METADATA_TEXT_SIZE = 10" - ] - }, - { - "cell_type": "code", - "source": [ - "# --- HUGGING FACE AUTHENTICATION FIX ---\n", - "from huggingface_hub import notebook_login\n", - "import os\n", - "\n", - "# 1. Log in to Hugging Face\n", - "print(\"--- Authenticating with Hugging Face ---\")\n", - "# This command will open an interactive prompt where you paste your token.\n", - "# You can get a token from: https://huggingface.co/settings/tokens\n", - "notebook_login()" - ], - "metadata": { - "id": "yRAVEjw-28yU" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "def load_and_prepare_dataset(parquet_url: str) -> \"datasets.Dataset\":\n", - " \"\"\"\n", - " Loads the dataset structure (metadata) directly from the remote Parquet file.\n", - " \"\"\"\n", - " print(f\"Loading metadata structure from: {parquet_url}\")\n", - " try:\n", - " ds = load_dataset(\"parquet\", data_files=parquet_url, split=SPLIT_NAME)\n", - " except Exception as e:\n", - " print(f\"\\n๐Ÿšจ Fatal Error: Could not load Parquet file from URL. Details: {e}\")\n", - " return None\n", - "\n", - " print(f\"\\nDataset loaded successfully. Total examples: {len(ds)}\")\n", - " if \"bboxes\" in ds.column_names:\n", - " print(\"โœ… SUCCESS: All metadata columns loaded correctly.\")\n", - " else:\n", - " print(\"๐Ÿšจ CRITICAL ERROR: 'bboxes' column is missing.\")\n", - "\n", - " return ds\n", - "\n", - "\n", - "def cache_all_images(dataset: \"datasets.Dataset\", dataset_id: str):\n", - " \"\"\"\n", - " Downloads all images from the HF Hub to the local folder and saves them as JPEGs.\n", - " \"\"\"\n", - " Path(LOCAL_IMAGE_DIR).mkdir(exist_ok=True)\n", - " print(f\"\\n--- Downloading and Caching All Images to: {LOCAL_IMAGE_DIR}/ ---\")\n", - "\n", - " for example in tqdm(dataset, desc=\"Downloading and saving images\", unit=\"img\"):\n", - " try:\n", - " # 1. Get the image's remote path and local target path\n", - " image_url_path = example['image_path']\n", - "\n", - " # The filename is the part after the last slash\n", - " filename = Path(image_url_path).name\n", - " local_save_path = Path(LOCAL_IMAGE_DIR) / filename\n", - "\n", - " # Skip if the file already exists locally\n", - " if local_save_path.exists():\n", - " continue\n", - "\n", - " # 2. Download the file's absolute path from HF cache\n", - " # This uses the correct function and full internal path\n", - " repo_file_path = image_url_path\n", - " cached_file_path = hf_hub_download(\n", - " repo_id=dataset_id,\n", - " filename=repo_file_path,\n", - " repo_type=\"dataset\"\n", - " )\n", - "\n", - " # 3. Open the downloaded file from the cache and save it to the local folder\n", - " img = PILImage.open(cached_file_path)\n", - " img.save(local_save_path)\n", - "\n", - " except Exception as e:\n", - " print(f\"\\nWarning: Could not process image {example.get('filename')}. Error: {e}\")\n", - "\n", - " print(\"Caching complete. Demo examples will load instantly from the local folder.\")\n", - "\n", - "\n", - "def load_local_image(filename: str) -> PILImage.Image:\n", - " \"\"\"Loads an image from the local directory using its simple filename.\"\"\"\n", - " local_path = Path(LOCAL_IMAGE_DIR) / filename\n", - " return PILImage.open(local_path)\n", - "\n", - "\n", - "# --- Example Selector Functions (Unchanged) ---\n", - "# ... (has_boxes, get_example_by_filename, find_first_example_with_boxes are here) ...