stevenbucaille/image-classification-models-dataset

model_id stringlengths 7 105	model_card stringlengths 1 130k	model_labels listlengths 2 80k
amyeroberts/my_food_classifier	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # amyeroberts/my_food_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.5833 - Validation Loss: 4.5438 - Train Accuracy: 0.125 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 4.5833 \| 4.5438 \| 0.125 \| 0 \| ### Framework versions - Transformers 4.26.0.dev0 - TensorFlow 2.10.0 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2	[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]
poolrf2001/platzi-vit-model-pool-river	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # platzi-vit-model-pool-river This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0096 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1561 \| 3.85 \| 500 \| 0.0096 \| 1.0 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2	[ "angular_leaf_spot", "bean_rust", "healthy" ]
kmewhort/beit-sketch-classifier-pt-metaset-2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # beit-sketch-classifier-pt-metaset-2 This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6703 - Accuracy: 0.8282 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\| \| 0.8028 \| 1.0 \| 76608 \| 0.7586 \| 0.8007 \| \| 0.7168 \| 2.0 \| 153216 \| 0.6983 \| 0.8154 \| \| 0.6357 \| 3.0 \| 229824 \| 0.6676 \| 0.8240 \| \| 0.5707 \| 4.0 \| 306432 \| 0.6606 \| 0.8276 \| \| 0.4254 \| 5.0 \| 383040 \| 0.6703 \| 0.8282 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2	[ "the eiffel tower", "the great wall of china", "the mona lisa", "aircraft carrier", "airplane", "alarm clock", "alien", "ambulance", "angel", "animal migration", "ant", "anvil", "ape", "apple", "arm", "armchair", "armor", "ashtray", "asparagus", "axe", "backpack", "banana", "bandage", "barn", "baseball", "baseball bat", "basket", "basketball", "bat", "bathtub", "beach", "bear", "beard", "bed", "bee", "beer mug", "beetle", "bell", "belt", "bench", "bicycle", "binoculars", "bird", "birthday cake", "blackberry", "blimp", "blueberry", "book", "bookshelf", "boomerang", "bottle opener", "bottlecap", "bowl", "bowtie", "boy", "bracelet", "brain", "bread", "bridge", "broccoli", "broom", "bucket", "bulldozer", "bus", "bush", "butterfly", "cabin", "cabinet", "cactus", "cake", "calculator", "calendar", "camel", "camera", "camouflage", "campfire", "candle", "cannon", "canoe", "car", "carrot", "castle", "cat", "ceiling fan", "cell phone", "cello", "chair", "chandelier", "chicken", "church", "cigarette", "circle", "clarinet", "clock", "cloud", "coffee cup", "comb", "compass", "computer", "computer monitor", "computer-mouse", "cookie", "cooler", "couch", "cow", "crab", "crane (machine)", "crayon", "crocodile", "crocodilian", "crown", "cruise ship", "cup", "deer", "diamond", "dishwasher", "diving board", "dog", "dolphin", "donut", "door", "door handle", "dragon", "dresser", "drill", "drums", "duck", "dumbbell", "ear", "elbow", "elephant", "elf", "envelope", "eraser", "eye", "eyeglasses", "face", "fairy", "fan", "feather", "fence", "finger", "fire hydrant", "fireplace", "firetruck", "fish", "flamingo", "flashlight", "flip flops", "floor lamp", "flower", "flower with stem", "flying bird", "flying saucer", "foot", "fork", "frog", "frying pan", "garden", "garden hose", "geyser", "giraffe", "girl", "goatee", "golf club", "grapes", "grass", "grenade", "guitar", "hamburger", "hammer", "hand", "harp", "hat", "head", "headphones", "hedgehog", "helicopter", "helmet", "hexagon", "hockey puck", "hockey stick", "horse", "hospital", "hot air balloon", "hot dog", "hot tub", "hot-air balloon", "hotdog", "hourglass", "house", "house plant", "human-skeleton", "hurricane", "ice cream", "ipod", "jack-o-lantern", "jacket", "jail", "jellyfish", "kangaroo", "key", "keyboard", "knee", "knife", "ladder", "lantern", "laptop", "leaf", "leg", "light bulb", "lighter", "lighthouse", "lightning", "line", "lion", "lipstick", "lizard", "lobster", "lollipop", "loudspeaker", "mailbox", "man", "map", "marker", "matches", "megaphone", "mermaid", "microphone", "microscope", "microwave", "monkey", "moon", "mosquito", "motorbike", "motorcycle", "mountain", "mouse", "moustache", "mouth", "mug", "mushroom", "nail", "necklace", "nose", "ocean", "octagon", "octopus", "onion", "oven", "owl", "paint can", "paintbrush", "palm tree", "panda", "pants", "paper clip", "parachute", "parking meter", "parrot", "passport", "peanut", "pear", "peas", "pen", "pencil", "penguin", "person sitting", "person standing", "person walking", "piano", "pickup truck", "picture frame", "pig", "pigeon", "pillow", "pineapple", "pipe (for smoking)", "pistol", "pizza", "planet", "pliers", "police car", "pond", "pool", "popsicle", "postcard", "potato", "potted plant", "power outlet", "present", "pretzel", "pumpkin", "purse", "rabbit", "raccoon", "race car", "racket", "radio", "rain", "rainbow", "rake", "ray", "remote control", "revolver", "rhinoceros", "rifle", "river", "robot", "rocket", "roller coaster", "rollerblades", "rollerskates", "rooster", "sailboat", "sandwich", "santa claus", "satellite", "satellite dish", "saw", "saxophone", "school bus", "scissors", "scorpion", "screwdriver", "sea turtle", "seagull", "seal", "see saw", "shark", "sheep", "ship", "shoe", "shorts", "shovel", "sink", "skateboard", "skull", "skyscraper", "sleeping bag", "smiley face", "snail", "snake", "snorkel", "snowboard", "snowflake", "snowman", "soccer ball", "sock", "socks", "songbird", "space shuttle", "speedboat", "spider", "sponge bob", "spoon", "spreadsheet", "square", "squiggle", "squirrel", "stairs", "stapler", "star", "starfish", "steak", "stereo", "stethoscope", "stitches", "stop sign", "stove", "strawberry", "streetlight", "string bean", "submarine", "suitcase", "sun", "suv", "swan", "sweater", "swing set", "sword", "syringe", "t-shirt", "table", "tablelamp", "tank", "teacup", "teapot", "teddy-bear", "telephone", "television", "tennis-racket", "tent", "tiger", "tire", "toaster", "toe", "toilet", "tomato", "tooth", "toothbrush", "toothpaste", "tornado", "tractor", "traffic light", "train", "tree", "triangle", "trombone", "trousers", "truck", "trumpet", "turtle", "tv", "umbrella", "underwear", "van", "vase", "violin", "volcano", "walkie talkie", "washing machine", "watermelon", "waterslide", "whale", "wheel", "wheelbarrow", "wheelchair", "windmill", "window", "wine bottle", "wine glass", "wineglass", "wizard", "woman", "wristwatch", "yoga", "zebra", "zigzag" ]
jctivensa/Ivenpeople_v2	license: apache-2.0 tags: - vision - image-classification datasets: - imagenet-1k widget: - src: >- https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: >- https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: >- https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace	[ "male", "female", "0-19", "20-29", "30-39", "40-69", "70+" ]
swww/autotrain-mm-2927885005	# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 2927885005 - CO2 Emissions (in grams): 0.3585 ## Validation Metrics - Loss: 0.015 - Accuracy: 1.000 - Precision: 1.000 - Recall: 1.000 - AUC: 1.000 - F1: 1.000	[ "normal", "viral%20pneumonia" ]
swww/autotrain-mm-2927885009	# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 2927885009 - CO2 Emissions (in grams): 0.3748 ## Validation Metrics - Loss: 0.002 - Accuracy: 1.000 - Precision: 1.000 - Recall: 1.000 - AUC: 1.000 - F1: 1.000	[ "normal", "viral%20pneumonia" ]
swww/test	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 2928085012 - CO2 Emissions (in grams): 1.9626 ## Validation Metrics - Loss: 0.226 - Accuracy: 0.925 - Macro F1: 0.925 - Micro F1: 0.925 - Weighted F1: 0.925 - Macro Precision: 0.929 - Micro Precision: 0.925 - Weighted Precision: 0.929 - Macro Recall: 0.925 - Micro Recall: 0.925 - Weighted Recall: 0.925	[ "lung_opacity", "normal", "viral%20pneumonia", "covid" ]
AdamOswald1/autotrain-let-2932785109	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 2932785109 - CO2 Emissions (in grams): 0.0171 ## Validation Metrics - Loss: 1.241 - Accuracy: 0.372 - Macro F1: 0.228 - Micro F1: 0.372 - Weighted F1: 0.344 - Macro Precision: 0.190 - Micro Precision: 0.372 - Weighted Precision: 0.337 - Macro Recall: 0.355 - Micro Recall: 0.372 - Weighted Recall: 0.372	[ "adult chara", "adult chara and young chara", "kris and a soul", "kris next to the ghost of chara", "male kris", "male kris and female kris", "storyshift chara", "storyshift chara and young chara", "teen chara and young chara", "teenager chara and young chara", "young chara", "chara", "female kris", "kris", "kris and adult chara", "kris and chara", "kris and female chara", "kris and male chara", "kris and the player" ]
AdamOswald1/autotrain-let-2932785111	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 2932785111 - CO2 Emissions (in grams): 3.2161 ## Validation Metrics - Loss: 1.165 - Accuracy: 0.376 - Macro F1: 0.269 - Micro F1: 0.376 - Weighted F1: 0.349 - Macro Precision: 0.235 - Micro Precision: 0.376 - Weighted Precision: 0.354 - Macro Recall: 0.413 - Micro Recall: 0.376 - Weighted Recall: 0.376	[ "adult chara", "adult chara and young chara", "kris and a soul", "kris next to the ghost of chara", "male kris", "male kris and female kris", "storyshift chara", "storyshift chara and young chara", "teen chara and young chara", "teenager chara and young chara", "young chara", "chara", "female kris", "kris", "kris and adult chara", "kris and chara", "kris and female chara", "kris and male chara", "kris and the player" ]
ivensamdh/autotrain-convnext_test_masterage-2947785378	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 2947785378 - CO2 Emissions (in grams): 2.5972 ## Validation Metrics - Loss: 1.470 - Accuracy: 0.408 - Macro F1: 0.261 - Micro F1: 0.408 - Weighted F1: 0.392 - Macro Precision: 0.285 - Micro Precision: 0.408 - Weighted Precision: 0.421 - Macro Recall: 0.266 - Micro Recall: 0.408 - Weighted Recall: 0.408	[ "age0to5", "age11to15", "age16to25", "age26to35", "age36to49", "age50to69", "age6to10", "age70to99" ]
jayanta/google-vit-base-patch16-224-cartoon-face-recognition	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # google-vit-base-patch16-224-cartoon-face-recognition This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3707 - Accuracy: 0.9005 - Precision: 0.9066 - Recall: 0.9005 - F1: 0.8984 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00012 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| No log \| 0.89 \| 6 \| 0.5459 \| 0.8611 \| 0.8683 \| 0.8611 \| 0.8577 \| \| 0.0812 \| 1.89 \| 12 \| 0.4703 \| 0.8796 \| 0.8833 \| 0.8796 \| 0.8764 \| \| 0.0812 \| 2.89 \| 18 \| 0.4430 \| 0.8935 \| 0.8969 \| 0.8935 \| 0.8906 \| \| 0.0307 \| 3.89 \| 24 \| 0.4045 \| 0.8819 \| 0.8849 \| 0.8819 \| 0.8767 \| \| 0.0091 \| 4.89 \| 30 \| 0.3672 \| 0.9005 \| 0.9025 \| 0.9005 \| 0.8980 \| \| 0.0091 \| 5.89 \| 36 \| 0.3841 \| 0.9028 \| 0.9125 \| 0.9028 \| 0.9011 \| \| 0.0043 \| 6.89 \| 42 \| 0.3926 \| 0.9005 \| 0.9073 \| 0.9005 \| 0.8972 \| \| 0.0043 \| 7.89 \| 48 \| 0.3786 \| 0.8958 \| 0.9005 \| 0.8958 \| 0.8931 \| \| 0.0031 \| 8.89 \| 54 \| 0.3791 \| 0.9028 \| 0.9091 \| 0.9028 \| 0.9007 \| \| 0.002 \| 9.89 \| 60 \| 0.3677 \| 0.9028 \| 0.9106 \| 0.9028 \| 0.9001 \| \| 0.002 \| 10.89 \| 66 \| 0.3740 \| 0.9028 \| 0.9099 \| 0.9028 \| 0.9007 \| \| 0.0027 \| 11.89 \| 72 \| 0.3869 \| 0.8981 \| 0.9043 \| 0.8981 \| 0.8956 \| \| 0.0027 \| 12.89 \| 78 \| 0.3801 \| 0.8981 \| 0.9021 \| 0.8981 \| 0.8954 \| \| 0.004 \| 13.89 \| 84 \| 0.3674 \| 0.9051 \| 0.9113 \| 0.9051 \| 0.9028 \| \| 0.0024 \| 14.89 \| 90 \| 0.3620 \| 0.9051 \| 0.9096 \| 0.9051 \| 0.9027 \| \| 0.0024 \| 15.89 \| 96 \| 0.3670 \| 0.9028 \| 0.9089 \| 0.9028 \| 0.9006 \| \| 0.0021 \| 16.89 \| 102 \| 0.3827 \| 0.9005 \| 0.9065 \| 0.9005 \| 0.8980 \| \| 0.0021 \| 17.89 \| 108 \| 0.3748 \| 0.8981 \| 0.9049 \| 0.8981 \| 0.8958 \| \| 0.0022 \| 18.89 \| 114 \| 0.3825 \| 0.9028 \| 0.9101 \| 0.9028 \| 0.9006 \| \| 0.0019 \| 19.89 \| 120 \| 0.3707 \| 0.9005 \| 0.9066 \| 0.9005 \| 0.8984 \| ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1	[ "aishwarya_rai", "akhilesh_yadav", "ms_dhoni", "mahatma_gandhi", "mamata_banerjee", "mukesh_ambani", "narendra_modi", "rahul_gandhi", "ranveer_singh", "ratan_tata", "sachin_tendulkar", "shahrukh_khan", "akshay_kumar", "sonia_gandhi", "sundar_pichai", "virat_kohli", "yogi_adityanath", "amit_saha", "arijit_singh", "arvind_kejriwal", "baba_ramdev", "gautam_adani", "kishore_kumar", "lata_mangeshkar" ]
flyswot/autotrain-flyswot-jan-2950385442	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 2950385442 - CO2 Emissions (in grams): 3.5608 ## Validation Metrics - Loss: 0.206 - Accuracy: 0.941 - Macro F1: 0.919 - Micro F1: 0.941 - Weighted F1: 0.940 - Macro Precision: 0.941 - Micro Precision: 0.941 - Weighted Precision: 0.940 - Macro Recall: 0.901 - Micro Recall: 0.941 - Weighted Recall: 0.941	[ "container", "control shot", "cover", "edge + spine", "flysheet", "other", "page + folio", "scroll" ]
Pafebla/autotrain-reconocimiento_banderas-2960885598	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 2960885598 - CO2 Emissions (in grams): 1.3324 ## Validation Metrics - Loss: 0.266 - Accuracy: 0.950 - Macro F1: 0.943 - Micro F1: 0.950 - Weighted F1: 0.949 - Macro Precision: 0.963 - Micro Precision: 0.950 - Weighted Precision: 0.956 - Macro Recall: 0.933 - Micro Recall: 0.950 - Weighted Recall: 0.950	[ "conflictivas", "no_conflictiva", "sin" ]
itslogannye/benignEnchondroma-vs-lowGradeMalignantChondrosarcoma-histopathology	# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 2962985627 - CO2 Emissions (in grams): 3.6593 ## Validation Metrics - Loss: 0.229 - Accuracy: 0.887 - Precision: 0.939 - Recall: 0.821 - AUC: 0.969 - F1: 0.876	[ "enchondroma", "low-grade chondrosarcoma" ]
Rocketknight1/my_food_classifier	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Rocketknight1/my_food_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1099 - Validation Loss: 0.2439 - Train Accuracy: 0.947 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 2.5330 \| 1.3738 \| 0.923 \| 0 \| \| 0.8871 \| 0.6131 \| 0.95 \| 1 \| \| 0.3703 \| 0.4042 \| 0.937 \| 2 \| \| 0.1942 \| 0.2981 \| 0.94 \| 3 \| \| 0.1099 \| 0.2439 \| 0.947 \| 4 \| ### Framework versions - Transformers 4.26.0.dev0 - TensorFlow 2.11.0 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2	[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]
MariaK/my_food_classifier	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # MariaK/my_food_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1163 - Validation Loss: 0.2927 - Train Accuracy: 0.936 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 2.5557 \| 1.4200 \| 0.897 \| 0 \| \| 0.8928 \| 0.6662 \| 0.931 \| 1 \| \| 0.3831 \| 0.4001 \| 0.938 \| 2 \| \| 0.1892 \| 0.3486 \| 0.93 \| 3 \| \| 0.1163 \| 0.2927 \| 0.936 \| 4 \| ### Framework versions - Transformers 4.25.1 - TensorFlow 2.9.2 - Datasets 2.8.0 - Tokenizers 0.13.2	[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]
ivensamdh/genderage2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # genderage2 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2771 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 11 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.3737 \| 0.89 \| 100 \| 0.3702 \| \| 0.3208 \| 1.79 \| 200 \| 0.3308 \| \| 0.2681 \| 2.68 \| 300 \| 0.2977 \| \| 0.2334 \| 3.57 \| 400 \| 0.2875 \| \| 0.2093 \| 4.46 \| 500 \| 0.2840 \| \| 0.1989 \| 5.36 \| 600 \| 0.2798 \| \| 0.1801 \| 6.25 \| 700 \| 0.2785 \| \| 0.1707 \| 7.14 \| 800 \| 0.2771 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2	[ "female", "male", "age0to5", "age6to10", "age11to15", "age16to25", "age26to35", "age36to49", "age50to69", "age70to99" ]
platzi/platzi-vit-model-javi-javiai	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # platzi-vit-model-javi-javiai This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0623 - Accuracy: 0.9774 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0532 \| 3.85 \| 500 \| 0.0623 \| 0.9774 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2	[ "angular_leaf_spot", "bean_rust", "healthy" ]
