Duplicate from asafaa/imagent100

Co-authored-by: asaf <asafaa@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow 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
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+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
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+*.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
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+*.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
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+*.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
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+*.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

Labels.json

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+{
+    "n01498041": "stingray",
+    "n01629819": "European fire salamander, Salamandra salamandra",
+    "n01631663": "eft",
+    "n01641577": "bullfrog, Rana catesbeiana",
+    "n01644900": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui",
+    "n01689811": "alligator lizard",
+    "n01693334": "green lizard, Lacerta viridis",
+    "n01770393": "scorpion",
+    "n01795545": "black grouse",
+    "n01796340": "ptarmigan",
+    "n01819313": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita",
+    "n01833805": "hummingbird",
+    "n01843065": "jacamar",
+    "n01855672": "goose",
+    "n01873310": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus",
+    "n01877812": "wallaby, brush kangaroo",
+    "n01945685": "slug",
+    "n02002556": "white stork, Ciconia ciconia",
+    "n02033041": "dowitcher",
+    "n02087394": "Rhodesian ridgeback",
+    "n02088466": "bloodhound, sleuthhound",
+    "n02089867": "Walker hound, Walker foxhound",
+    "n02097047": "miniature schnauzer",
+    "n02098286": "West Highland white terrier",
+    "n02101006": "Gordon setter",
+    "n02102480": "Sussex spaniel",
+    "n02104365": "schipperke",
+    "n02105162": "malinois",
+    "n02105505": "komondor",
+    "n02106382": "Bouvier des Flandres, Bouviers des Flandres",
+    "n02107574": "Greater Swiss Mountain dog",
+    "n02110185": "Siberian husky",
+    "n02114548": "white wolf, Arctic wolf, Canis lupus tundrarum",
+    "n02123045": "tabby, tabby cat",
+    "n02123597": "Siamese cat, Siamese",
+    "n02134084": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus",
+    "n02165105": "tiger beetle",
+    "n02398521": "hippopotamus, hippo, river horse, Hippopotamus amphibius",
+    "n02412080": "ram, tup",
+    "n02422699": "impala, Aepyceros melampus",
+    "n02454379": "armadillo",
+    "n02481823": "chimpanzee, chimp, Pan troglodytes",
+    "n02484975": "guenon, guenon monkey",
+    "n02487347": "macaque",
+    "n02492660": "howler monkey, howler",
+    "n02509815": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens",
+    "n02514041": "barracouta, snoek",
+    "n02799071": "baseball",
+    "n02804610": "bassoon",
+    "n02892767": "brassiere, bra, bandeau",
+    "n02910353": "buckle",
+    "n02948072": "candle, taper, wax light",
+    "n03126707": "crane2",
+    "n03291819": "envelope",
+    "n03372029": "flute, transverse flute",
+    "n03393912": "freight car",
+    "n03445777": "golf ball",
+    "n03485794": "handkerchief, hankie, hanky, hankey",
+    "n03538406": "horse cart, horse-cart",
+    "n03544143": "hourglass",
+    "n03627232": "knot",
+    "n03633091": "ladle",
+    "n03692522": "loupe, jeweler's loupe",
+    "n03724870": "mask",
+    "n03759954": "microphone, mike",
+    "n03769881": "minibus",
+    "n03785016": "moped",
+    "n03804744": "nail",
+    "n03843555": "oil filter",
+    "n03871628": "packet",
+    "n03929660": "pick, plectrum, plectron",
+    "n03933933": "pier",
+    "n03938244": "pillow",
+    "n03976657": "pole",
+    "n04023962": "punching bag, punch bag, punching ball, punchball",
+    "n04041544": "radio, wireless",
+    "n04067472": "reel",
+    "n04069434": "reflex camera",
+    "n04141327": "scabbard",
+    "n04149813": "scoreboard",
+    "n04204347": "shopping cart",
+    "n04326547": "stone wall",
+    "n04328186": "stopwatch, stop watch",
+    "n04332243": "strainer",
+    "n04398044": "teapot",
+    "n04418357": "theater curtain, theatre curtain",
+    "n04467665": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
+    "n04487394": "trombone",
+    "n04517823": "vacuum, vacuum cleaner",
+    "n04532106": "vestment",
+    "n04540053": "volleyball",
+    "n04591157": "Windsor tie",
+    "n04606251": "wreck",
+    "n07734744": "mushroom",
+    "n07753592": "banana",
+    "n07875152": "potpie",
+    "n09428293": "seashore, coast, seacoast, sea-coast",
+    "n10565667": "scuba diver",
+    "n12057211": "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
+    "n12768682": "buckeye, horse chestnut, conker"
+}