\n", - "\n", - "def has_boxes(example: dict) -> bool:\n", - " bboxes = example.get(\"bboxes\", None)\n", - " return isinstance(bboxes, list) and len(bboxes) > 0\n", - "\n", - "def get_example_by_filename(dataset: \"datasets.Dataset\", filename: str) -> dict:\n", - " \"\"\"Retrieves an example from the dataset by its 'filename'.\"\"\"\n", - " for i, ex in tqdm(\n", - " enumerate(dataset),\n", - " total=len(dataset),\n", - " desc=f\"Searching for '{filename}'\",\n", - " unit=\"ex\",\n", - " leave=False\n", - " ):\n", - " if ex.get(\"filename\") == filename:\n", - " return dataset[i]\n", - " raise ValueError(f\"Example with filename '{filename}' not found.\")\n", - "\n", - "def find_first_example_with_boxes(dataset: \"datasets.Dataset\") -> dict:\n", - " \"\"\"Finds the first example in the dataset that contains bounding boxes.\"\"\"\n", - " print(\"Searching for first example with bounding boxes...\")\n", - " for ex in tqdm(dataset, desc=\"Finding example with boxes\", unit=\"ex\"):\n", - " if has_boxes(ex):\n", - " return ex\n", - "\n", - " print(\"Warning: No example with bounding boxes found. Displaying index 0 instead.\")\n", - " return dataset[0]" - ], - "metadata": { - "id": "5u2Ezk2YFh3o" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Execute the loading\n", - "ds = load_and_prepare_dataset(PARQUET_FILE_URL)\n", - "\n", - "if ds is None:\n", - " raise RuntimeError(\"Dataset loading failed. Cannot proceed with demo.\")\n", - "\n", - "# --- STEP 2: CACHE ALL IMAGES ---\n", - "cache_all_images(ds, DATASET_ID)" - ], - "metadata": { - "id": "hPB6_pzXIBup" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "def display_nova_example(dataset: \"datasets.Dataset\", selector: str | int = None):\n", - " # --- Selection Logic (Unchanged) ---\n", - " example = None\n", - " try:\n", - " if isinstance(selector, int):\n", - " if 0 <= selector < len(dataset):\n", - " example = dataset[selector]\n", - " else:\n", - " raise IndexError(f\"Index {selector} out of bounds for dataset of size {len(dataset)}.\")\n", - " elif isinstance(selector, str):\n", - " example = get_example_by_filename(dataset, selector)\n", - " elif selector is None:\n", - " example = find_first_example_with_boxes(dataset)\n", - " else:\n", - " raise TypeError(f\"Selector must be an integer index or a string filename, got {type(selector)}.\")\n", - "\n", - " if example is None: return\n", - "\n", - " filename = example.get('filename', 'N/A')\n", - " total_boxes = len(example.get('bboxes', []))\n", - " print(f\"\\n--- Displaying File: {filename} | Total Boxes: {total_boxes} ---\")\n", - "\n", - " except (ValueError, IndexError, TypeError) as e:\n", - " print(f\"Error selecting example: {e}\")\n", - " return\n", - "\n", - " # --- IMAGE LOADING (FAST, LOCAL) ---\n", - " try:\n", - " # Load the image using its filename from the local folder\n", - " img = load_local_image(filename)\n", - "\n", - " except Exception as e:\n", - " print(f\"\\n๐Ÿ›‘ Fatal Error: Image for file {filename} could not be loaded from local path. Error: {e}\")\n", - " return\n", - "\n", - " # --- Visualization Setup (Plotting - Unchanged) ---\n", - " fig, (ax_img, ax_meta) = plt.subplots(1, 2, figsize=(14, 7), gridspec_kw={'width_ratios': [1, 1]})\n", - "\n", - " # Left Subplot: Image and Bounding Boxes\n", - " ax_img.imshow(img)\n", - " bboxes = example.get(\"bboxes\", [])\n", - "\n", - " img_title = f\"Image: {filename} ({total_boxes} Boxes)\"\n", - " ax_img.set_title(img_title, fontsize=TITLE_FONT_SIZE, fontweight='bold')\n", - "\n", - " # Draw Bounding Boxes\n", - " for i, b in enumerate(bboxes[:MAX_BOXES_TO_DRAW]):\n", - " x, y, w, h = b[\"x\"], b[\"y\"], b[\"width\"], b[\"height\"]\n", - " source = b.get(\"source\", \"N/A\")\n", - "\n", - " rect = patches.Rectangle((x, y), w, h,\n", - " linewidth=2,\n", - " edgecolor=BOX_COLOR,\n", - " linestyle='-',\n", - " fill=False)\n", - " ax_img.add_patch(rect)\n", - "\n", - " ax_img.text(x, y - 5, f\"Source: {source}\",\n", - " fontsize=8,\n", - " color=TEXT_COLOR,\n", - " bbox=dict(facecolor=BOX_LABEL_BG_COLOR, alpha=0.8, edgecolor='none', pad=2))\n", - "\n", - " ax_img.axis(\"off\")\n", - "\n", - " # Right Subplot: Structured Metadata\n", - " ax_meta.set_axis_off()\n", - " ax_meta.set_xlim(0, 1)\n", - " ax_meta.set_ylim(0, 1)\n", - "\n", - " y_pos = 0.95\n", - " x_pos = 0.05\n", - " line_height = 0.08\n", - "\n", - " ax_meta.text(x_pos, y_pos, \"Metadata Details\", fontsize=METADATA_SECTION_TITLE_SIZE + 2,\n", - " fontweight='bold', transform=ax_meta.transAxes)\n", - " y_pos -= line_height * 1.5\n", - "\n", - " # Caption\n", - " caption = example.get(\"caption_text\", \"N/A (No caption available)\")\n", - " ax_meta.text(x_pos, y_pos, \"Caption:\", fontsize=METADATA_SECTION_TITLE_SIZE,\n", - " fontweight='bold', transform=ax_meta.transAxes)\n", - " y_pos -= line_height\n", - "\n", - " wrapped_caption = \"\\n\".join([caption[i:i+60] for i in range(0, len(caption), 60)])\n", - " ax_meta.text(x_pos, y_pos, wrapped_caption, fontsize=METADATA_TEXT_SIZE, transform=ax_meta.transAxes,\n", - " verticalalignment='top')\n", - " y_pos -= line_height * (wrapped_caption.count('\\n') + 2)\n", - "\n", - " # Clinical and Publication Details\n", - " meta = example.get(\"meta\", {})\n", - " ax_meta.text(x_pos, y_pos, \"Clinical & Publication:\", fontsize=METADATA_SECTION_TITLE_SIZE,\n", - " fontweight='bold', transform=ax_meta.transAxes)\n", - " y_pos -= line_height\n", - "\n", - " details = [\n", - " (\"Final Diagnosis:\", meta.get('final_diagnosis', 'N/A')),\n", - " (\"Title:\", meta.get('title', 'N/A')),\n", - " (\"Publication Date:\", meta.get('publication_date', 'N/A')),\n", - " (\"Link:\", meta.get('link', 'N/A'))\n", - " ]\n", - "\n", - " for label, value in details:\n", - " ax_meta.text(x_pos, y_pos, f\"{label:<18} {value}\", fontsize=METADATA_TEXT_SIZE, transform=ax_meta.transAxes)\n", - " y_pos -= line_height\n", - "\n", - " plt.tight_layout()\n", - " plt.show()" - ], - "metadata": { - "id": "-QzYNu9dFtG9" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# --- Demo Examples (Instant after initial caching) ---\n", - "\n", - "# 1. Default Example: Find the first entry that has bounding boxes\n", - "print(\"--- DEMO 1: Displaying first example with boxes ---\")\n", - "display_nova_example(ds)\n", - "\n", - "# 2. Select by Integer Index\n", - "print(\"\\n--- DEMO 2: Select by Index (95) ---\")\n", - "display_nova_example(ds, selector=543)\n", - "\n", - "# 3. Select by String Filename\n", - "print(\"\\n--- DEMO 3: Select by Filename (Logical ID) ---\")\n", - "display_nova_example(ds, selector=ds[0]['filename'])" - ], - "metadata": { - "id": "6O1rffbKwOxV" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "import os\n" - ], - "metadata": { - "id": "zZ8r9w7YFa6Y" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "mPsz_1vZLIFM" - }, - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file diff --git a/test/Images.zip b/test/Images.zip new file mode 100644 index 0000000000000000000000000000000000000000..c9333f637e105ec60255a7ec540b5b838f133356 --- /dev/null +++ b/test/Images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0a87feb802ea8cd5bb5edb14c12f098268fa08e7be4f9aacc3b0ba6f4dbb4bad +size 126289347