ivensamdh/genderage_convnext	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # genderage_convnext This model is a fine-tuned version of [facebook/convnext-base-224](https://huggingface.co/facebook/convnext-base-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3896 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 11 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.5114 \| 0.89 \| 100 \| 0.4998 \| \| 0.425 \| 1.79 \| 200 \| 0.4246 \| \| 0.4124 \| 2.68 \| 300 \| 0.4100 \| \| 0.4034 \| 3.57 \| 400 \| 0.4037 \| \| 0.3938 \| 4.46 \| 500 \| 0.3992 \| \| 0.4021 \| 5.36 \| 600 \| 0.3952 \| \| 0.3843 \| 6.25 \| 700 \| 0.3918 \| \| 0.3804 \| 7.14 \| 800 \| 0.3896 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2	[ "female", "male", "age0to5", "age6to10", "age11to15", "age16to25", "age26to35", "age36to49", "age50to69", "age70to99" ]
Arm627/WaspImageRecComap	# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 2977085925 - CO2 Emissions (in grams): 0.8069 ## Validation Metrics - Loss: 0.647 - Accuracy: 0.697 - Precision: 0.065 - Recall: 0.667 - AUC: 0.601 - F1: 0.118	[ "negative", "positive" ]
sbrandeis-test-org/autotrain-retrain-db16d58-2983986070	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 2983986070 - CO2 Emissions (in grams): 0.5760 ## Validation Metrics - Loss: 0.001 - Accuracy: 1.000 - Macro F1: 1.000 - Micro F1: 1.000 - Weighted F1: 1.000 - Macro Precision: 1.000 - Micro Precision: 1.000 - Weighted Precision: 1.000 - Macro Recall: 1.000 - Micro Recall: 1.000 - Weighted Recall: 1.000	[ "adonis", "african giant swallowtail", "american snoot" ]
fernando232s/vit-model1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-model1 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0584 - Accuracy: 0.9774 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1296 \| 3.85 \| 500 \| 0.0584 \| 0.9774 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2	[ "angular_leaf_spot", "bean_rust", "healthy" ]
jayanta/microsoft-resnet-50-cartoon-emotion-detection	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # microsoft-resnet-50-cartoon-emotion-detection This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4801 - Accuracy: 0.8165 - Precision: 0.8182 - Recall: 0.8165 - F1: 0.8173 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00012 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 80 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| No log \| 0.97 \| 8 \| 1.3855 \| 0.2294 \| 0.2697 \| 0.2294 \| 0.2165 \| \| 1.4222 \| 1.97 \| 16 \| 1.3792 \| 0.2569 \| 0.2808 \| 0.2569 \| 0.2543 \| \| 1.4183 \| 2.97 \| 24 \| 1.3646 \| 0.3853 \| 0.4102 \| 0.3853 \| 0.3511 \| \| 1.4097 \| 3.97 \| 32 \| 1.3563 \| 0.4128 \| 0.5062 \| 0.4128 \| 0.3245 \| \| 1.3944 \| 4.97 \| 40 \| 1.3462 \| 0.4037 \| 0.3927 \| 0.4037 \| 0.2939 \| \| 1.3944 \| 5.97 \| 48 \| 1.3223 \| 0.4037 \| 0.5152 \| 0.4037 \| 0.2841 \| \| 1.411 \| 6.97 \| 56 \| 1.3040 \| 0.4128 \| 0.4404 \| 0.4128 \| 0.2985 \| \| 1.346 \| 7.97 \| 64 \| 1.2700 \| 0.4954 \| 0.4960 \| 0.4954 \| 0.4093 \| \| 1.3031 \| 8.97 \| 72 \| 1.2150 \| 0.5596 \| 0.5440 \| 0.5596 \| 0.4672 \| \| 1.2371 \| 9.97 \| 80 \| 1.1580 \| 0.5963 \| 0.5659 \| 0.5963 \| 0.5101 \| \| 1.2371 \| 10.97 \| 88 \| 1.0670 \| 0.6055 \| 0.7279 \| 0.6055 \| 0.5211 \| \| 1.1736 \| 11.97 \| 96 \| 0.9856 \| 0.6606 \| 0.5537 \| 0.6606 \| 0.5772 \| \| 1.0457 \| 12.97 \| 104 \| 0.8963 \| 0.6697 \| 0.7631 \| 0.6697 \| 0.5965 \| \| 0.953 \| 13.97 \| 112 \| 0.8547 \| 0.6697 \| 0.6885 \| 0.6697 \| 0.6081 \| \| 0.8579 \| 14.97 \| 120 \| 0.7849 \| 0.7156 \| 0.7396 \| 0.7156 \| 0.6643 \| \| 0.8579 \| 15.97 \| 128 \| 0.7564 \| 0.7431 \| 0.7372 \| 0.7431 \| 0.7119 \| \| 0.8167 \| 16.97 \| 136 \| 0.7133 \| 0.7615 \| 0.7507 \| 0.7615 \| 0.7211 \| \| 0.7273 \| 17.97 \| 144 \| 0.6888 \| 0.7523 \| 0.7379 \| 0.7523 \| 0.7202 \| \| 0.6547 \| 18.97 \| 152 \| 0.6592 \| 0.7798 \| 0.7773 \| 0.7798 \| 0.7577 \| \| 0.5963 \| 19.97 \| 160 \| 0.6136 \| 0.7706 \| 0.7642 \| 0.7706 \| 0.7551 \| \| 0.5963 \| 20.97 \| 168 \| 0.5723 \| 0.7890 \| 0.7802 \| 0.7890 \| 0.7787 \| \| 0.551 \| 21.97 \| 176 \| 0.5686 \| 0.7890 \| 0.7761 \| 0.7890 \| 0.7781 \| \| 0.4929 \| 22.97 \| 184 \| 0.5597 \| 0.7706 \| 0.7649 \| 0.7706 \| 0.7651 \| \| 0.4309 \| 23.97 \| 192 \| 0.5234 \| 0.7890 \| 0.7774 \| 0.7890 \| 0.7810 \| \| 0.3945 \| 24.97 \| 200 \| 0.5008 \| 0.7890 \| 0.7837 \| 0.7890 \| 0.7813 \| \| 0.3945 \| 25.97 \| 208 \| 0.5289 \| 0.7523 \| 0.7537 \| 0.7523 \| 0.7529 \| \| 0.3704 \| 26.97 \| 216 \| 0.4399 \| 0.7982 \| 0.7958 \| 0.7982 \| 0.7963 \| \| 0.3267 \| 27.97 \| 224 \| 0.4539 \| 0.8073 \| 0.7983 \| 0.8073 \| 0.8005 \| \| 0.2966 \| 28.97 \| 232 \| 0.4735 \| 0.7798 \| 0.7892 \| 0.7798 \| 0.7837 \| \| 0.2645 \| 29.97 \| 240 \| 0.4594 \| 0.7706 \| 0.7706 \| 0.7706 \| 0.7706 \| \| 0.2645 \| 30.97 \| 248 \| 0.4699 \| 0.7523 \| 0.7554 \| 0.7523 \| 0.7533 \| \| 0.2527 \| 31.97 \| 256 \| 0.4551 \| 0.7890 \| 0.7856 \| 0.7890 \| 0.7857 \| \| 0.2202 \| 32.97 \| 264 \| 0.4458 \| 0.8165 \| 0.8198 \| 0.8165 \| 0.8170 \| \| 0.2006 \| 33.97 \| 272 \| 0.4632 \| 0.7798 \| 0.7941 \| 0.7798 \| 0.7850 \| \| 0.1589 \| 34.97 \| 280 \| 0.4651 \| 0.7890 \| 0.7993 \| 0.7890 \| 0.7925 \| \| 0.1589 \| 35.97 \| 288 \| 0.4595 \| 0.7798 \| 0.7824 \| 0.7798 \| 0.7804 \| \| 0.153 \| 36.97 \| 296 \| 0.4584 \| 0.7615 \| 0.7691 \| 0.7615 \| 0.7633 \| \| 0.1427 \| 37.97 \| 304 \| 0.4608 \| 0.7798 \| 0.7830 \| 0.7798 \| 0.7796 \| \| 0.113 \| 38.97 \| 312 \| 0.4571 \| 0.7890 \| 0.7922 \| 0.7890 \| 0.7899 \| \| 0.1146 \| 39.97 \| 320 \| 0.5270 \| 0.7615 \| 0.7651 \| 0.7615 \| 0.7613 \| \| 0.1146 \| 40.97 \| 328 \| 0.4888 \| 0.7706 \| 0.7782 \| 0.7706 \| 0.7710 \| \| 0.1275 \| 41.97 \| 336 \| 0.4523 \| 0.7890 \| 0.7809 \| 0.7890 \| 0.7837 \| \| 0.0959 \| 42.97 \| 344 \| 0.4697 \| 0.7798 \| 0.7753 \| 0.7798 \| 0.7767 \| \| 0.0882 \| 43.97 \| 352 \| 0.4286 \| 0.7706 \| 0.7686 \| 0.7706 \| 0.7686 \| \| 0.0847 \| 44.97 \| 360 \| 0.5317 \| 0.7890 \| 0.7993 \| 0.7890 \| 0.7925 \| \| 0.0847 \| 45.97 \| 368 \| 0.5431 \| 0.7615 \| 0.7700 \| 0.7615 \| 0.7647 \| \| 0.0813 \| 46.97 \| 376 \| 0.4432 \| 0.8257 \| 0.8435 \| 0.8257 \| 0.8284 \| \| 0.0768 \| 47.97 \| 384 \| 0.4886 \| 0.7982 \| 0.8005 \| 0.7982 \| 0.7956 \| \| 0.0627 \| 48.97 \| 392 \| 0.5373 \| 0.7982 \| 0.8072 \| 0.7982 \| 0.8010 \| \| 0.0688 \| 49.97 \| 400 \| 0.5897 \| 0.7798 \| 0.7892 \| 0.7798 \| 0.7822 \| \| 0.0688 \| 50.97 \| 408 \| 0.5115 \| 0.7982 \| 0.8015 \| 0.7982 \| 0.7992 \| \| 0.0676 \| 51.97 \| 416 \| 0.4881 \| 0.7982 \| 0.7998 \| 0.7982 \| 0.7978 \| \| 0.0539 \| 52.97 \| 424 \| 0.4820 \| 0.8073 \| 0.8139 \| 0.8073 \| 0.8077 \| \| 0.0596 \| 53.97 \| 432 \| 0.4450 \| 0.8257 \| 0.8246 \| 0.8257 \| 0.8244 \| \| 0.0611 \| 54.97 \| 440 \| 0.5057 \| 0.7890 \| 0.8008 \| 0.7890 \| 0.7924 \| \| 0.0611 \| 55.97 \| 448 \| 0.4918 \| 0.7982 \| 0.8056 \| 0.7982 \| 0.8008 \| \| 0.0643 \| 56.97 \| 456 \| 0.5946 \| 0.7523 \| 0.7587 \| 0.7523 \| 0.7545 \| \| 0.0605 \| 57.97 \| 464 \| 0.4888 \| 0.8073 \| 0.8239 \| 0.8073 \| 0.8121 \| \| 0.063 \| 58.97 \| 472 \| 0.5917 \| 0.7890 \| 0.8051 \| 0.7890 \| 0.7937 \| \| 0.0595 \| 59.97 \| 480 \| 0.5117 \| 0.7890 \| 0.7904 \| 0.7890 \| 0.7894 \| \| 0.0595 \| 60.97 \| 488 \| 0.5497 \| 0.7615 \| 0.7692 \| 0.7615 \| 0.7635 \| \| 0.0554 \| 61.97 \| 496 \| 0.4742 \| 0.8165 \| 0.8101 \| 0.8165 \| 0.8126 \| \| 0.0557 \| 62.97 \| 504 \| 0.5369 \| 0.7890 \| 0.7886 \| 0.7890 \| 0.7886 \| \| 0.0539 \| 63.97 \| 512 \| 0.5440 \| 0.7890 \| 0.7922 \| 0.7890 \| 0.7899 \| \| 0.048 \| 64.97 \| 520 \| 0.5924 \| 0.7890 \| 0.7878 \| 0.7890 \| 0.7883 \| \| 0.048 \| 65.97 \| 528 \| 0.4863 \| 0.8440 \| 0.8440 \| 0.8440 \| 0.8440 \| \| 0.045 \| 66.97 \| 536 \| 0.5850 \| 0.8073 \| 0.8076 \| 0.8073 \| 0.8047 \| \| 0.047 \| 67.97 \| 544 \| 0.4939 \| 0.8257 \| 0.8212 \| 0.8257 \| 0.8227 \| \| 0.0412 \| 68.97 \| 552 \| 0.4850 \| 0.7890 \| 0.7912 \| 0.7890 \| 0.7900 \| \| 0.0392 \| 69.97 \| 560 \| 0.5066 \| 0.8257 \| 0.8265 \| 0.8257 \| 0.8258 \| \| 0.0392 \| 70.97 \| 568 \| 0.4965 \| 0.8073 \| 0.8053 \| 0.8073 \| 0.8058 \| \| 0.0423 \| 71.97 \| 576 \| 0.4717 \| 0.8349 \| 0.8376 \| 0.8349 \| 0.8351 \| \| 0.0471 \| 72.97 \| 584 \| 0.4845 \| 0.8257 \| 0.8378 \| 0.8257 \| 0.8296 \| \| 0.0322 \| 73.97 \| 592 \| 0.5188 \| 0.7706 \| 0.7689 \| 0.7706 \| 0.7693 \| \| 0.042 \| 74.97 \| 600 \| 0.5242 \| 0.7706 \| 0.7699 \| 0.7706 \| 0.7701 \| \| 0.042 \| 75.97 \| 608 \| 0.5945 \| 0.7798 \| 0.7824 \| 0.7798 \| 0.7804 \| \| 0.0416 \| 76.97 \| 616 \| 0.5432 \| 0.7982 \| 0.8038 \| 0.7982 \| 0.7993 \| \| 0.0399 \| 77.97 \| 624 \| 0.5381 \| 0.7982 \| 0.8072 \| 0.7982 \| 0.7994 \| \| 0.0439 \| 78.97 \| 632 \| 0.6181 \| 0.7798 \| 0.7878 \| 0.7798 \| 0.7827 \| \| 0.0462 \| 79.97 \| 640 \| 0.4801 \| 0.8165 \| 0.8182 \| 0.8165 \| 0.8173 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.11.0	[ "angry1", "happy1", "neutral1", "sad1" ]
jayanta/microsoft-resnet-50-cartoon-face-recognition	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # microsoft-resnet-50-cartoon-face-recognition This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.8508 - Accuracy: 0.7755 - Precision: 0.7715 - Recall: 0.7755 - F1: 0.7676 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00012 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| No log \| 0.89 \| 6 \| 3.1774 \| 0.0370 \| 0.0069 \| 0.0370 \| 0.0098 \| \| 3.4185 \| 1.89 \| 12 \| 3.1739 \| 0.0301 \| 0.0100 \| 0.0301 \| 0.0126 \| \| 3.4185 \| 2.89 \| 18 \| 3.1668 \| 0.0440 \| 0.0805 \| 0.0440 \| 0.0340 \| \| 3.6463 \| 3.89 \| 24 \| 3.1583 \| 0.0370 \| 0.0180 \| 0.0370 \| 0.0151 \| \| 3.3899 \| 4.89 \| 30 \| 3.1425 \| 0.0741 \| 0.0610 \| 0.0741 \| 0.0453 \| \| 3.3899 \| 5.89 \| 36 \| 3.1262 \| 0.0856 \| 0.0334 \| 0.0856 \| 0.0405 \| \| 3.5947 \| 6.89 \| 42 \| 3.1055 \| 0.1019 \| 0.0784 \| 0.1019 \| 0.0481 \| \| 3.5947 \| 7.89 \| 48 \| 3.0841 \| 0.1181 \| 0.1071 \| 0.1181 \| 0.0500 \| \| 3.5553 \| 8.89 \| 54 \| 3.0650 \| 0.1065 \| 0.0216 \| 0.1065 \| 0.0343 \| \| 3.2713 \| 9.89 \| 60 \| 3.0351 \| 0.1273 \| 0.0323 \| 0.1273 \| 0.0418 \| \| 3.2713 \| 10.89 \| 66 \| 3.0069 \| 0.1227 \| 0.0311 \| 0.1227 \| 0.0390 \| \| 3.4382 \| 11.89 \| 72 \| 2.9754 \| 0.1204 \| 0.0353 \| 0.1204 \| 0.0366 \| \| 3.4382 \| 12.89 \| 78 \| 2.9455 \| 0.1227 \| 0.0224 \| 0.1227 \| 0.0338 \| \| 3.3573 \| 13.89 \| 84 \| 2.9167 \| 0.1204 \| 0.0213 \| 0.1204 \| 0.0332 \| \| 3.0549 \| 14.89 \| 90 \| 2.8841 \| 0.1227 \| 0.0474 \| 0.1227 \| 0.0408 \| \| 3.0549 \| 15.89 \| 96 \| 2.8534 \| 0.1412 \| 0.1174 \| 0.1412 \| 0.0540 \| \| 3.1853 \| 16.89 \| 102 \| 2.8143 \| 0.1505 \| 0.1595 \| 0.1505 \| 0.0667 \| \| 3.1853 \| 17.89 \| 108 \| 2.7771 \| 0.1667 \| 0.1693 \| 0.1667 \| 0.0719 \| \| 3.0871 \| 18.89 \| 114 \| 2.7400 \| 0.1759 \| 0.1454 \| 0.1759 \| 0.0896 \| \| 2.7666 \| 19.89 \| 120 \| 2.7048 \| 0.2014 \| 0.0927 \| 0.2014 \| 0.1051 \| \| 2.7666 \| 20.89 \| 126 \| 2.6458 \| 0.2315 \| 0.1622 \| 0.2315 \| 0.1250 \| \| 2.846 \| 21.89 \| 132 \| 2.5803 \| 0.2569 \| 0.2386 \| 0.2569 \| 0.1470 \| \| 2.846 \| 22.89 \| 138 \| 2.5291 \| 0.2639 \| 0.2725 \| 0.2639 \| 0.1523 \| \| 2.7428 \| 23.89 \| 144 \| 2.4916 \| 0.2870 \| 0.2114 \| 0.2870 \| 0.1811 \| \| 2.4183 \| 24.89 \| 150 \| 2.4273 \| 0.3079 \| 0.2322 \| 0.3079 \| 0.2048 \| \| 2.4183 \| 25.89 \| 156 \| 2.3923 \| 0.3194 \| 0.2937 \| 0.3194 \| 0.2238 \| \| 2.5064 \| 26.89 \| 162 \| 2.3349 \| 0.3403 \| 0.3183 \| 0.3403 \| 0.2494 \| \| 2.5064 \| 27.89 \| 168 \| 2.2977 \| 0.3542 \| 0.3554 \| 0.3542 \| 0.2663 \| \| 2.4046 \| 28.89 \| 174 \| 2.2363 \| 0.3773 \| 0.3214 \| 0.3773 \| 0.2981 \| \| 2.1201 \| 29.89 \| 180 \| 2.1791 \| 0.3889 \| 0.4024 \| 0.3889 \| 0.3179 \| \| 2.1201 \| 30.89 \| 186 \| 2.1448 \| 0.4144 \| 0.4079 \| 0.4144 \| 0.3455 \| \| 2.1705 \| 31.89 \| 192 \| 2.0969 \| 0.4306 \| 0.4214 \| 0.4306 \| 0.3583 \| \| 2.1705 \| 32.89 \| 198 \| 2.0535 \| 0.4468 \| 0.4448 \| 0.4468 \| 0.3797 \| \| 2.0295 \| 33.89 \| 204 \| 1.9940 \| 0.4745 \| 0.4877 \| 0.4745 \| 0.4133 \| \| 1.8114 \| 34.89 \| 210 \| 1.9467 \| 0.4861 \| 0.4952 \| 0.4861 \| 0.4261 \| \| 1.8114 \| 35.89 \| 216 \| 1.8896 \| 0.4931 \| 0.4510 \| 0.4931 \| 0.4321 \| \| 1.8048 \| 36.89 \| 222 \| 1.8404 \| 0.5046 \| 0.4859 \| 0.5046 \| 0.4507 \| \| 1.8048 \| 37.89 \| 228 \| 1.7999 \| 0.5278 \| 0.5142 \| 0.5278 \| 0.4816 \| \| 1.6862 \| 38.89 \| 234 \| 1.7363 \| 0.5324 \| 0.5169 \| 0.5324 \| 0.4844 \| \| 1.4545 \| 39.89 \| 240 \| 1.7104 \| 0.5440 \| 0.5100 \| 0.5440 \| 0.4971 \| \| 1.4545 \| 40.89 \| 246 \| 1.6492 \| 0.5648 \| 0.5239 \| 0.5648 \| 0.5138 \| \| 1.4444 \| 41.89 \| 252 \| 1.6076 \| 0.5671 \| 0.5329 \| 0.5671 \| 0.5260 \| \| 1.4444 \| 42.89 \| 258 \| 1.5784 \| 0.5741 \| 0.5708 \| 0.5741 \| 0.5424 \| \| 1.3124 \| 43.89 \| 264 \| 1.5259 \| 0.6019 \| 0.5977 \| 0.6019 \| 0.5619 \| \| 1.1645 \| 44.89 \| 270 \| 1.4814 \| 0.6181 \| 0.6033 \| 0.6181 \| 0.5880 \| \| 1.1645 \| 45.89 \| 276 \| 1.4697 \| 0.6088 \| 0.6033 \| 0.6088 \| 0.5803 \| \| 1.1307 \| 46.89 \| 282 \| 1.4380 \| 0.6088 \| 0.6015 \| 0.6088 \| 0.5769 \| \| 1.1307 \| 47.89 \| 288 \| 1.3872 \| 0.6227 \| 0.6085 \| 0.6227 \| 0.5917 \| \| 1.0347 \| 48.89 \| 294 \| 1.3709 \| 0.6157 \| 0.6039 \| 0.6157 \| 0.5880 \| \| 0.8962 \| 49.89 \| 300 \| 1.3415 \| 0.6296 \| 0.6120 \| 0.6296 \| 0.6057 \| \| 0.8962 \| 50.89 \| 306 \| 1.3290 \| 0.6389 \| 0.6327 \| 0.6389 \| 0.6134 \| \| 0.8898 \| 51.89 \| 312 \| 1.2836 \| 0.6389 \| 0.6192 \| 0.6389 \| 0.6119 \| \| 0.8898 \| 52.89 \| 318 \| 1.2665 \| 0.6412 \| 0.6186 \| 0.6412 \| 0.6162 \| \| 0.7886 \| 53.89 \| 324 \| 1.2272 \| 0.6551 \| 0.6431 \| 0.6551 \| 0.6319 \| \| 0.6794 \| 54.89 \| 330 \| 1.2144 \| 0.6806 \| 0.6643 \| 0.6806 \| 0.6629 \| \| 0.6794 \| 55.89 \| 336 \| 1.1817 \| 0.6806 \| 0.6666 \| 0.6806 \| 0.6642 \| \| 0.6459 \| 56.89 \| 342 \| 1.1702 \| 0.6782 \| 0.6591 \| 0.6782 \| 0.6574 \| \| 0.6459 \| 57.89 \| 348 \| 1.0947 \| 0.7037 \| 0.6863 \| 0.7037 \| 0.6883 \| \| 0.6075 \| 58.89 \| 354 \| 1.1227 \| 0.7037 \| 0.6874 \| 0.7037 \| 0.6867 \| \| 0.4979 \| 59.89 \| 360 \| 1.0849 \| 0.7083 \| 0.6813 \| 0.7083 \| 0.6895 \| \| 0.4979 \| 60.89 \| 366 \| 1.0742 \| 0.7153 \| 0.6924 \| 0.7153 \| 0.6976 \| \| 0.4895 \| 61.89 \| 372 \| 1.0452 \| 0.7245 \| 0.7020 \| 0.7245 \| 0.7057 \| \| 0.4895 \| 62.89 \| 378 \| 1.0435 \| 0.7361 \| 0.7316 \| 0.7361 \| 0.7235 \| \| 0.456 \| 63.89 \| 384 \| 1.0698 \| 0.6921 \| 0.6835 \| 0.6921 \| 0.6783 \| \| 0.3816 \| 64.89 \| 390 \| 1.0126 \| 0.7222 \| 0.7064 \| 0.7222 \| 0.7091 \| \| 0.3816 \| 65.89 \| 396 \| 0.9934 \| 0.7361 \| 0.7247 \| 0.7361 \| 0.7205 \| \| 0.3599 \| 66.89 \| 402 \| 0.9960 \| 0.7292 \| 0.7213 \| 0.7292 \| 0.7170 \| \| 0.3599 \| 67.89 \| 408 \| 1.0141 \| 0.7222 \| 0.7148 \| 0.7222 \| 0.7087 \| \| 0.3484 \| 68.89 \| 414 \| 0.9934 \| 0.7222 \| 0.7125 \| 0.7222 \| 0.7107 \| \| 0.2939 \| 69.89 \| 420 \| 0.9835 \| 0.7431 \| 0.7417 \| 0.7431 \| 0.7349 \| \| 0.2939 \| 70.89 \| 426 \| 0.9870 \| 0.7315 \| 0.7275 \| 0.7315 \| 0.7217 \| \| 0.285 \| 71.89 \| 432 \| 0.9656 \| 0.7431 \| 0.7411 \| 0.7431 \| 0.7340 \| \| 0.285 \| 72.89 \| 438 \| 0.9462 \| 0.7338 \| 0.7320 \| 0.7338 \| 0.7267 \| \| 0.2463 \| 73.89 \| 444 \| 0.9513 \| 0.7454 \| 0.7467 \| 0.7454 \| 0.7384 \| \| 0.2328 \| 74.89 \| 450 \| 0.9334 \| 0.7361 \| 0.7389 \| 0.7361 \| 0.7286 \| \| 0.2328 \| 75.89 \| 456 \| 0.9375 \| 0.7384 \| 0.7278 \| 0.7384 \| 0.7291 \| \| 0.2208 \| 76.89 \| 462 \| 0.9332 \| 0.7407 \| 0.7357 \| 0.7407 \| 0.7322 \| \| 0.2208 \| 77.89 \| 468 \| 0.9408 \| 0.7384 \| 0.7406 \| 0.7384 \| 0.7346 \| \| 0.2177 \| 78.89 \| 474 \| 0.9059 \| 0.7222 \| 0.7183 \| 0.7222 \| 0.7136 \| \| 0.1734 \| 79.89 \| 480 \| 0.9517 \| 0.7315 \| 0.7371 \| 0.7315 \| 0.7257 \| \| 0.1734 \| 80.89 \| 486 \| 0.9063 \| 0.7523 \| 0.7462 \| 0.7523 \| 0.7424 \| \| 0.1791 \| 81.89 \| 492 \| 0.9171 \| 0.7454 \| 0.7461 \| 0.7454 \| 0.7386 \| \| 0.1791 \| 82.89 \| 498 \| 0.8846 \| 0.7523 \| 0.7561 \| 0.7523 \| 0.7485 \| \| 0.1681 \| 83.89 \| 