README.md

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+---
+license: apache-2.0
+task_categories:
+- image-classification
+tags:
+- imagenet
+- computer-vision
+- imagenet-100
+- image-compression
+- decolorization
+size_categories:
+- 100K<n<1M
+---
+
+# ImageNet-100 Dataset (Zipped ImageFolder)
+
+### Overview
+This dataset is a 100-class subset of the ImageNet-2012 (ILSVRC2012) dataset. It was specifically curated for academic research in computer vision, including tasks such as image compression and color-to-grayscale conversion (decolorization).
+
+### Dataset Details
+* **Class Selection**: 100 classes were selected using a fixed random seed (42) to ensure reproducibility.
+* **Format**: The dataset is stored as zipped chunks (10 files total) to facilitate stable uploads and high-speed downloads.
+* **Total Splits**:
+    * **Train**: ~130,000 images, distributed across 5 ZIP chunks (`part1` to `part5`).
+    * **Validation**: 5,000 images, distributed across 5 ZIP chunks (`part1` to `part5`).
+* **Labels**: A complete mapping of WordNet IDs to human-readable labels is included in the `Labels.json` file.
+
+### 📂 Class Categories
+The 100 selected classes cover a diverse range of categories:
+
+| Category | Count | Examples |
+| :--- | :---: | :--- |
+| **Canines (Dogs)** | 13 | Siberian husky, Bloodhound, Miniature schnauzer |
+| **Birds** | 8 | Hummingbird, Sulphur-crested cockatoo, Goose |
+| **Primates** | 4 | Chimpanzee, Howler monkey, Macaque |
+| **Wild Mammals** | 12 | Polar bear, Hippopotamus, Red panda, White wolf |
+| **Reptiles & Fish** | 8 | Stingray, Bullfrog, Alligator lizard |
+| **Vehicles** | 5 | Minibus, Moped, Trailer truck |
+| **Instruments** | 3 | Flute, Bassoon, Trombone |
+| **Household Items** | 18 | Teapot, Hourglass, Vacuum cleaner, Reflex camera |
+| **Food & Nature** | 7 | Banana, Mushroom, Seashore, Potpie |
+| **Sports & Other** | 22 | Volleyball, Baseball, Scuba diver, Stone wall |
+
+---
+
+### How to Use
+
+#### 1. Automatic Download and Extraction
+Since the data is split into multiple chunks, use the following script to reconstruct the `ImageFolder` structure.
+
+```python
+import os
+import zipfile
+from huggingface_hub import hf_hub_download
+
+repo_id = "asafaa/imagent100"
+target_dir = "./imagenet100"
+
+def download_and_extract(split, num_parts=5):
+    for i in range(1, num_parts + 1):
+        filename = f"imagenet100_{split}_part{i}.zip"
+        print(f"Downloading {filename}...")
+        path = hf_hub_download(repo_id=repo_id, filename=filename, repo_type="dataset")
+        
+        with zipfile.ZipFile(path, 'r') as zip_ref:
+            zip_ref.extractall(target_dir)
+    print(f"Finished extracting {split} split.")
+
+# Download both splits
+download_and_extract("train")
+download_and_extract("val")

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