504 \| 0.8871 \| 0.7384 \| 0.7431 \| 0.7384 \| 0.7320 \| \| 0.1573 \| 84.89 \| 510 \| 0.9118 \| 0.7454 \| 0.7474 \| 0.7454 \| 0.7395 \| \| 0.1573 \| 85.89 \| 516 \| 0.9006 \| 0.7407 \| 0.7432 \| 0.7407 \| 0.7366 \| \| 0.1439 \| 86.89 \| 522 \| 0.8703 \| 0.7616 \| 0.7693 \| 0.7616 \| 0.7579 \| \| 0.1439 \| 87.89 \| 528 \| 0.8988 \| 0.7454 \| 0.7570 \| 0.7454 \| 0.7401 \| \| 0.1362 \| 88.89 \| 534 \| 0.9234 \| 0.7454 \| 0.7477 \| 0.7454 \| 0.7396 \| \| 0.1249 \| 89.89 \| 540 \| 0.8860 \| 0.75 \| 0.7473 \| 0.75 \| 0.7425 \| \| 0.1249 \| 90.89 \| 546 \| 0.8608 \| 0.7546 \| 0.7601 \| 0.7546 \| 0.7513 \| \| 0.1264 \| 91.89 \| 552 \| 0.8871 \| 0.7593 \| 0.7640 \| 0.7593 \| 0.7560 \| \| 0.1264 \| 92.89 \| 558 \| 0.8432 \| 0.7639 \| 0.7727 \| 0.7639 \| 0.7599 \| \| 0.1201 \| 93.89 \| 564 \| 0.8654 \| 0.7639 \| 0.7698 \| 0.7639 \| 0.7569 \| \| 0.1117 \| 94.89 \| 570 \| 0.8856 \| 0.7454 \| 0.7569 \| 0.7454 \| 0.7415 \| \| 0.1117 \| 95.89 \| 576 \| 0.8668 \| 0.7546 \| 0.7686 \| 0.7546 \| 0.7535 \| \| 0.1128 \| 96.89 \| 582 \| 0.8630 \| 0.7662 \| 0.7698 \| 0.7662 \| 0.7619 \| \| 0.1128 \| 97.89 \| 588 \| 0.8551 \| 0.7731 \| 0.7826 \| 0.7731 \| 0.7696 \| \| 0.1155 \| 98.89 \| 594 \| 0.8697 \| 0.7708 \| 0.7738 \| 0.7708 \| 0.7643 \| \| 0.0987 \| 99.89 \| 600 \| 0.8613 \| 0.7546 \| 0.7518 \| 0.7546 \| 0.7484 \| \| 0.0987 \| 100.89 \| 606 \| 0.8742 \| 0.7569 \| 0.7597 \| 0.7569 \| 0.7524 \| \| 0.1063 \| 101.89 \| 612 \| 0.8498 \| 0.7755 \| 0.7807 \| 0.7755 \| 0.7712 \| \| 0.1063 \| 102.89 \| 618 \| 0.8557 \| 0.7708 \| 0.7749 \| 0.7708 \| 0.7655 \| \| 0.097 \| 103.89 \| 624 \| 0.8764 \| 0.7546 \| 0.7634 \| 0.7546 \| 0.7527 \| \| 0.0947 \| 104.89 \| 630 \| 0.8677 \| 0.7616 \| 0.7628 \| 0.7616 \| 0.7572 \| \| 0.0947 \| 105.89 \| 636 \| 0.8909 \| 0.75 \| 0.7614 \| 0.75 \| 0.7469 \| \| 0.1013 \| 106.89 \| 642 \| 0.8283 \| 0.7639 \| 0.7621 \| 0.7639 \| 0.7580 \| \| 0.1013 \| 107.89 \| 648 \| 0.8471 \| 0.7662 \| 0.7864 \| 0.7662 \| 0.7651 \| \| 0.0963 \| 108.89 \| 654 \| 0.8653 \| 0.7593 \| 0.7701 \| 0.7593 \| 0.7558 \| \| 0.0874 \| 109.89 \| 660 \| 0.8479 \| 0.7731 \| 0.7834 \| 0.7731 \| 0.7692 \| \| 0.0874 \| 110.89 \| 666 \| 0.8584 \| 0.7639 \| 0.7719 \| 0.7639 \| 0.7620 \| \| 0.0876 \| 111.89 \| 672 \| 0.8714 \| 0.7616 \| 0.7600 \| 0.7616 \| 0.7550 \| \| 0.0876 \| 112.89 \| 678 \| 0.8509 \| 0.7731 \| 0.7847 \| 0.7731 \| 0.7727 \| \| 0.0974 \| 113.89 \| 684 \| 0.8688 \| 0.7685 \| 0.7741 \| 0.7685 \| 0.7648 \| \| 0.0869 \| 114.89 \| 690 \| 0.8590 \| 0.7847 \| 0.7932 \| 0.7847 \| 0.7794 \| \| 0.0869 \| 115.89 \| 696 \| 0.8687 \| 0.7593 \| 0.7703 \| 0.7593 \| 0.7579 \| \| 0.0877 \| 116.89 \| 702 \| 0.8735 \| 0.7593 \| 0.7698 \| 0.7593 \| 0.7554 \| \| 0.0877 \| 117.89 \| 708 \| 0.8566 \| 0.7546 \| 0.7732 \| 0.7546 \| 0.7518 \| \| 0.0883 \| 118.89 \| 714 \| 0.8681 \| 0.7569 \| 0.7591 \| 0.7569 \| 0.7525 \| \| 0.0762 \| 119.89 \| 720 \| 0.8508 \| 0.7755 \| 0.7715 \| 0.7755 \| 0.7676 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.11.0	[ "aishwarya_rai", "akhilesh_yadav", "ms_dhoni", "mahatma_gandhi", "mamata_banerjee", "mukesh_ambani", "narendra_modi", "rahul_gandhi", "ranveer_singh", "ratan_tata", "sachin_tendulkar", "shahrukh_khan", "akshay_kumar", "sonia_gandhi", "sundar_pichai", "virat_kohli", "yogi_adityanath", "amit_saha", "arijit_singh", "arvind_kejriwal", "baba_ramdev", "gautam_adani", "kishore_kumar", "lata_mangeshkar" ]
Devarshi/Brain_Tumor_Classification_using_swin	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Brain_Tumor_Classification_using_swin This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0123 - Accuracy: 0.9961 - F1: 0.9961 - Recall: 0.9961 - Precision: 0.9961 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Recall \| Precision \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:------:\|:---------:\| \| 0.1234 \| 1.0 \| 180 \| 0.0450 \| 0.9840 \| 0.9840 \| 0.9840 \| 0.9840 \| \| 0.0837 \| 2.0 \| 360 \| 0.0198 \| 0.9926 \| 0.9926 \| 0.9926 \| 0.9926 \| \| 0.0373 \| 3.0 \| 540 \| 0.0123 \| 0.9961 \| 0.9961 \| 0.9961 \| 0.9961 \| ### Framework versions - Transformers 4.23.1 - Pytorch 1.13.0 - Datasets 2.6.1 - Tokenizers 0.13.1	[ "gliomas tumor", "meningiomas tumor", "pituitary tumor" ]
jayanta/google-vit-base-patch16-224-cartoon-emotion-detection	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # google-vit-base-patch16-224-cartoon-emotion-detection This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3706 - Accuracy: 0.8807 - Precision: 0.8769 - Recall: 0.8807 - F1: 0.8783 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00012 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| No log \| 0.97 \| 8 \| 0.9902 \| 0.5596 \| 0.5506 \| 0.5596 \| 0.5360 \| \| 1.242 \| 1.97 \| 16 \| 0.5157 \| 0.8165 \| 0.8195 \| 0.8165 \| 0.8132 \| \| 0.4438 \| 2.97 \| 24 \| 0.3871 \| 0.8440 \| 0.8516 \| 0.8440 \| 0.8446 \| \| 0.1768 \| 3.97 \| 32 \| 0.3531 \| 0.8624 \| 0.8653 \| 0.8624 \| 0.8585 \| \| 0.0661 \| 4.97 \| 40 \| 0.3780 \| 0.8716 \| 0.8693 \| 0.8716 \| 0.8674 \| \| 0.0661 \| 5.97 \| 48 \| 0.3747 \| 0.8624 \| 0.8649 \| 0.8624 \| 0.8632 \| \| 0.0375 \| 6.97 \| 56 \| 0.3760 \| 0.8991 \| 0.8961 \| 0.8991 \| 0.8971 \| \| 0.0362 \| 7.97 \| 64 \| 0.4092 \| 0.8716 \| 0.8684 \| 0.8716 \| 0.8681 \| \| 0.0322 \| 8.97 \| 72 \| 0.3499 \| 0.8899 \| 0.8880 \| 0.8899 \| 0.8888 \| \| 0.029 \| 9.97 \| 80 \| 0.3706 \| 0.8807 \| 0.8769 \| 0.8807 \| 0.8783 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.11.0	[ "angry", "happy", "neutral", "sad" ]
sbrandeis-test-org/autotrain-auto-retrain-190471b-3020886685	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3020886685 - CO2 Emissions (in grams): 0.5727 ## Validation Metrics - Loss: 0.002 - Accuracy: 1.000 - Macro F1: 1.000 - Micro F1: 1.000 - Weighted F1: 1.000 - Macro Precision: 1.000 - Micro Precision: 1.000 - Weighted Precision: 1.000 - Macro Recall: 1.000 - Micro Recall: 1.000 - Weighted Recall: 1.000	[ "adonis", "african giant swallowtail", "american snoot" ]
DunnBC22/vit-base-patch16-224-in21k_Human_Activity_Recognition	# vit-base-patch16-224-in21k_Human_Activity_Recognition This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k). It achieves the following results on the evaluation set: - Loss: 0.7403 - Accuracy: 0.8381 - F1 - Weighted: 0.8388 - Micro: 0.8381 - Macro: 0.8394 - Recall - Weighted: 0.8381 - Micro: 0.8381 - Macro: 0.8390 - Precision - Weighted: 0.8421 - Micro: 0.8381 - Macro: 0.8424 ## Model description This is a multiclass image classification model of humans doing different activities. For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Human%20Activity%20Recognition/ViT-Human%20Action_Recogniton.ipynb ## Intended uses & limitations This model is intended to demonstrate my ability to solve a complex problem using technology. You are welcome to test and experiment with this model, but it is at your own risk/peril. ## Training and evaluation data Dataset Source: https://www.kaggle.com/datasets/meetnagadia/human-action-recognition-har-dataset _Sample Images From Dataset:_ ![Sample Images](https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/raw/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Human%20Activity%20Recognition/Images/Sample%20Images.png) ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Weighted F1 \| Micro F1 \| Macro F1 \| Weighted Recall \| Micro Recall \| Macro Recall \| Weighted Precision \| Micro Precision \| Macro Precision \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-----------:\|:--------:\|:--------:\|:---------------:\|:------------:\|:------------:\|:------------------:\|:---------------:\|:---------------:\| \| 1.0814 \| 1.0 \| 630 \| 0.7368 \| 0.7794 \| 0.7795 \| 0.7794 \| 0.7798 \| 0.7794 \| 0.7794 \| 0.7797 \| 0.7896 \| 0.7794 \| 0.7896 \| \| 0.5149 \| 2.0 \| 1260 \| 0.6439 \| 0.8060 \| 0.8049 \| 0.8060 \| 0.8036 \| 0.8060 \| 0.8060 \| 0.8051 \| 0.8136 \| 0.8060 \| 0.8130 \| \| 0.3023 \| 3.0 \| 1890 \| 0.7026 \| 0.8254 \| 0.8272 \| 0.8254 \| 0.8278 \| 0.8254 \| 0.8254 \| 0.8256 \| 0.8335 \| 0.8254 \| 0.8345 \| \| 0.0507 \| 4.0 \| 2520 \| 0.7414 \| 0.8317 \| 0.8342 \| 0.8317 \| 0.8348 \| 0.8317 \| 0.8317 \| 0.8321 \| 0.8427 \| 0.8317 \| 0.8438 \| \| 0.0128 \| 5.0 \| 3150 \| 0.7403 \| 0.8381 \| 0.8388 \| 0.8381 \| 0.8394 \| 0.8381 \| 0.8381 \| 0.8390 \| 0.8421 \| 0.8381 \| 0.8424 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.8.0 - Tokenizers 0.12.1	[ "calling", "clapping", "running", "sitting", "sleeping", "texting", "using_laptop", "cycling", "dancing", "drinking", "eating", "fighting", "hugging", "laughing", "listening_to_music" ]
alexrods/vit_model	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0125 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1336 \| 3.85 \| 500 \| 0.0125 \| 1.0 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2	[ "angular_leaf_spot", "bean_rust", "healthy" ]
agudelozc/cristian-vit	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # cristian-vit This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0101 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1425 \| 3.85 \| 500 \| 0.0101 \| 1.0 \| ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2	[ "angular_leaf_spot", "bean_rust", "healthy" ]
surajjoshi/Brain_Tumor_Classification_using_swin_transformer	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Brain_Tumor_Classification_using_swin_transformer This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0118 - Accuracy: 0.9949 - F1: 0.9949 - Recall: 0.9949 - Precision: 0.9949 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Recall \| Precision \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:------:\|:---------:\| \| 0.081 \| 1.0 \| 180 \| 0.0557 \| 0.9832 \| 0.9832 \| 0.9832 \| 0.9832 \| \| 0.0816 \| 2.0 \| 360 \| 0.0187 \| 0.9937 \| 0.9937 \| 0.9937 \| 0.9937 \| \| 0.0543 \| 3.0 \| 540 \| 0.0118 \| 0.9949 \| 0.9949 \| 0.9949 \| 0.9949 \| ### Framework versions - Transformers 4.23.1 - Pytorch 1.13.0 - Datasets 2.6.1 - Tokenizers 0.13.1	[ "gliomas tumor", "meningiomas tumor", "pituitary tumor" ]
Prachi1234/corn_leaf_detector	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # corn_leaf_detector This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1631 - Accuracy: 0.9154 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 3.903 \| 0.96 \| 20 \| 3.7614 \| 0.8792 \| \| 3.5402 \| 1.96 \| 40 \| 3.4920 \| 0.9063 \| \| 3.348 \| 2.96 \| 60 \| 3.3117 \| 0.9154 \| \| 3.1824 \| 3.96 \| 80 \| 3.2024 \| 0.9154 \| \| 3.1366 \| 4.96 \| 100 \| 3.1631 \| 0.9154 \| ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2	[ "3", "3", "3", "3", "3", "3", "2", "0", "3", "3", "3", "3", "3", "3", "3", "3", "3", "0", "3", "0", "3", "1", "3", "3", "3", "3", "3", "2", "3", "3", "3", "3", "2", "3", "3", "3", "3", "3", "3", "0", "2", "1", "3", "3", "0", "3", "0", "3", "3", "3", "3", "3", "3", "3", "3", "2", "3", "3", "3", "3", "3", "3", "3", "3", "3", "0", "3", "3", "3", "3", "3", "0", "3", "3", "3", "3", "2", "3", "0", "3", "3", "0", "3", "3", "3", "3", "0", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "1", "3", "3", "3", "3", "0", "0", "3", "3", "3", "3", "3", "0", "3", "2", "3", "3", "3", "3", "0", "3", "3", "3", "3", "0", "2", "0", "3", "3", "2", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "1", "2", "3", "3", "3", "3", "2", "3", "3", "0", "3", "2", "3", "2", "0", "2", "0", "3", "3", "3", "2", "1", "3", "3", "1", "3", "0", "3", "3", "3", "3", "3", "3", "3", "3", 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Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05-16	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05-16 This model is a fine-tuned version of [Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05](https://huggingface.co/Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.8185 - Accuracy: 0.7154 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7923 \| 1.0 \| 224 \| 0.8570 \| 0.7009 \| \| 0.6737 \| 2.0 \| 448 \| 0.8185 \| 0.7154 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1	[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]
Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05-32	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05-32 This model is a fine-tuned version of [Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05](https://huggingface.co/Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.8037 - Accuracy: 0.7201 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.8058 \| 1.0 \| 112 \| 0.8260 \| 0.7056 \| \| 0.6999 \| 2.0 \| 224 \| 0.8037 \| 0.7201 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1	[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]
Celal11/resnet-50-finetuned-FER2013-0.001	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # resnet-50-finetuned-FER2013-0.001 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.9002 - Accuracy: 0.6847 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.4723 \| 1.0 \| 224 \| 1.3382 \| 0.4887 \| \| 1.2236 \| 2.0 \| 448 \| 1.1090 \| 0.5751 \| \| 1.1728 \| 3.0 \| 672 \| 1.0262 \| 0.6158 \| \| 1.1545 \| 4.0 \| 896 \| 0.9717 \| 0.6339 \| \| 1.0776 \| 5.0 \| 1120 \| 0.9885 \| 0.6360 \| \| 1.0183 \| 6.0 \| 1344 \| 0.9475 \| 0.6560 \| \| 0.9856 \| 7.0 \| 1568 \| 0.9114 \| 0.6700 \| \| 0.953 \| 8.0 \| 1792 \| 0.9074 \| 0.6767 \| \| 0.9151 \| 9.0 \| 2016 \| 0.9076 \| 0.6833 \| \| 0.9355 \| 10.0 \| 2240 \| 0.9002 \| 0.6847 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1	[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]
Celal11/resnet-50-finetuned-FER2013-0.003	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # resnet-50-finetuned-FER2013-0.003 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.9036 - Accuracy: 0.6971 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.4393 \| 1.0 \| 224 \| 1.2746 \| 0.5173 \| \| 1.2564 \| 2.0 \| 448 \| 1.1456 \| 0.5542 \| \| 1.218 \| 3.0 \| 672 \| 1.1102 \| 0.5816 \| \| 1.1919 \| 4.0 \| 896 \| 1.0255 \| 0.6151 \| \| 1.1222 \| 5.0 \| 1120 \| 1.0257 \| 0.6167 \| \| 1.0925 \| 6.0 \| 1344 \| 0.9676 \| 0.6317 \| \| 1.0241 \| 7.0 \| 1568 \| 0.9406 \| 0.6510 \| \| 1.0015 \| 8.0 \| 1792 \| 0.9465 \| 0.6532 \| \| 0.987 \| 9.0 \| 2016 \| 0.9002 \| 0.6748 \| \| 0.9768 \| 10.0 \| 2240 \| 0.9086 \| 0.6737 \| \| 0.9408 \| 11.0 \| 2464 \| 0.8975 \| 0.6793 \| \| 0.8907 \| 12.0 \| 2688 \| 0.8966 \| 0.6769 \| \| 0.8051 \| 13.0 \| 2912 \| 0.9142 \| 0.6826 \| \| 0.8169 \| 14.0 \| 3136 \| 0.9082 \| 0.6870 \| \| 0.7729 \| 15.0 \| 3360 \| 0.9036 \| 0.6971 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1	[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]
Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05-finetuned-FER2013-7e-05	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05-finetuned-FER2013-7e-05 This model is a fine-tuned version of [Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05](https://huggingface.co/Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.9121 - Accuracy: 0.7116 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4564 \| 1.0 \| 224 \| 0.9463 \| 0.7014 \| \| 0.6463 \| 2.0 \| 448 \| 0.9121 \| 0.7116 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1	[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]
Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05-4-64	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05-4-64 This model is a fine-tuned version of [Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05](https://huggingface.co/Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.8708 - Accuracy: 0.7201 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5721 \| 1.0 \| 112 \| 0.9867 \| 0.7052 \| \| 0.6816 \| 2.0 \| 224 \| 0.9131 \| 0.7062 \| \| 0.6785 \| 3.0 \| 336 \| 0.8935 \| 0.7091 \| \| 0.6251 \| 4.0 \| 448 \| 0.8708 \| 0.7201 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1	[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]
uisikdag/vit-base-patch16-224-in21k-fog-or-smog-classification	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fogsmog_hfclass This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3700 - Accuracy: 0.91 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6766 \| 1.0 \| 25 \| 0.6299 \| 0.795 \| \| 0.3444 \| 2.0 \| 50 \| 0.3701 \| 0.8625 \| \| 0.2456 \| 3.0 \| 75 \| 0.2988 \| 0.885 \| \| 0.1402 \| 4.0 \| 100 \| 0.3076 \| 0.905 \| \| 0.1275 \| 5.0 \| 125 \| 0.4505 \| 0.8525 \| \| 0.0909 \| 6.0 \| 150 \| 0.3739 \| 0.8825 \| \| 0.0792 \| 7.0 \| 175 \| 0.3642 \| 0.885 \| \| 0.0482 \| 8.0 \| 200 \| 0.3812 \| 0.885 \| \| 0.0451 \| 9.0 \| 225 \| 0.3290 \| 0.9 \| \| 0.0526 \| 10.0 \| 250 \| 0.4004 \| 0.8825 \| \| 0.0575 \| 11.0 \| 275 \| 0.2842 \| 0.925 \| \| 0.0457 \| 12.0 \| 300 \| 0.3952 \| 0.895 \| \| 0.0505 \| 13.0 \| 325 \| 0.4411 \| 0.885 \| \| 0.0324 \| 14.0 \| 350 \| 0.4185 \| 0.8925 \| \| 0.0354 \| 15.0 \| 375 \| 0.3347 \| 0.9025 \| \| 0.0443 \| 16.0 \| 400 \| 0.2949 \| 0.915 \| \| 0.0305 \| 17.0 \| 425 \| 0.3603 \| 0.905 \| \| 0.0234 \| 18.0 \| 450 \| 0.3858 \| 0.8875 \| \| 0.0219 \| 19.0 \| 475 \| 0.3541 \| 0.91 \| \| 0.0284 \| 20.0 \| 500 \| 0.3700 \| 0.91 \| ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "fog", "smog" ]
torresflo/Poke-Model	# Poké Model Poké Model is a Pokémon Classifier created to be used with [Pokédex AI](https://github.com/torresflo/Pokedex-AI). It is a fine-tuned model of google/vit-base-patch16-224 to classify Pokémon of the first generation. More information on how to generate and how to use the model can be found on this [dedicated repository](https://github.com/torresflo/Poke-Model). ## License Distributed under the GNU General Public License v3.0. See [here](https://www.gnu.org/licenses/gpl-3.0.en.html) for more information.	[ "bulbasaur", "ivysaur", "venusaur", "charmander", "charmeleon", "charizard", "squirtle", "wartortle", "blastoise", "caterpie", "metapod", "butterfree", "weedle", "kakuna", "beedrill", "pidgey", "pidgeotto", "pidgeot", "rattata", "raticate", "spearow", "fearow", "ekans", "arbok", "pikachu", "raichu", "sandshrew", "sandslash", "nidoran-f", "nidorina", "nidoqueen", "nidoran-m", "nidorino", "nidoking", "clefairy", "clefable", "vulpix", "ninetales", "jigglypuff", "wigglytuff", "zubat", "golbat", "oddish", "gloom", "vileplume", "paras", "parasect", "venonat", "venomoth", "diglett", "dugtrio", "meowth", "persian", "psyduck", "golduck", "mankey", "primeape", "growlithe", "arcanine", "poliwag", "poliwhirl", "poliwrath", "abra", "kadabra", "alakazam", "machop", "machoke", "machamp", "bellsprout", "weepinbell", "victreebel", "tentacool", "tentacruel", "geodude", "graveler", "golem", "ponyta", "rapidash", "slowpoke", "slowbro", "magnemite", "magneton", "farfetchd", "doduo", "dodrio", "seel", "dewgong", "grimer", "muk", "shellder", "cloyster", "gastly", "haunter", "gengar", "onix", "drowzee", "hypno", "krabby", "kingler", "voltorb", "electrode", "exeggcute", "exeggutor", "cubone", "marowak", "hitmonlee", "hitmonchan", "lickitung", "koffing", "weezing", "rhyhorn", "rhydon", "chansey", "tangela", "kangaskhan", "horsea", "seadra", "goldeen", "seaking", "staryu", "starmie", "mr-mime", "scyther", "jynx", "electabuzz", "magmar", "pinsir", "tauros", "magikarp", "gyarados", "lapras", "ditto", "eevee", "vaporeon", "jolteon", "flareon", "porygon", "omanyte", "omastar", "kabuto", "kabutops", "aerodactyl", "snorlax", "articuno", "zapdos", "moltres", "dratini", "dragonair", "dragonite", "mewtwo", "mew" ]
swordsman1/swin-tiny-patch4-window7-224-finetuned-eurosat	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0583 - Accuracy: 0.9822 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2655 \| 1.0 \| 190 \| 0.1039 \| 0.9707 \| \| 0.1519 \| 2.0 \| 380 \| 0.0866 \| 0.9715 \| \| 0.1402 \| 3.0 \| 570 \| 0.0583 \| 0.9822 \| ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "annualcrop", "forest", "herbaceousvegetation", "highway", "industrial", "pasture", "permanentcrop", "residential", "river", "sealake" ]
Lloviant/autotrain-ex-and-pt-3122688386	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3122688386 - CO2 Emissions (in grams): 0.6203 ## Validation Metrics - Loss: 1.338 - Accuracy: 0.571 - Macro F1: 0.389 - Micro F1: 0.571 - Weighted F1: 0.429 - Macro Precision: 0.333 - Micro Precision: 0.571 - Weighted Precision: 0.357 - Macro Recall: 0.500 - Micro Recall: 0.571 - Weighted Recall: 0.571	[ "ex and pt", "ex and pt logo", "ex and pt mutant", "ex and pt mutants", "ex and pt tcg", "vagitron" ]
Lloviant/autotrain-ex-and-pt-3122688387	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3122688387 - CO2 Emissions (in grams): 0.5722 ## Validation Metrics - Loss: 1.749 - Accuracy: 0.571 - Macro F1: 0.444 - Micro F1: 0.571 - Weighted F1: 0.476 - Macro Precision: 0.417 - Micro Precision: 0.571 - Weighted Precision: 0.429 - Macro Recall: 0.500 - Micro Recall: 0.571 - Weighted Recall: 0.571	[ "ex and pt", "ex and pt logo", "ex and pt mutant", "ex and pt mutants", "ex and pt tcg", "vagitron" ]
Lloviant/autotrain-ex-and-pt-3122688388	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3122688388 - CO2 Emissions (in grams): 0.2158 ## Validation Metrics - Loss: 1.818 - Accuracy: 0.000 - Macro F1: 0.000 - Micro F1: 0.000 - Weighted F1: 0.000 - Macro Precision: 0.000 - Micro Precision: 0.000 - Weighted Precision: 0.000 - Macro Recall: 0.000 - Micro Recall: 0.000 - Weighted Recall: 0.000	[ "ex and pt", "ex and pt logo", "ex and pt mutant", "ex and pt mutants", "ex and pt tcg", "vagitron" ]
Lloviant/autotrain-ex-and-pt-3122688389	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3122688389 - CO2 Emissions (in grams): 0.7206 ## Validation Metrics - Loss: 1.599 - Accuracy: 0.286 - Macro F1: 0.250 - Micro F1: 0.286 - Weighted F1: 0.286 - Macro Precision: 0.250 - Micro Precision: 0.286 - Weighted Precision: 0.286 - Macro Recall: 0.250 - Micro Recall: 0.286 - Weighted Recall: 0.286	[ "ex and pt", "ex and pt logo", "ex and pt mutant", "ex and pt mutants", "ex and pt tcg", "vagitron" ]
Lloviant/autotrain-ex-and-pt-3122688390	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3122688390 - CO2 Emissions (in grams): 0.4229 ## Validation Metrics - Loss: 1.919 - Accuracy: 0.286 - Macro F1: 0.214 - Micro F1: 0.286 - Weighted F1: 0.184 - Macro Precision: 0.194 - Micro Precision: 0.286 - Weighted Precision: 0.167 - Macro Recall: 0.333 - Micro Recall: 0.286 - Weighted Recall: 0.286	[ "ex and pt", "ex and pt logo", "ex and pt mutant", "ex and pt mutants", "ex and pt tcg", "vagitron" ]
tadeous/vit-model-beimer	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-model-beimer This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0637 - Accuracy: 0.9850 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1394 \| 3.85 \| 500 \| 0.0637 \| 0.9850 \| ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "angular_leaf_spot", "bean_rust", "healthy" ]
SaintGermain/is-this-furry	This detects furry images, mostly profile pictures, although it may be able detect any sort of furry picture (I haven't tried it, though). # Dataset Info This was trained on scraped pfp images from Mastodon, with some non-pfp images thrown in for "balancing" (i.e ensuring pokemon, kemonomimi (catgirls/foxgirls/etc), and normal animals weren't classified as 'furry') Furry images: 551 Non-furry images: 641 # Disclaimer Please do not ruin this by using this to harass anyone. This is not intended to be used for targeted harrassement, and I will explicitly condemn any use that attempts to do so. If you're wondering why I made this public in the first place? I believe in freedom of information - this image classification model has various perfectly valid uses, and it's kinda useless to keep it private. # Statistics ## Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 2890884434 - CO2 Emissions (in grams): 2.8752 ## Validation Metrics - Loss: 0.175 - Accuracy: 0.933 - Precision: 0.938 - Recall: 0.938 - AUC: 0.975 - F1: 0.938	[ "bad", "good" ]
lixiqi/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05-finetuned-SFEW-7e-05	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05-finetuned-SFEW-7e-05 This model is a fine-tuned version of [Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05](https://huggingface.co/Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 1.5629 - Accuracy: 0.4960 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.1509 \| 0.97 \| 14 \| 1.6920 \| 0.3725 \| \| 1.6764 \| 1.97 \| 28 \| 1.5035 \| 0.4694 \| \| 1.2723 \| 2.97 \| 42 \| 1.5061 \| 0.4694 \| \| 1.1746 \| 3.97 \| 56 \| 1.5421 \| 0.4729 \| \| 0.9954 \| 4.97 \| 70 \| 1.5657 \| 0.4787 \| \| 1.0029 \| 5.97 \| 84 \| 1.5867 \| 0.4844 \| \| 0.9139 \| 6.97 \| 98 \| 1.5943 \| 0.4879 \| \| 0.8335 \| 7.97 \| 112 \| 1.6003 \| 0.4890 \| \| 0.8382 \| 8.97 \| 126 \| 1.5629 \| 0.4960 \| \| 0.7169 \| 9.97 \| 140 \| 1.5772 \| 0.4856 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1	[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]
learnsolana/autotrain-chest-xray-demo-3129688461	# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3129688461 - CO2 Emissions (in grams): 8.0323 ## Validation Metrics - Loss: 0.445 - Accuracy: 0.774 - Precision: 0.738 - Recall: 0.990 - AUC: 0.920 - F1: 0.846	[ "normal", "pneumonia" ]
Kaludi/Food-Classification	# Food Classification This is a Food Image Classifier model that has been trained by [Kaludi](https://huggingface.co/Kaludi) to recognize 7 different types of popular foods, including apple pie, falafel, french toast, ice cream, ramen, sushi, and tiramisu. It can accurately classify an image of food into one of these categories by analyzing its visual features. This model can be used by food bloggers, restaurants, and recipe websites to quickly categorize and sort their food images, making it easier to manage their content and provide a better user experience. ### Gradio Tis model supports a [Gradio](https://github.com/gradio-app/gradio) Web UI to run the data-food-classification model: [![Open In HF Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/Kaludi/Food-Classification_App) ## Validation Metrics - Loss: 0.094 - Accuracy: 0.977 - Macro F1: 0.977 - Micro F1: 0.977 - Weighted F1: 0.977 - Macro Precision: 0.978 - Micro Precision: 0.977 - Weighted Precision: 0.978 - Macro Recall: 0.977 - Micro Recall: 0.977 - Weighted Recall: 0.977	[ "apple_pie", "falafel", "french_toast", "ice_cream", "ramen", "sushi", "tiramisu" ]
Gokulapriyan/swin-tiny-patch4-window7-224-finetuned-eurosat	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0684 - Accuracy: 0.9742 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4378 \| 1.0 \| 527 \| 0.3668 \| 0.8507 \| \| 0.3133 \| 2.0 \| 1054 \| 0.1586 \| 0.9430 \| \| 0.2065 \| 3.0 \| 1581 \| 0.1049 \| 0.9607 \| \| 0.22 \| 4.0 \| 2108 \| 0.0838 \| 0.9701 \| \| 0.173 \| 5.0 \| 2635 \| 0.0684 \| 0.9742 \| ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "fnh_frames", "hcc_frames", "hhe_frames", "healthy_frames" ]
StatsGary/VIT-food101-image-classifier	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # VIT-food101-image-classifier This model was trained from scratch on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 0.5661 - Accuracy: 0.933 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.1716 \| 0.99 \| 62 \| 1.2149 \| 0.896 \| \| 0.7758 \| 1.99 \| 124 \| 0.8727 \| 0.906 \| \| 0.6269 \| 2.99 \| 186 \| 0.6833 \| 0.928 \| \| 0.5495 \| 3.99 \| 248 \| 0.6041 \| 0.931 \| \| 0.4973 \| 4.99 \| 310 \| 0.5661 \| 0.933 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2	[ "apple_pie", "baby_back_ribs", "baklava", "beef_carpaccio", "beef_tartare", "beet_salad", "beignets", "bibimbap", "bread_pudding", "breakfast_burrito", "bruschetta", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare", "waffles" ]
Thabet/swin-tiny-patch4-window7-224-finetuned-aiornot-baseline	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-aiornot-baseline This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2231 - Accuracy: 0.9119 - F1: 0.9086 - Log Loss: 3.0422 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Log Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\| \| 0.2538 \| 0.98 \| 32 \| 0.2327 \| 0.9055 \| 0.9018 \| 3.2647 \| \| 0.1735 \| 1.98 \| 64 \| 0.2029 \| 0.9151 \| 0.9122 \| 2.9309 \| \| 0.1562 \| 2.98 \| 96 \| 0.2231 \| 0.9119 \| 0.9086 \| 3.0422 \| ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1	[ "no_ai", "ai" ]
platzi/platzi-vit-model-orlando-murcia	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # platzi-vit-model-orlando-murcia This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0532 - Accuracy: 0.9850 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0776 \| 3.85 \| 500 \| 0.0532 \| 0.9850 \| ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "angular_leaf_spot", "bean_rust", "healthy" ]
Celal11/resnet-50-finetuned-FER2013CKPlus-0.003	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # resnet-50-finetuned-FER2013CKPlus-0.003 This model is a fine-tuned version of [Celal11/resnet-50-finetuned-FER2013-0.003](https://huggingface.co/Celal11/resnet-50-finetuned-FER2013-0.003) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.0073 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.8084 \| 0.97 \| 27 \| 0.2004 \| 0.9289 \| \| 0.362 \| 1.97 \| 54 \| 0.0828 \| 0.9848 \| \| 0.2972 \| 2.97 \| 81 \| 0.0185 \| 0.9949 \| \| 0.1917 \| 3.97 \| 108 \| 0.0132 \| 1.0 \| \| 0.1572 \| 4.97 \| 135 \| 0.0073 \| 1.0 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1	[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]
Celal11/resnet-50-finetuned-FER2013-0.003-CKPlus	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # resnet-50-finetuned-FER2013-0.003-CKPlus This model is a fine-tuned version of [Celal11/resnet-50-finetuned-FER2013-0.003](https://huggingface.co/Celal11/resnet-50-finetuned-FER2013-0.003) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.0614 - Accuracy: 0.9848 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6689 \| 0.97 \| 27 \| 0.1123 \| 0.9797 \| \| 0.2929 \| 1.97 \| 54 \| 0.0614 \| 0.9848 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1	[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]
ashutoshmondal/pneumo_v3	# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3177689678 - CO2 Emissions (in grams): 1.9594 ## Validation Metrics - Loss: 0.017 - Accuracy: 1.000 - Precision: 1.000 - Recall: 1.000 - AUC: 1.000 - F1: 1.000	[ "normal", "pneumonia" ]
ashutoshmondal/autotrain-pneumo-v3-3180589690	# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3180589690 - CO2 Emissions (in grams): 3.4021 ## Validation Metrics - Loss: 0.131 - Accuracy: 0.964 - Precision: 0.964 - Recall: 0.964 - AUC: 0.994 - F1: 0.964	[ "normal", "pneumonia" ]
Kanr1u/rose_charlotte	# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3206689984 - CO2 Emissions (in grams): 2.1410 ## Validation Metrics - Loss: 0.303 - Accuracy: 0.846 - Precision: 0.846 - Recall: 0.846 - AUC: 0.929 - F1: 0.846	[ "emma roberts", "emma watson" ]
anaghasavit/swin-tiny-patch4-window7-224-finetuned-eurosat	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0071 - Accuracy: 0.9980 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0209 \| 1.0 \| 426 \| 0.0071 \| 0.9980 \| ### Framework versions - Transformers 4.27.4 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.2	[ "dot", "horizontal_bar", "line", "scatter", "vertical_bar" ]
Kaludi/csgo-weapon-classification	# CSGO Weapon Classification This is a CSGO Weapon Classifier Model that has been trained by [Kaludi](https://huggingface.co/Kaludi) to recognize 11 different types of Counter-Strike: Global Offensive (CSGO) Weapons, which include AK-47,AWP,Famas,Galil-AR,Glock,M4A1,M4A4,P-90,SG-553,UMP,USP. The model is capable of accurately classifying the weapon name present in an image. With its deep understanding of the characteristics of each weapon in the game, the model is a valuable tool for players and fans of CSGO. ### Gradio Tis model supports a [Gradio](https://github.com/gradio-app/gradio) Web UI to run the csgo-weapon-classification model: [![Open In HF Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/Kaludi/CSGO-Weapon-Classification_App) ## Validation Metrics - Loss: 0.282 - Accuracy: 0.945 - Macro F1: 0.946 - Micro F1: 0.945 - Weighted F1: 0.946 - Macro Precision: 0.948 - Micro Precision: 0.945 - Weighted Precision: 0.948 - Macro Recall: 0.945 - Micro Recall: 0.945 - Weighted Recall: 0.945	[ "ak-47", "awp", "usp", "famas", "galil-ar", "glock", "m4a1", "m4a4", "p-90", "sg-553", "ump" ]
Kaludi/food-category-classification	# Food Category Classification This is a Food Category Image Classifier model that has been trained by [Kaludi](https://huggingface.co/Kaludi) to recognize 11 different categories of foods, including Bread, Dairy Product, Dessert, Egg, Fried Food, Meat, Noodles-Pasta, Rice, Seafood, Soup, and Vegetable-Fruit. It can accurately classify an image of food into one of these categories by analyzing its visual features. This model can be used by food bloggers, restaurants, and recipe websites to quickly categorize and sort their food images, making it easier to manage their content and provide a better user experience. ### Gradio Tis model supports a [Gradio](https://github.com/gradio-app/gradio) Web UI to run the data-food-classification model: [![Open In HF Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/Kaludi/Food-Category-Classification_App) ## Validation Metrics - Loss: 0.079 - Accuracy: 0.978 - Macro F1: 0.978 - Micro F1: 0.978 - Weighted F1: 0.978 - Macro Precision: 0.979 - Micro Precision: 0.978 - Weighted Precision: 0.979 - Macro Recall: 0.978 - Micro Recall: 0.978 - Weighted Recall: 0.978	[ "bread", "dairy product", "vegetable-fruit", "dessert", "egg", "fried food", "meat", "noodles-pasta", "rice", "seafood", "soup" ]
NehaBardeDUKE/autotrain-ai-generated-image-classification-3250490787	# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3250490787 - CO2 Emissions (in grams): 0.0106 ## Validation Metrics - Loss: 0.217 - Accuracy: 0.941 - Precision: 0.929 - Recall: 1.000 - AUC: 1.000 - F1: 0.963	[ "artificial", "human" ]
Ailyth/2_Labels	# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3268491180 - CO2 Emissions (in grams): 2.0388 ## Validation Metrics - Loss: 0.044 - Accuracy: 0.970 - Precision: 0.966 - Recall: 0.982 - AUC: 0.998 - F1: 0.974	[ "cuisine", "zhazu" ]
adielsa/swin-tiny-patch4-window7-224-finetuned-eurosat	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1627 - Accuracy: 0.9464 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2486 \| 0.98 \| 36 \| 0.2120 \| 0.9100 \| \| 0.1844 \| 1.98 \| 72 \| 0.3417 \| 0.8563 \| \| 0.1646 \| 2.98 \| 108 \| 0.1627 \| 0.9464 \| ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "normal", "pneumonia" ]
adielsa/vit-base-patch16-224-finetuned-chest	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-finetuned-chest This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0318 - Accuracy: 0.9900 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0947 \| 0.98 \| 36 \| 0.0785 \| 0.9732 \| \| 0.048 \| 1.98 \| 72 \| 0.0678 \| 0.9732 \| \| 0.0352 \| 2.98 \| 108 \| 0.0329 \| 0.9887 \| ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "normal", "pneumonia" ]
hamdan07/UltraSound-Lung	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3310291874 - CO2 Emissions (in grams): 1.3971 ## Validation Metrics - Loss: 0.001 - Accuracy: 1.000 - Macro F1: 1.000 - Micro F1: 1.000 - Weighted F1: 1.000 - Macro Precision: 1.000 - Micro Precision: 1.000 - Weighted Precision: 1.000 - Macro Recall: 1.000 - Micro Recall: 1.000 - Weighted Recall: 1.000	[ "covid", "pneumonia", "regular" ]
sayakpaul/vit-base-patch16-224-in21k-finetuned-lora-food101	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-finetuned-lora-food101 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 0.1448 - Accuracy: 0.96 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 9 \| 0.5069 \| 0.896 \| \| 2.1627 \| 2.0 \| 18 \| 0.1891 \| 0.946 \| \| 0.3451 \| 3.0 \| 27 \| 0.1448 \| 0.96 \| \| 0.2116 \| 4.0 \| 36 \| 0.1509 \| 0.958 \| \| 0.1711 \| 5.0 \| 45 \| 0.1498 \| 0.958 \| ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "apple_pie", "baby_back_ribs", "baklava", "beef_carpaccio", "beef_tartare", "beet_salad", "beignets", "bibimbap", "bread_pudding", "breakfast_burrito", "bruschetta", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare", "waffles" ]
anaghasavit/beit-base-patch16-224-pt22k-ft22k-finetunedt	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # beit-base-patch16-224-pt22k-ft22k-finetunedt This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0147 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4714 \| 1.0 \| 25 \| 0.0147 \| 1.0 \| \| 0.0089 \| 2.0 \| 50 \| 0.0008 \| 1.0 \| \| 0.0101 \| 3.0 \| 75 \| 0.0003 \| 1.0 \| \| 0.0021 \| 4.0 \| 100 \| 0.0002 \| 1.0 \| \| 0.0028 \| 5.0 \| 125 \| 0.0001 \| 1.0 \| \| 0.0016 \| 6.0 \| 150 \| 0.0001 \| 1.0 \| \| 0.0044 \| 7.0 \| 175 \| 0.0001 \| 1.0 \| \| 0.0007 \| 8.0 \| 200 \| 0.0001 \| 1.0 \| \| 0.0013 \| 9.0 \| 225 \| 0.0001 \| 1.0 \| \| 0.0004 \| 10.0 \| 250 \| 0.0001 \| 1.0 \| ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "apple", "banana", "onion", "orange", "package", "potato", "watermelon" ]
davanstrien/autotrain-encyclopaedia-illustrations-blog-post-3327992158	# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3327992158 - CO2 Emissions (in grams): 13.4530 ## Validation Metrics - Loss: 0.025 - Accuracy: 0.992 - Precision: 0.998 - Recall: 0.994 - AUC: 0.998 - F1: 0.996	[ "illustrated", "not-illustrated" ]
davanstrien/autotrain-encyclopaedia-illustrations-blog-post-3327992159	# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3327992159 - CO2 Emissions (in grams): 4.6084 ## Validation Metrics - Loss: 0.040 - Accuracy: 0.992 - Precision: 0.994 - Recall: 0.998 - AUC: 0.993 - F1: 0.996	[ "illustrated", "not-illustrated" ]
muhammaddjunas/cvt-13-finetuned-waste	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # cvt-13-finetuned-waste This model is a fine-tuned version of [microsoft/cvt-13](https://huggingface.co/microsoft/cvt-13) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1715 \| 0.99 \| 117 \| 0.0000 \| 1.0 \| \| 0.1194 \| 1.99 \| 234 \| 0.0000 \| 1.0 \| \| 0.1496 \| 2.99 \| 351 \| 0.0000 \| 1.0 \| ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "tench, tinca tinca", "goldfish, carassius auratus", "great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias", "tiger shark, galeocerdo cuvieri", "hammerhead, hammerhead shark", "electric ray, crampfish, numbfish, torpedo", "stingray", "cock", "hen", "ostrich, struthio camelus", "brambling, fringilla montifringilla", "goldfinch, carduelis carduelis", "house finch, linnet, carpodacus mexicanus", "junco, snowbird", "indigo bunting, indigo finch, indigo bird, passerina cyanea", "robin, american robin, turdus migratorius", "bulbul", "jay", "magpie", "chickadee", "water ouzel, dipper", "kite", "bald eagle, american eagle, haliaeetus leucocephalus", "vulture", "great grey owl, great gray owl, strix nebulosa", "european fire salamander, salamandra salamandra", "common newt, triturus vulgaris", "eft", "spotted salamander, ambystoma maculatum", "axolotl, mud puppy, ambystoma mexicanum", "bullfrog, rana catesbeiana", "tree frog, tree-frog", "tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui", "loggerhead, loggerhead turtle, caretta caretta", "leatherback turtle, leatherback, leathery turtle, dermochelys coriacea", "mud turtle", "terrapin", "box turtle, box tortoise", "banded gecko", "common iguana, iguana, iguana iguana", "american chameleon, anole, anolis carolinensis", "whiptail, whiptail lizard", "agama", "frilled lizard, chlamydosaurus kingi", "alligator lizard", "gila monster, heloderma suspectum", "green lizard, lacerta viridis", "african chameleon, chamaeleo chamaeleon", "komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis", "african crocodile, nile crocodile, crocodylus niloticus", "american alligator, alligator mississipiensis", "triceratops", "thunder snake, worm snake, carphophis amoenus", "ringneck snake, ring-necked snake, ring snake", "hognose snake, puff adder, sand viper", "green snake, grass snake", "king snake, kingsnake", "garter snake, grass snake", "water snake", "vine snake", "night snake, hypsiglena torquata", "boa constrictor, constrictor constrictor", "rock python, rock snake, python sebae", "indian cobra, naja naja", "green mamba", "sea snake", "horned viper, cerastes, sand viper, horned asp, cerastes cornutus", "diamondback, diamondback rattlesnake, crotalus adamanteus", "sidewinder, horned rattlesnake, crotalus cerastes", "trilobite", "harvestman, daddy longlegs, phalangium opilio", "scorpion", "black and gold garden spider, argiope aurantia", "barn spider, araneus cavaticus", "garden spider, aranea diademata", "black widow, latrodectus mactans", "tarantula", "wolf spider, hunting spider", "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse, partridge, bonasa umbellus", "prairie chicken, prairie grouse, prairie fowl", "peacock", "quail", "partridge", "african grey, african gray, psittacus erithacus", "macaw", "sulphur-crested cockatoo, kakatoe galerita, cacatua galerita", "lorikeet", "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "drake", "red-breasted merganser, mergus serrator", "goose", "black swan, cygnus atratus", "tusker", "echidna, spiny anteater, anteater", "platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus", "wallaby, brush kangaroo", "koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus", "wombat", "jellyfish", "sea anemone, anemone", "brain coral", "flatworm, platyhelminth", "nematode, nematode worm, roundworm", "conch", "snail", "slug", "sea slug, nudibranch", "chiton, coat-of-mail shell, sea cradle, polyplacophore", "chambered nautilus, pearly nautilus, nautilus", "dungeness crab, cancer magister", "rock crab, cancer irroratus", "fiddler crab", "king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica", "american lobster, northern lobster, maine lobster, homarus americanus", "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "crayfish, crawfish, crawdad, crawdaddy", "hermit crab", "isopod", "white stork, ciconia ciconia", "black stork, ciconia nigra", "spoonbill", "flamingo", "little blue heron, egretta caerulea", "american egret, great white heron, egretta albus", "bittern", "crane", "limpkin, aramus pictus", "european gallinule, porphyrio porphyrio", "american coot, marsh hen, mud hen, water hen, fulica americana", "bustard", "ruddy turnstone, arenaria interpres", "red-backed sandpiper, dunlin, erolia alpina", "redshank, tringa totanus", "dowitcher", "oystercatcher, oyster catcher", "pelican", "king penguin, aptenodytes patagonica", "albatross, mollymawk", "grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus", "killer whale, killer, orca, grampus, sea wolf, orcinus orca", "dugong, dugong dugon", "sea lion", "chihuahua", "japanese spaniel", "maltese dog, maltese terrier, maltese", "pekinese, pekingese, peke", "shih-tzu", "blenheim spaniel", "papillon", "toy terrier", "rhodesian ridgeback", "afghan hound, afghan", "basset, basset hound", "beagle", "bloodhound, sleuthhound", "bluetick", "black-and-tan coonhound", "walker hound, walker foxhound", "english foxhound", "redbone", "borzoi, russian wolfhound", "irish wolfhound", "italian greyhound", "whippet", "ibizan hound, ibizan podenco", "norwegian elkhound, elkhound", "otterhound, otter hound", "saluki, gazelle hound", "scottish deerhound, deerhound", "weimaraner", "staffordshire bullterrier, staffordshire bull terrier", "american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier", "bedlington terrier", "border terrier", "kerry blue terrier", "irish terrier", "norfolk terrier", "norwich terrier", "yorkshire terrier", "wire-haired fox terrier", "lakeland terrier", "sealyham terrier, sealyham", "airedale, airedale terrier", "cairn, cairn terrier", "australian terrier", "dandie dinmont, dandie dinmont terrier", "boston bull, boston terrier", "miniature schnauzer", "giant schnauzer", "standard schnauzer", "scotch terrier, scottish terrier, scottie", "tibetan terrier, chrysanthemum dog", "silky terrier, sydney silky", "soft-coated wheaten terrier", "west highland white terrier", "lhasa, lhasa apso", "flat-coated retriever", "curly-coated retriever", "golden retriever", "labrador retriever", "chesapeake bay retriever", "german short-haired pointer", "vizsla, hungarian pointer", "english setter", "irish setter, red setter", "gordon setter", "brittany spaniel", "clumber, clumber spaniel", "english springer, english springer spaniel", "welsh springer spaniel", "cocker spaniel, english cocker spaniel, cocker", "sussex spaniel", "irish water spaniel", "kuvasz", "schipperke", "groenendael", "malinois", "briard", "kelpie", "komondor", "old english sheepdog, bobtail", "shetland sheepdog, shetland sheep dog, shetland", "collie", "border collie", "bouvier des flandres, bouviers des flandres", "rottweiler", "german shepherd, german shepherd dog, german police dog, alsatian", "doberman, doberman pinscher", "miniature pinscher", "greater swiss mountain dog", "bernese mountain dog", "appenzeller", "entlebucher", "boxer", "bull mastiff", "tibetan mastiff", "french bulldog", "great dane", "saint bernard, st bernard", "eskimo dog, husky", "malamute, malemute, alaskan malamute", "siberian husky", "dalmatian, coach dog, carriage dog", "affenpinscher, monkey pinscher, monkey dog", "basenji", "pug, pug-dog", "leonberg", "newfoundland, newfoundland dog", "great pyrenees", "samoyed, samoyede", "pomeranian", "chow, chow chow", "keeshond", "brabancon griffon", "pembroke, pembroke welsh corgi", "cardigan, cardigan welsh corgi", "toy poodle", "miniature poodle", "standard poodle", "mexican hairless", "timber wolf, grey wolf, gray wolf, canis lupus", "white wolf, arctic wolf, canis lupus tundrarum", "red wolf, maned wolf, canis rufus, canis niger", "coyote, prairie wolf, brush wolf, canis latrans", "dingo, warrigal, warragal, canis dingo", "dhole, cuon alpinus", "african hunting dog, hyena dog, cape hunting dog, lycaon pictus", "hyena, hyaena", "red fox, vulpes vulpes", "kit fox, vulpes macrotis", "arctic fox, white fox, alopex lagopus", "grey fox, gray fox, urocyon cinereoargenteus", "tabby, tabby cat", "tiger cat", "persian cat", "siamese cat, siamese", "egyptian cat", "cougar, puma, catamount, mountain lion, painter, panther, felis concolor", "lynx, catamount", "leopard, panthera pardus", "snow leopard, ounce, panthera uncia", "jaguar, panther, panthera onca, felis onca", "lion, king of beasts, panthera leo", "tiger, panthera tigris", "cheetah, chetah, acinonyx jubatus", "brown bear, bruin, ursus arctos", "american black bear, black bear, ursus americanus, euarctos americanus", "ice bear, polar bear, ursus maritimus, thalarctos maritimus", "sloth bear, melursus ursinus, ursus ursinus", "mongoose", "meerkat, mierkat", "tiger beetle", "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "ground beetle, carabid beetle", "long-horned beetle, longicorn, longicorn beetle", "leaf beetle, chrysomelid", "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant, emmet, pismire", "grasshopper, hopper", "cricket", "walking stick, walkingstick, stick insect", "cockroach, roach", "mantis, mantid", "cicada, cicala", "leafhopper", "lacewing, lacewing fly", "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "damselfly", "admiral", "ringlet, ringlet butterfly", "monarch, monarch butterfly, milkweed butterfly, danaus plexippus", "cabbage butterfly", "sulphur butterfly, sulfur butterfly", "lycaenid, lycaenid butterfly", "starfish, sea star", "sea urchin", "sea cucumber, holothurian", "wood rabbit, cottontail, cottontail rabbit", "hare", "angora, angora rabbit", "hamster", "porcupine, hedgehog", "fox squirrel, eastern fox squirrel, sciurus niger", "marmot", "beaver", "guinea pig, cavia cobaya", "sorrel", "zebra", "hog, pig, grunter, squealer, sus scrofa", "wild boar, boar, sus scrofa", "warthog", "hippopotamus, hippo, river horse, hippopotamus amphibius", "ox", "water buffalo, water ox, asiatic buffalo, bubalus bubalis", "bison", "ram, tup", "bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis", "ibex, capra ibex", "hartebeest", "impala, aepyceros melampus", "gazelle", "arabian camel, dromedary, camelus dromedarius", "llama", "weasel", "mink", "polecat, fitch, foulmart, foumart, mustela putorius", "black-footed ferret, ferret, mustela nigripes", "otter", "skunk, polecat, wood pussy", "badger", "armadillo", "three-toed sloth, ai, bradypus tridactylus", "orangutan, orang, orangutang, pongo pygmaeus", "gorilla, gorilla gorilla", "chimpanzee, chimp, pan troglodytes", "gibbon, hylobates lar", "siamang, hylobates syndactylus, symphalangus syndactylus", "guenon, guenon monkey", "patas, hussar monkey, erythrocebus patas", "baboon", "macaque", "langur", "colobus, colobus monkey", "proboscis monkey, nasalis larvatus", "marmoset", "capuchin, ringtail, cebus capucinus", "howler monkey, howler", "titi, titi monkey", "spider monkey, ateles geoffroyi", "squirrel monkey, saimiri sciureus", "madagascar cat, ring-tailed lemur, lemur catta", "indri, indris, indri indri, indri brevicaudatus", "indian elephant, elephas maximus", "african elephant, loxodonta africana", "lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens", "giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca", "barracouta, snoek", "eel", "coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch", "rock beauty, holocanthus tricolor", "anemone fish", "sturgeon", "gar, garfish, garpike, billfish, lepisosteus osseus", "lionfish", "puffer, pufferfish, blowfish, globefish", "abacus", "abaya", "academic gown, academic robe, judge's robe", "accordion, piano accordion, squeeze box", "acoustic guitar", "aircraft carrier, carrier, flattop, attack aircraft carrier", "airliner", "airship, dirigible", "altar", "ambulance", "amphibian, amphibious vehicle", "analog clock", "apiary, bee house", "apron", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "assault rifle, assault gun", "backpack, back pack, knapsack, packsack, rucksack, haversack", "bakery, bakeshop, bakehouse", "balance beam, beam", "balloon", "ballpoint, ballpoint pen, ballpen, biro", "band aid", "banjo", "bannister, banister, balustrade, balusters, handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel, cask", "barrow, garden cart, lawn cart, wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "bathing cap, swimming cap", "bath towel", "bathtub, bathing tub, bath, tub", "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "beacon, lighthouse, beacon light, pharos", "beaker", "bearskin, busby, shako", "beer bottle", "beer glass", "bell cote, bell cot", "bib", "bicycle-built-for-two, tandem bicycle, tandem", "bikini, two-piece", "binder, ring-binder", "binoculars, field glasses, opera glasses", "birdhouse", "boathouse", "bobsled, bobsleigh, bob", "bolo tie, bolo, bola tie, bola", "bonnet, poke bonnet", "bookcase", "bookshop, bookstore, bookstall", "bottlecap", "bow", "bow tie, bow-tie, bowtie", "brass, memorial tablet, plaque", "brassiere, bra, bandeau", "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "breastplate, aegis, egis", "broom", "bucket, pail", "buckle", "bulletproof vest", "bullet train, bullet", "butcher shop, meat market", "cab, hack, taxi, taxicab", "caldron, cauldron", "candle, taper, wax light", "cannon", "canoe", "can opener, tin opener", "cardigan", "car mirror", "carousel, carrousel, merry-go-round, roundabout, whirligig", "carpenter's kit, tool kit", "carton", "car wheel", "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm", "cassette", "cassette player", "castle", "catamaran", "cd player", "cello, violoncello", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "chain", "chainlink fence", "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "chain saw, chainsaw", "chest", "chiffonier, commode", "chime, bell, gong", "china cabinet, china closet", "christmas stocking", "church, church building", "cinema, movie theater, movie theatre, movie house, picture palace", "cleaver, meat cleaver, chopper", "cliff dwelling", "cloak", "clog, geta, patten, sabot", "cocktail shaker", "coffee mug", "coffeepot", "coil, spiral, volute, whorl, helix", "combination lock", "computer keyboard, keypad", "confectionery, confectionary, candy store", "container ship, containership, container vessel", "convertible", "corkscrew, bottle screw", "cornet, horn, trumpet, trump", "cowboy boot", "cowboy hat, ten-gallon hat", "cradle", "crane", "crash helmet", "crate", "crib, cot", "crock pot", "croquet ball", "crutch", "cuirass", "dam, dike, dyke", "desk", "desktop computer", "dial telephone, dial phone", "diaper, nappy, napkin", "digital clock", "digital watch", "dining table, board", "dishrag, dishcloth", "dishwasher, dish washer, dishwashing machine", "disk brake, disc brake", "dock, dockage, docking facility", "dogsled, dog sled, dog sleigh", "dome", "doormat, welcome mat", "drilling platform, offshore rig", "drum, membranophone, tympan", "drumstick", "dumbbell", "dutch oven", "electric fan, blower", "electric guitar", "electric locomotive", "entertainment center", "envelope", "espresso maker", "face powder", "feather boa, boa", "file, file cabinet, filing cabinet", "fireboat", "fire engine, fire truck", "fire screen, fireguard", "flagpole, flagstaff", "flute, transverse flute", "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster", "freight car", "french horn, horn", "frying pan, frypan, skillet", "fur coat", "garbage truck, dustcart", "gasmask, respirator, gas helmet", "gas pump, gasoline pump, petrol pump, island dispenser", "goblet", "go-kart", "golf ball", "golfcart, golf cart", "gondola", "gong, tam-tam", "gown", "grand piano, grand", "greenhouse, nursery, glasshouse", "grille, radiator grille", "grocery store, grocery, food market, market", "guillotine", "hair slide", "hair spray", "half track", "hammer", "hamper", "hand blower, blow dryer, blow drier, hair dryer, hair drier", "hand-held computer, hand-held microcomputer", "handkerchief, hankie, hanky, hankey", "hard disc, hard disk, fixed disk", "harmonica, mouth organ, harp, mouth harp", "harp", "harvester, reaper", "hatchet", "holster", "home theater, home theatre", "honeycomb", "hook, claw", "hoopskirt, crinoline", "horizontal bar, high bar", "horse cart, horse-cart", "hourglass", "ipod", "iron, smoothing iron", "jack-o'-lantern", "jean, blue jean, denim", "jeep, landrover", "jersey, t-shirt, tee shirt", "jigsaw puzzle", "jinrikisha, ricksha, rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat, laboratory coat", "ladle", "lampshade, lamp shade", "laptop, laptop computer", "lawn mower, mower", "lens cap, lens cover", "letter opener, paper knife, paperknife", "library", "lifeboat", "lighter, light, igniter, ignitor", "limousine, limo", "liner, ocean liner", "lipstick, lip rouge", "loafer", "lotion", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "loupe, jeweler's loupe", "lumbermill, sawmill", "magnetic compass", "mailbag, postbag", "mailbox, letter box", "maillot", "maillot, tank suit", "manhole cover", "maraca", "marimba, xylophone", "mask", "matchstick", "maypole", "maze, labyrinth", "measuring cup", "medicine chest, medicine cabinet", "megalith, megalithic structure", "microphone, mike", "microwave, microwave oven", "military uniform", "milk can", "minibus", "miniskirt, mini", "minivan", "missile", "mitten", "mixing bowl", "mobile home, manufactured home", "model t", "modem", "monastery", "monitor", "moped", "mortar", "mortarboard", "mosque", "mosquito net", "motor scooter, scooter", "mountain bike, all-terrain bike, off-roader", "mountain tent", "mouse, computer mouse", "mousetrap", "moving van", "muzzle", "nail", "neck brace", "necklace", "nipple", "notebook, notebook computer", "obelisk", "oboe, hautboy, hautbois", "ocarina, sweet potato", "odometer, hodometer, mileometer, milometer", "oil filter", "organ, pipe organ", "oscilloscope, scope, cathode-ray oscilloscope, cro", "overskirt", "oxcart", "oxygen mask", "packet", "paddle, boat paddle", "paddlewheel, paddle wheel", "padlock", "paintbrush", "pajama, pyjama, pj's, jammies", "palace", "panpipe, pandean pipe, syrinx", "paper towel", "parachute, chute", "parallel bars, bars", "park bench", "parking meter", "passenger car, coach, carriage", "patio, terrace", "pay-phone, pay-station", "pedestal, plinth, footstall", "pencil box, pencil case", "pencil sharpener", "perfume, essence", "petri dish", "photocopier", "pick, plectrum, plectron", "pickelhaube", "picket fence, paling", "pickup, pickup truck", "pier", "piggy bank, penny bank", "pill bottle", "pillow", "ping-pong ball", "pinwheel", "pirate, pirate ship", "pitcher, ewer", "plane, carpenter's plane, woodworking plane", "planetarium", "plastic bag", "plate rack", "plow, plough", "plunger, plumber's helper", "polaroid camera, polaroid land camera", "pole", "police van, police wagon, paddy wagon, patrol wagon, wagon, black maria", "poncho", "pool table, billiard table, snooker table", "pop bottle, soda bottle", "pot, flowerpot", "potter's wheel", "power drill", "prayer rug, prayer mat", "printer", "prison, prison house", "projectile, missile", "projector", "puck, hockey puck", "punching bag, punch bag, punching ball, punchball", "purse", "quill, quill pen", "quilt, comforter, comfort, puff", "racer, race car, racing car", "racket, racquet", "radiator", "radio, wireless", "radio telescope, radio reflector", "rain barrel", "recreational vehicle, rv, r.v.", "reel", "reflex camera", "refrigerator, icebox", "remote control, remote", "restaurant, eating house, eating place, eatery", "revolver, six-gun, six-shooter", "rifle", "rocking chair, rocker", "rotisserie", "rubber eraser, rubber, pencil eraser", "rugby ball", "rule, ruler", "running shoe", "safe", "safety pin", "saltshaker, salt shaker", "sandal", "sarong", "sax, saxophone", "scabbard", "scale, weighing machine", "school bus", "schooner", "scoreboard", "screen, crt screen", "screw", "screwdriver", "seat belt, seatbelt", "sewing machine", "shield, buckler", "shoe shop, shoe-shop, shoe store", "shoji", "shopping basket", "shopping cart", "shovel", "shower cap", "shower curtain", "ski", "ski mask", "sleeping bag", "slide rule, slipstick", "sliding door", "slot, one-armed bandit", "snorkel", "snowmobile", "snowplow, snowplough", "soap dispenser", "soccer ball", "sock", "solar dish, solar collector, solar furnace", "sombrero", "soup bowl", "space bar", "space heater", "space shuttle", "spatula", "speedboat", "spider web, spider's web", "spindle", "sports car, sport car", "spotlight, spot", "stage", "steam locomotive", "steel arch bridge", "steel drum", "stethoscope", "stole", "stone wall", "stopwatch, stop watch", "stove", "strainer", "streetcar, tram, tramcar, trolley, trolley car", "stretcher", "studio couch, day bed", "stupa, tope", "submarine, pigboat, sub, u-boat", "suit, suit of clothes", "sundial", "sunglass", "sunglasses, dark glasses, shades", "sunscreen, sunblock, sun blocker", "suspension bridge", "swab, swob, mop", "sweatshirt", "swimming trunks, bathing trunks", "swing", "switch, electric switch, electrical switch", "syringe", "table lamp", "tank, army tank, armored combat vehicle, armoured combat vehicle", "tape player", "teapot", "teddy, teddy bear", "television, television system", "tennis ball", "thatch, thatched roof", "theater curtain, theatre curtain", "thimble", "thresher, thrasher, threshing machine", "throne", "tile roof", "toaster", "tobacco shop, tobacconist shop, tobacconist", "toilet seat", "torch", "totem pole", "tow truck, tow car, wrecker", "toyshop", "tractor", "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "tray", "trench coat", "tricycle, trike, velocipede", "trimaran", "tripod", "triumphal arch", "trolleybus, trolley coach, trackless trolley", "trombone", "tub, vat", "turnstile", "typewriter keyboard", "umbrella", "unicycle, monocycle", "upright, upright piano", "vacuum, vacuum cleaner", "vase", "vault", "velvet", "vending machine", "vestment", "viaduct", "violin, fiddle", "volleyball", "waffle iron", "wall clock", "wallet, billfold, notecase, pocketbook", "wardrobe, closet, press", "warplane, military plane", "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "washer, automatic washer, washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle", "wig", "window screen", "window shade", "windsor tie", "wine bottle", "wing", "wok", "wooden spoon", "wool, woolen, woollen", "worm fence, snake fence, snake-rail fence, virginia fence", "wreck", "yawl", "yurt", "web site, website, internet site, site", "comic book", "crossword puzzle, crossword", "street sign", "traffic light, traffic signal, stoplight", "book jacket, dust cover, dust jacket, dust wrapper", "menu", "plate", "guacamole", "consomme", "hot pot, hotpot", "trifle", "ice cream, icecream", "ice lolly, lolly, lollipop, popsicle", "french loaf", "bagel, beigel", "pretzel", "cheeseburger", "hotdog, hot dog, red hot", "mashed potato", "head cabbage", "broccoli", "cauliflower", "zucchini, courgette", "spaghetti squash", "acorn squash", "butternut squash", "cucumber, cuke", "artichoke, globe artichoke", "bell pepper", "cardoon", "mushroom", "granny smith", "strawberry", "orange", "lemon", "fig", "pineapple, ananas", "banana", "jackfruit, jak, jack", "custard apple", "pomegranate", "hay", "carbonara", "chocolate sauce, chocolate syrup", "dough", "meat loaf, meatloaf", "pizza, pizza pie", "potpie", "burrito", "red wine", "espresso", "cup", "eggnog", "alp", "bubble", "cliff, drop, drop-off", "coral reef", "geyser", "lakeside, lakeshore", "promontory, headland, head, foreland", "sandbar, sand bar", "seashore, coast, seacoast, sea-coast", "valley, vale", "volcano", "ballplayer, baseball player", "groom, bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum", "corn", "acorn", "hip, rose hip, rosehip", "buckeye, horse chestnut, conker", "coral fungus", "agaric", "gyromitra", "stinkhorn, carrion fungus", "earthstar", "hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa", "bolete", "ear, spike, capitulum", "toilet tissue, toilet paper, bathroom tissue" ]
ernie-ai/finetuned-vit-image-text-classifier	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned-vit-doc-text-classifer This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the ernie-ai/image-text-examples-ar-cn-latin-notext dataset. It achieves the following results on the evaluation set: - Loss: 0.3107 - Accuracy: 0.9030 ## Model description It is an image classificatin model fine-tuned to predict whether an images contains text and if that text is Latin script, Chinese or Arabic. It also classifies non-text images. ## Training and evaluation data Dataset: [ernie-ai/image-text-examples-ar-cn-latin-notext] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2719 \| 2.08 \| 100 \| 0.4120 \| 0.8657 \| \| 0.1027 \| 4.17 \| 200 \| 0.3907 \| 0.8881 \| \| 0.0723 \| 6.25 \| 300 \| 0.3107 \| 0.9030 \| ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "ar_docs", "cn_docs", "latin_docs", "non-text" ]
ernie-ai/autotrain-document-text-language-ar-en-zh-3338392240	# finetuned-MS-swin-doc-text-classifer This model is a fine-tuned version of Microsoft’s Swin Transformer tiny-sized model [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the ernie-ai/image-text-examples-ar-cn-latin-notext dataset. It achieves the following results on the evaluation set: - Loss: 0.267 - Accuracy: 0.882 ## Model description It is an image classificatin model fine-tuned to predict whether an images contains text and if that text is Latin script, Chinese or Arabic. It also classifies non-text images. ## Training and evaluation data Dataset: [ernie-ai/image-text-examples-ar-cn-latin-notext] # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3338392240 - CO2 Emissions (in grams): 2.2267 ## Validation Metrics - Loss: 0.267 - Accuracy: 0.882 - Macro F1: 0.862 - Micro F1: 0.882 - Weighted F1: 0.880 - Macro Precision: 0.877 - Micro Precision: 0.882 - Weighted Precision: 0.883 - Macro Recall: 0.856 - Micro Recall: 0.882 - Weighted Recall: 0.882	[ "ar_docs", "cn_docs", "latin_docs", "non-text" ]
Gokulapriyan/swin-tiny-patch4-window7-224-finetuned-3e	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-3e This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1065 - Accuracy: 0.9606 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4549 \| 1.0 \| 527 \| 0.2910 \| 0.8857 \| \| 0.2838 \| 2.0 \| 1054 \| 0.1524 \| 0.9410 \| \| 0.254 \| 3.0 \| 1581 \| 0.1065 \| 0.9606 \| ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "fnh_frames", "hcc_frames", "hhe_frames", "healthy_frames" ]
Ailyth/3_Labels	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3341092265 - CO2 Emissions (in grams): 2.6501 ## Validation Metrics - Loss: 0.133 - Accuracy: 0.950 - Macro F1: 0.951 - Micro F1: 0.950 - Weighted F1: 0.950 - Macro Precision: 0.951 - Micro Precision: 0.950 - Weighted Precision: 0.950 - Macro Recall: 0.951 - Micro Recall: 0.950 - Weighted Recall: 0.950	[ "cuisine", "versailles", "zhazu" ]
Gokulapriyan/swin-tiny-patch4-window7-224-finetuned-new_dataset_50e	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-new_dataset_50e This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6407 - Accuracy: 0.7973 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.94 \| 4 \| 0.7081 \| 0.6081 \| \| No log \| 1.94 \| 8 \| 0.7104 \| 0.6351 \| \| 0.5516 \| 2.94 \| 12 \| 0.6911 \| 0.6351 \| \| 0.5516 \| 3.94 \| 16 \| 0.7156 \| 0.7027 \| \| 0.537 \| 4.94 \| 20 \| 0.7345 \| 0.7297 \| \| 0.537 \| 5.94 \| 24 \| 0.6745 \| 0.6892 \| \| 0.537 \| 6.94 \| 28 \| 0.7146 \| 0.7297 \| \| 0.5333 \| 7.94 \| 32 \| 0.7057 \| 0.6892 \| \| 0.5333 \| 8.94 \| 36 \| 0.6531 \| 0.7027 \| \| 0.4871 \| 9.94 \| 40 \| 0.6405 \| 0.7027 \| \| 0.4871 \| 10.94 \| 44 \| 0.6126 \| 0.6892 \| \| 0.4871 \| 11.94 \| 48 \| 0.6303 \| 0.7027 \| \| 0.4432 \| 12.94 \| 52 \| 0.6264 \| 0.7027 \| \| 0.4432 \| 13.94 \| 56 \| 0.6347 \| 0.7432 \| \| 0.3669 \| 14.94 \| 60 \| 0.6698 \| 0.6622 \| \| 0.3669 \| 15.94 \| 64 \| 0.6346 \| 0.7568 \| \| 0.3669 \| 16.94 \| 68 \| 0.6510 \| 0.6892 \| \| 0.3704 \| 17.94 \| 72 \| 0.6491 \| 0.6892 \| \| 0.3704 \| 18.94 \| 76 \| 0.5947 \| 0.7568 \| \| 0.3624 \| 19.94 \| 80 \| 0.6248 \| 0.7027 \| \| 0.3624 \| 20.94 \| 84 \| 0.6580 \| 0.7027 \| \| 0.3624 \| 21.94 \| 88 \| 0.6345 \| 0.7162 \| \| 0.3164 \| 22.94 \| 92 \| 0.6092 \| 0.7568 \| \| 0.3164 \| 23.94 \| 96 \| 0.6498 \| 0.7162 \| \| 0.2777 \| 24.94 \| 100 \| 0.6915 \| 0.7703 \| \| 0.2777 \| 25.94 \| 104 \| 0.6482 \| 0.7838 \| \| 0.2777 \| 26.94 \| 108 \| 0.6407 \| 0.7973 \| \| 0.2946 \| 27.94 \| 112 \| 0.6135 \| 0.7838 \| \| 0.2946 \| 28.94 \| 116 \| 0.6819 \| 0.7568 \| \| 0.2546 \| 29.94 \| 120 \| 0.6401 \| 0.7568 \| \| 0.2546 \| 30.94 \| 124 \| 0.6370 \| 0.7432 \| \| 0.2546 \| 31.94 \| 128 \| 0.6488 \| 0.7703 \| \| 0.2477 \| 32.94 \| 132 \| 0.6429 \| 0.7973 \| \| 0.2477 \| 33.94 \| 136 \| 0.6540 \| 0.7703 \| \| 0.1968 \| 34.94 \| 140 \| 0.5895 \| 0.7973 \| \| 0.1968 \| 35.94 \| 144 \| 0.6242 \| 0.7568 \| \| 0.1968 \| 36.94 \| 148 \| 0.6575 \| 0.7568 \| \| 0.2235 \| 37.94 \| 152 \| 0.6263 \| 0.7703 \| \| 0.2235 \| 38.94 \| 156 \| 0.6225 \| 0.7838 \| \| 0.2005 \| 39.94 \| 160 \| 0.6731 \| 0.7703 \| \| 0.2005 \| 40.94 \| 164 \| 0.6844 \| 0.7703 \| \| 0.2005 \| 41.94 \| 168 \| 0.6550 \| 0.7703 \| \| 0.2062 \| 42.94 \| 172 \| 0.6700 \| 0.7703 \| \| 0.2062 \| 43.94 \| 176 \| 0.6661 \| 0.7703 \| \| 0.1933 \| 44.94 \| 180 \| 0.6606 \| 0.7838 \| \| 0.1933 \| 45.94 \| 184 \| 0.6757 \| 0.7703 \| \| 0.1933 \| 46.94 \| 188 \| 0.6889 \| 0.7568 \| \| 0.1895 \| 47.94 \| 192 \| 0.6940 \| 0.7568 \| \| 0.1895 \| 48.94 \| 196 \| 0.6919 \| 0.7568 \| \| 0.1666 \| 49.94 \| 200 \| 0.6899 \| 0.7432 \| ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "0", "1", "2" ]
Kaludi/food-category-classification-v2.0	# Food Category Classification v2.0 This is an updated Food Category Image Classifier model of the [old](https://huggingface.co/Kaludi/food-category-classification) model that has been trained by [Kaludi](https://huggingface.co/Kaludi) to recognize 12 different categories of foods, which includes Bread, Dairy, Dessert, Egg, Fried Food, Fruit, Meat, Noodles, Rice, Seafood, Soup, and Vegetable. It can accurately classify an image of food into one of these categories by analyzing its visual features. This model can be used by food bloggers, restaurants, and recipe websites to quickly categorize and sort their food images, making it easier to manage their content and provide a better user experience. ### Gradio This model supports a [Gradio](https://github.com/gradio-app/gradio) Web UI to run the data-food-classification model: [![Open In HF Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/Kaludi/Food-Category-Classification_V2_App) ## Validation Metrics - Problem type: Multi-class Classification - Model ID: 3353292434 - CO2 Emissions (in grams): 12.4563 - Loss: 0.144 - Accuracy: 0.960 - Macro F1: 0.959 - Micro F1: 0.960 - Weighted F1: 0.959 - Macro Precision: 0.962 - Micro Precision: 0.960 - Weighted Precision: 0.962 - Macro Recall: 0.960 - Micro Recall: 0.960 - Weighted Recall: 0.960	[ "bread", "dairy", "soup", "vegetable", "dessert", "egg", "fried food", "fruit", "meat", "noodles", "rice", "seafood" ]
Mustafa21/my_awesome_food_model	Full notebook : https://github.com/MustafaAlahmid/hugging_face_models/blob/main/Vit-classifier_food_dataset.ipynb --- license: apache-2.0 tags: - generated_from_trainer datasets: - food101 metrics: - accuracy model-index: - name: my_awesome_food_model results: - task: name: Image Classification type: image-classification dataset: name: food101 type: food101 config: default split: train[:1000] args: default metrics: - name: Accuracy type: accuracy value: 0.985 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_food_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 1.2335 - Accuracy: 0.985 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.0523 \| 1.0 \| 50 \| 1.9226 \| 0.935 \| \| 1.3718 \| 2.0 \| 100 \| 1.3422 \| 0.995 \| \| 1.2298 \| 3.0 \| 150 \| 1.2335 \| 0.985 \| ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]
eormeno12/platzi_vit_model	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # platzi_vit_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0328 - Accuracy: 0.9925 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1427 \| 3.85 \| 500 \| 0.0328 \| 0.9925 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "angular_leaf_spot", "bean_rust", "healthy" ]
AllanOuii/resnet50_mask_classification	# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3387392923 - CO2 Emissions (in grams): 1.5545 ## Validation Metrics - Loss: 0.138 - Accuracy: 0.977 - Precision: 0.958 - Recall: 1.000 - AUC: 0.996 - F1: 0.979	[ "mask", "no_mask" ]
JoffreyMa/autotrain-histopathological_image_classification-3393093035	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3393093035 - CO2 Emissions (in grams): 4.0129 ## Validation Metrics - Loss: 0.183 - Accuracy: 0.933 - Macro F1: 0.931 - Micro F1: 0.933 - Weighted F1: 0.933 - Macro Precision: 0.927 - Micro Precision: 0.933 - Weighted Precision: 0.935 - Macro Recall: 0.939 - Micro Recall: 0.933 - Weighted Recall: 0.933	[ "1", "2", "3", "4", "5", "6", "7", "8" ]
JoffreyMa/autotrain-histopathological_image_classification-3393093036	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3393093036 - CO2 Emissions (in grams): 4.8204 ## Validation Metrics - Loss: 0.186 - Accuracy: 0.933 - Macro F1: 0.933 - Micro F1: 0.933 - Weighted F1: 0.932 - Macro Precision: 0.929 - Micro Precision: 0.933 - Weighted Precision: 0.934 - Macro Recall: 0.941 - Micro Recall: 0.933 - Weighted Recall: 0.933	[ "1", "2", "3", "4", "5", "6", "7", "8" ]
JoffreyMa/autotrain-histopathological_image_classification-3393093038	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3393093038 - CO2 Emissions (in grams): 3.5031 ## Validation Metrics - Loss: 0.179 - Accuracy: 0.966 - Macro F1: 0.959 - Micro F1: 0.966 - Weighted F1: 0.966 - Macro Precision: 0.969 - Micro Precision: 0.966 - Weighted Precision: 0.969 - Macro Recall: 0.954 - Micro Recall: 0.966 - Weighted Recall: 0.966	[ "1", "2", "3", "4", "5", "6", "7", "8" ]
JoffreyMa/autotrain-histopathological_image_classification-3393093039	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3393093039 - CO2 Emissions (in grams): 3.9275 ## Validation Metrics - Loss: 0.243 - Accuracy: 0.910 - Macro F1: 0.928 - Micro F1: 0.910 - Weighted F1: 0.910 - Macro Precision: 0.929 - Micro Precision: 0.910 - Weighted Precision: 0.914 - Macro Recall: 0.929 - Micro Recall: 0.910 - Weighted Recall: 0.910	[ "1", "2", "3", "4", "5", "6", "7", "8" ]
JoffreyMa/autotrain-histopathological_image_classification-3393093037	# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3393093037 - CO2 Emissions (in grams): 4.0015 ## Validation Metrics - Loss: 1.988 - Accuracy: 0.348 - Macro F1: 0.210 - Micro F1: 0.348 - Weighted F1: 0.279 - Macro Precision: 0.217 - Micro Precision: 0.348 - Weighted Precision: 0.278 - Macro Recall: 0.245 - Micro Recall: 0.348 - Weighted Recall: 0.348	[ "1", "2", "3", "4", "5", "6", "7", "8" ]
apatidar0/vit-base-beans_own	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-beans_own This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0558 - Accuracy: 0.9850 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "angular_leaf_spot", "bean_rust", "healthy" ]
dhairyakapadia/swin-tiny-patch4-window7-224-finetuned-skin-cancer	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-skin-cancer This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "actinic-keratoses", "basal-cell-carcinoma", "benign-keratosis-like-lesions", "dermatofibroma", "melanocytic-nevi", "melanoma", "vascular-lesions" ]
DunnBC22/vit-base-patch16-224-in21k-weather-images-classification	# vit-base-patch16-224-in21k-weather-images-classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2255 - Accuracy: 0.9340 - Weighted f1: 0.9341 - Micro f1: 0.9340 - Macro f1: 0.9372 - Weighted recall: 0.9340 - Micro recall: 0.9340 - Macro recall: 0.9354 - Weighted precision: 0.9347 - Micro precision: 0.9340 - Macro precision: 0.9398 ## Model description This is a classification model of weather images. For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Weather%20Images/Weather_Images_ViT.ipynb ## Intended uses & limitations This model is intended to demonstrate my ability to solve a complex problem using technology. ## Training and evaluation data Dataset Source: https://www.kaggle.com/datasets/jehanbhathena/weather-dataset ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Weighted f1 \| Micro f1 \| Macro f1 \| Weighted recall \| Micro recall \| Macro recall \| Weighted precision \| Micro precision \| Macro precision \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-----------:\|:--------:\|:--------:\|:---------------:\|:------------:\|:------------:\|:------------------:\|:---------------:\|:---------------:\| \| 2.4333 \| 1.0 \| 337 \| 0.3374 \| 0.9036 \| 0.9028 \| 0.9036 \| 0.9080 \| 0.9036 \| 0.9036 \| 0.9002 \| 0.9088 \| 0.9036 \| 0.9234 \| \| 0.4422 \| 2.0 \| 674 \| 0.2504 \| 0.9228 \| 0.9226 \| 0.9228 \| 0.9285 \| 0.9228 \| 0.9228 \| 0.9273 \| 0.9248 \| 0.9228 \| 0.9318 \| \| 0.1051 \| 3.0 \| 1011 \| 0.2255 \| 0.9340 \| 0.9341 \| 0.9340 \| 0.9372 \| 0.9340 \| 0.9340 \| 0.9354 \| 0.9347 \| 0.9340 \| 0.9398 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.8.0 - Tokenizers 0.12.1	[ "dew", "fogsmog", "snow", "frost", "glaze", "hail", "lightning", "rain", "rainbow", "rime", "sandstorm" ]
asaderu-ai/kebersihan_jalan_detection	## Validation Metrics - Loss: 0.004 - Accuracy: 1.000 - Precision: 1.000 - Recall: 1.000 - AUC: 1.000 - F1: 1.000	[ "bersih", "kotor" ]
Celal11/resnet-50-4-32	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # resnet-50-4-32 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.9705 - Accuracy: 0.6410 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.3833 \| 1.0 \| 224 \| 1.2683 \| 0.5134 \| \| 1.2404 \| 2.0 \| 448 \| 1.1342 \| 0.5659 \| \| 1.1492 \| 3.0 \| 672 \| 1.0359 \| 0.6087 \| \| 1.1433 \| 4.0 \| 896 \| 0.9705 \| 0.6410 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1	[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]
Celal11/resnet-50-0.007	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # resnet-50-0.007 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.9735 - Accuracy: 0.6296 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.007 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.4221 \| 1.0 \| 224 \| 1.2410 \| 0.5274 \| \| 1.2521 \| 2.0 \| 448 \| 1.1716 \| 0.5499 \| \| 1.1609 \| 3.0 \| 672 \| 1.0495 \| 0.5968 \| \| 1.1457 \| 4.0 \| 896 \| 0.9735 \| 0.6296 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1	[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]
davanstrien/autotrain-ia_covers-3416193421	# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3416193421 - CO2 Emissions (in grams): 1.6972 ## Validation Metrics - Loss: 0.213 - Accuracy: 0.904 - Precision: 0.714 - Recall: 0.875 - AUC: 0.948 - F1: 0.787	[ "year-1923-not-very-useful-covers", "year-1923-useful-covers" ]
jsacex/vit-base-patch16-224-in21k-finetuned-lora-food101	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-in21k-finetuned-lora-food101 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 0.1408 - Accuracy: 0.964 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 9 \| 0.5739 \| 0.874 \| \| 2.1968 \| 2.0 \| 18 \| 0.2064 \| 0.944 \| \| 0.3323 \| 3.0 \| 27 \| 0.1521 \| 0.96 \| \| 0.2087 \| 4.0 \| 36 \| 0.1408 \| 0.964 \| \| 0.1678 \| 5.0 \| 45 \| 0.1352 \| 0.962 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.12.1	[ "apple_pie", "baby_back_ribs", "baklava", "beef_carpaccio", "beef_tartare", "beet_salad", "beignets", "bibimbap", "bread_pudding", "breakfast_burrito", "bruschetta", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare", "waffles" ]
MPSTME/swin-tiny-patch4-window7-224-finetuned-skin-cancer	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-skin-cancer This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "actinic-keratoses", "basal-cell-carcinoma", "benign-keratosis-like-lesions", "dermatofibroma", "melanocytic-nevi", "melanoma", "vascular-lesions" ]
lixiqi/beit-base-patch16-224-pt22k-ft22k-finetuned-FER-5e-05-3	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # beit-base-patch16-224-pt22k-ft22k-finetuned-FER-5e-05-3 This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.8598 - Accuracy: 0.6860 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.1566 \| 1.0 \| 224 \| 0.9830 \| 0.6311 \| \| 1.0301 \| 2.0 \| 448 \| 0.8939 \| 0.6730 \| \| 0.991 \| 3.0 \| 672 \| 0.8598 \| 0.6860 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1	[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]
asaderu-ai/kualitas_lemon	## Validation Metrics - Loss: 0.001 - Accuracy: 1.000 - Precision: 1.000 - Recall: 1.000 - AUC: 1.000 - F1: 1.000	[ "bagus", "tidak_bagus" ]
Gokulapriyan/swin-tiny-patch4-window7-224-finetuned-og_dataset_5e	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-og_dataset_5e This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0788 - Accuracy: 0.9705 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4626 \| 1.0 \| 546 \| 0.3468 \| 0.8578 \| \| 0.2915 \| 2.0 \| 1092 \| 0.1998 \| 0.9200 \| \| 0.2333 \| 3.0 \| 1638 \| 0.1155 \| 0.9566 \| \| 0.2019 \| 4.0 \| 2184 \| 0.0977 \| 0.9634 \| \| 0.1713 \| 5.0 \| 2730 \| 0.0788 \| 0.9705 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2	[ "fnh", "hcc", "hhe", "healthy" ]
1024khandsom/autotrain-ant-bee-3482194557	# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3482194557 - CO2 Emissions (in grams): 0.7388 ## Validation Metrics - Loss: 0.013 - Accuracy: 1.000 - Precision: 1.000 - Recall: 1.000 - AUC: 1.000 - F1: 1.000	[ "ants", "bees" ]

amyeroberts/my_food_classifier

# amyeroberts/my_food_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.5833 - Validation Loss: 4.5438 - Train Accuracy: 0.125 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 4.5833 | 4.5438 | 0.125 | 0 | ### Framework versions - Transformers 4.26.0.dev0 - TensorFlow 2.10.0 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2

[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]

poolrf2001/platzi-vit-model-pool-river

# platzi-vit-model-pool-river This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0096 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1561 | 3.85 | 500 | 0.0096 | 1.0 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

[ "angular_leaf_spot", "bean_rust", "healthy" ]

kmewhort/beit-sketch-classifier-pt-metaset-2

# beit-sketch-classifier-pt-metaset-2 This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6703 - Accuracy: 0.8282 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 0.8028 | 1.0 | 76608 | 0.7586 | 0.8007 | | 0.7168 | 2.0 | 153216 | 0.6983 | 0.8154 | | 0.6357 | 3.0 | 229824 | 0.6676 | 0.8240 | | 0.5707 | 4.0 | 306432 | 0.6606 | 0.8276 | | 0.4254 | 5.0 | 383040 | 0.6703 | 0.8282 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2

[ "the eiffel tower", "the great wall of china", "the mona lisa", "aircraft carrier", "airplane", "alarm clock", "alien", "ambulance", "angel", "animal migration", "ant", "anvil", "ape", "apple", "arm", "armchair", "armor", "ashtray", "asparagus", "axe", "backpack", "banana", "bandage", "barn", "baseball", "baseball bat", "basket", "basketball", "bat", "bathtub", "beach", "bear", "beard", "bed", "bee", "beer mug", "beetle", "bell", "belt", "bench", "bicycle", "binoculars", "bird", "birthday cake", "blackberry", "blimp", "blueberry", "book", "bookshelf", "boomerang", "bottle opener", "bottlecap", "bowl", "bowtie", "boy", "bracelet", "brain", "bread", "bridge", "broccoli", "broom", "bucket", "bulldozer", "bus", "bush", "butterfly", "cabin", "cabinet", "cactus", "cake", "calculator", "calendar", "camel", "camera", "camouflage", "campfire", "candle", "cannon", "canoe", "car", "carrot", "castle", "cat", "ceiling fan", "cell phone", "cello", "chair", "chandelier", "chicken", "church", "cigarette", "circle", "clarinet", "clock", "cloud", "coffee cup", "comb", "compass", "computer", "computer monitor", "computer-mouse", "cookie", "cooler", "couch", "cow", "crab", "crane (machine)", "crayon", "crocodile", "crocodilian", "crown", "cruise ship", "cup", "deer", "diamond", "dishwasher", "diving board", "dog", "dolphin", "donut", "door", "door handle", "dragon", "dresser", "drill", "drums", "duck", "dumbbell", "ear", "elbow", "elephant", "elf", "envelope", "eraser", "eye", "eyeglasses", "face", "fairy", "fan", "feather", "fence", "finger", "fire hydrant", "fireplace", "firetruck", "fish", "flamingo", "flashlight", "flip flops", "floor lamp", "flower", "flower with stem", "flying bird", "flying saucer", "foot", "fork", "frog", "frying pan", "garden", "garden hose", "geyser", "giraffe", "girl", "goatee", "golf club", "grapes", "grass", "grenade", "guitar", "hamburger", "hammer", "hand", "harp", "hat", "head", "headphones", "hedgehog", "helicopter", "helmet", "hexagon", "hockey puck", "hockey stick", "horse", "hospital", "hot air balloon", "hot dog", "hot tub", "hot-air balloon", "hotdog", "hourglass", "house", "house plant", "human-skeleton", "hurricane", "ice cream", "ipod", "jack-o-lantern", "jacket", "jail", "jellyfish", "kangaroo", "key", "keyboard", "knee", "knife", "ladder", "lantern", "laptop", "leaf", "leg", "light bulb", "lighter", "lighthouse", "lightning", "line", "lion", "lipstick", "lizard", "lobster", "lollipop", "loudspeaker", "mailbox", "man", "map", "marker", "matches", "megaphone", "mermaid", "microphone", "microscope", "microwave", "monkey", "moon", "mosquito", "motorbike", "motorcycle", "mountain", "mouse", "moustache", "mouth", "mug", "mushroom", "nail", "necklace", "nose", "ocean", "octagon", "octopus", "onion", "oven", "owl", "paint can", "paintbrush", "palm tree", "panda", "pants", "paper clip", "parachute", "parking meter", "parrot", "passport", "peanut", "pear", "peas", "pen", "pencil", "penguin", "person sitting", "person standing", "person walking", "piano", "pickup truck", "picture frame", "pig", "pigeon", "pillow", "pineapple", "pipe (for smoking)", "pistol", "pizza", "planet", "pliers", "police car", "pond", "pool", "popsicle", "postcard", "potato", "potted plant", "power outlet", "present", "pretzel", "pumpkin", "purse", "rabbit", "raccoon", "race car", "racket", "radio", "rain", "rainbow", "rake", "ray", "remote control", "revolver", "rhinoceros", "rifle", "river", "robot", "rocket", "roller coaster", "rollerblades", "rollerskates", "rooster", "sailboat", "sandwich", "santa claus", "satellite", "satellite dish", "saw", "saxophone", "school bus", "scissors", "scorpion", "screwdriver", "sea turtle", "seagull", "seal", "see saw", "shark", "sheep", "ship", "shoe", "shorts", "shovel", "sink", "skateboard", "skull", "skyscraper", "sleeping bag", "smiley face", "snail", "snake", "snorkel", "snowboard", "snowflake", "snowman", "soccer ball", "sock", "socks", "songbird", "space shuttle", "speedboat", "spider", "sponge bob", "spoon", "spreadsheet", "square", "squiggle", "squirrel", "stairs", "stapler", "star", "starfish", "steak", "stereo", "stethoscope", "stitches", "stop sign", "stove", "strawberry", "streetlight", "string bean", "submarine", "suitcase", "sun", "suv", "swan", "sweater", "swing set", "sword", "syringe", "t-shirt", "table", "tablelamp", "tank", "teacup", "teapot", "teddy-bear", "telephone", "television", "tennis-racket", "tent", "tiger", "tire", "toaster", "toe", "toilet", "tomato", "tooth", "toothbrush", "toothpaste", "tornado", "tractor", "traffic light", "train", "tree", "triangle", "trombone", "trousers", "truck", "trumpet", "turtle", "tv", "umbrella", "underwear", "van", "vase", "violin", "volcano", "walkie talkie", "washing machine", "watermelon", "waterslide", "whale", "wheel", "wheelbarrow", "wheelchair", "windmill", "window", "wine bottle", "wine glass", "wineglass", "wizard", "woman", "wristwatch", "yoga", "zebra", "zigzag" ]

jctivensa/Ivenpeople_v2

license: apache-2.0 tags: - vision - image-classification datasets: - imagenet-1k widget: - src: >- https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: >- https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: >- https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace

[ "male", "female", "0-19", "20-29", "30-39", "40-69", "70+" ]

swww/autotrain-mm-2927885005

# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 2927885005 - CO2 Emissions (in grams): 0.3585 ## Validation Metrics - Loss: 0.015 - Accuracy: 1.000 - Precision: 1.000 - Recall: 1.000 - AUC: 1.000 - F1: 1.000

[ "normal", "viral%20pneumonia" ]

swww/autotrain-mm-2927885009

# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 2927885009 - CO2 Emissions (in grams): 0.3748 ## Validation Metrics - Loss: 0.002 - Accuracy: 1.000 - Precision: 1.000 - Recall: 1.000 - AUC: 1.000 - F1: 1.000

[ "normal", "viral%20pneumonia" ]

swww/test

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 2928085012 - CO2 Emissions (in grams): 1.9626 ## Validation Metrics - Loss: 0.226 - Accuracy: 0.925 - Macro F1: 0.925 - Micro F1: 0.925 - Weighted F1: 0.925 - Macro Precision: 0.929 - Micro Precision: 0.925 - Weighted Precision: 0.929 - Macro Recall: 0.925 - Micro Recall: 0.925 - Weighted Recall: 0.925

[ "lung_opacity", "normal", "viral%20pneumonia", "covid" ]

AdamOswald1/autotrain-let-2932785109

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 2932785109 - CO2 Emissions (in grams): 0.0171 ## Validation Metrics - Loss: 1.241 - Accuracy: 0.372 - Macro F1: 0.228 - Micro F1: 0.372 - Weighted F1: 0.344 - Macro Precision: 0.190 - Micro Precision: 0.372 - Weighted Precision: 0.337 - Macro Recall: 0.355 - Micro Recall: 0.372 - Weighted Recall: 0.372

[ "adult chara", "adult chara and young chara", "kris and a soul", "kris next to the ghost of chara", "male kris", "male kris and female kris", "storyshift chara", "storyshift chara and young chara", "teen chara and young chara", "teenager chara and young chara", "young chara", "chara", "female kris", "kris", "kris and adult chara", "kris and chara", "kris and female chara", "kris and male chara", "kris and the player" ]

AdamOswald1/autotrain-let-2932785111

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 2932785111 - CO2 Emissions (in grams): 3.2161 ## Validation Metrics - Loss: 1.165 - Accuracy: 0.376 - Macro F1: 0.269 - Micro F1: 0.376 - Weighted F1: 0.349 - Macro Precision: 0.235 - Micro Precision: 0.376 - Weighted Precision: 0.354 - Macro Recall: 0.413 - Micro Recall: 0.376 - Weighted Recall: 0.376

[ "adult chara", "adult chara and young chara", "kris and a soul", "kris next to the ghost of chara", "male kris", "male kris and female kris", "storyshift chara", "storyshift chara and young chara", "teen chara and young chara", "teenager chara and young chara", "young chara", "chara", "female kris", "kris", "kris and adult chara", "kris and chara", "kris and female chara", "kris and male chara", "kris and the player" ]

ivensamdh/autotrain-convnext_test_masterage-2947785378

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 2947785378 - CO2 Emissions (in grams): 2.5972 ## Validation Metrics - Loss: 1.470 - Accuracy: 0.408 - Macro F1: 0.261 - Micro F1: 0.408 - Weighted F1: 0.392 - Macro Precision: 0.285 - Micro Precision: 0.408 - Weighted Precision: 0.421 - Macro Recall: 0.266 - Micro Recall: 0.408 - Weighted Recall: 0.408

[ "age0to5", "age11to15", "age16to25", "age26to35", "age36to49", "age50to69", "age6to10", "age70to99" ]

jayanta/google-vit-base-patch16-224-cartoon-face-recognition

# google-vit-base-patch16-224-cartoon-face-recognition This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3707 - Accuracy: 0.9005 - Precision: 0.9066 - Recall: 0.9005 - F1: 0.8984 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00012 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 0.89 | 6 | 0.5459 | 0.8611 | 0.8683 | 0.8611 | 0.8577 | | 0.0812 | 1.89 | 12 | 0.4703 | 0.8796 | 0.8833 | 0.8796 | 0.8764 | | 0.0812 | 2.89 | 18 | 0.4430 | 0.8935 | 0.8969 | 0.8935 | 0.8906 | | 0.0307 | 3.89 | 24 | 0.4045 | 0.8819 | 0.8849 | 0.8819 | 0.8767 | | 0.0091 | 4.89 | 30 | 0.3672 | 0.9005 | 0.9025 | 0.9005 | 0.8980 | | 0.0091 | 5.89 | 36 | 0.3841 | 0.9028 | 0.9125 | 0.9028 | 0.9011 | | 0.0043 | 6.89 | 42 | 0.3926 | 0.9005 | 0.9073 | 0.9005 | 0.8972 | | 0.0043 | 7.89 | 48 | 0.3786 | 0.8958 | 0.9005 | 0.8958 | 0.8931 | | 0.0031 | 8.89 | 54 | 0.3791 | 0.9028 | 0.9091 | 0.9028 | 0.9007 | | 0.002 | 9.89 | 60 | 0.3677 | 0.9028 | 0.9106 | 0.9028 | 0.9001 | | 0.002 | 10.89 | 66 | 0.3740 | 0.9028 | 0.9099 | 0.9028 | 0.9007 | | 0.0027 | 11.89 | 72 | 0.3869 | 0.8981 | 0.9043 | 0.8981 | 0.8956 | | 0.0027 | 12.89 | 78 | 0.3801 | 0.8981 | 0.9021 | 0.8981 | 0.8954 | | 0.004 | 13.89 | 84 | 0.3674 | 0.9051 | 0.9113 | 0.9051 | 0.9028 | | 0.0024 | 14.89 | 90 | 0.3620 | 0.9051 | 0.9096 | 0.9051 | 0.9027 | | 0.0024 | 15.89 | 96 | 0.3670 | 0.9028 | 0.9089 | 0.9028 | 0.9006 | | 0.0021 | 16.89 | 102 | 0.3827 | 0.9005 | 0.9065 | 0.9005 | 0.8980 | | 0.0021 | 17.89 | 108 | 0.3748 | 0.8981 | 0.9049 | 0.8981 | 0.8958 | | 0.0022 | 18.89 | 114 | 0.3825 | 0.9028 | 0.9101 | 0.9028 | 0.9006 | | 0.0019 | 19.89 | 120 | 0.3707 | 0.9005 | 0.9066 | 0.9005 | 0.8984 | ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1

[ "aishwarya_rai", "akhilesh_yadav", "ms_dhoni", "mahatma_gandhi", "mamata_banerjee", "mukesh_ambani", "narendra_modi", "rahul_gandhi", "ranveer_singh", "ratan_tata", "sachin_tendulkar", "shahrukh_khan", "akshay_kumar", "sonia_gandhi", "sundar_pichai", "virat_kohli", "yogi_adityanath", "amit_saha", "arijit_singh", "arvind_kejriwal", "baba_ramdev", "gautam_adani", "kishore_kumar", "lata_mangeshkar" ]

flyswot/autotrain-flyswot-jan-2950385442

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 2950385442 - CO2 Emissions (in grams): 3.5608 ## Validation Metrics - Loss: 0.206 - Accuracy: 0.941 - Macro F1: 0.919 - Micro F1: 0.941 - Weighted F1: 0.940 - Macro Precision: 0.941 - Micro Precision: 0.941 - Weighted Precision: 0.940 - Macro Recall: 0.901 - Micro Recall: 0.941 - Weighted Recall: 0.941

[ "container", "control shot", "cover", "edge + spine", "flysheet", "other", "page + folio", "scroll" ]

Pafebla/autotrain-reconocimiento_banderas-2960885598

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 2960885598 - CO2 Emissions (in grams): 1.3324 ## Validation Metrics - Loss: 0.266 - Accuracy: 0.950 - Macro F1: 0.943 - Micro F1: 0.950 - Weighted F1: 0.949 - Macro Precision: 0.963 - Micro Precision: 0.950 - Weighted Precision: 0.956 - Macro Recall: 0.933 - Micro Recall: 0.950 - Weighted Recall: 0.950

[ "conflictivas", "no_conflictiva", "sin" ]

itslogannye/benignEnchondroma-vs-lowGradeMalignantChondrosarcoma-histopathology

# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 2962985627 - CO2 Emissions (in grams): 3.6593 ## Validation Metrics - Loss: 0.229 - Accuracy: 0.887 - Precision: 0.939 - Recall: 0.821 - AUC: 0.969 - F1: 0.876

[ "enchondroma", "low-grade chondrosarcoma" ]

Rocketknight1/my_food_classifier

# Rocketknight1/my_food_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1099 - Validation Loss: 0.2439 - Train Accuracy: 0.947 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 2.5330 | 1.3738 | 0.923 | 0 | | 0.8871 | 0.6131 | 0.95 | 1 | | 0.3703 | 0.4042 | 0.937 | 2 | | 0.1942 | 0.2981 | 0.94 | 3 | | 0.1099 | 0.2439 | 0.947 | 4 | ### Framework versions - Transformers 4.26.0.dev0 - TensorFlow 2.11.0 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2

[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]

MariaK/my_food_classifier

# MariaK/my_food_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1163 - Validation Loss: 0.2927 - Train Accuracy: 0.936 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 2.5557 | 1.4200 | 0.897 | 0 | | 0.8928 | 0.6662 | 0.931 | 1 | | 0.3831 | 0.4001 | 0.938 | 2 | | 0.1892 | 0.3486 | 0.93 | 3 | | 0.1163 | 0.2927 | 0.936 | 4 | ### Framework versions - Transformers 4.25.1 - TensorFlow 2.9.2 - Datasets 2.8.0 - Tokenizers 0.13.2

[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]

ivensamdh/genderage2

# genderage2 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2771 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 11 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.3737 | 0.89 | 100 | 0.3702 | | 0.3208 | 1.79 | 200 | 0.3308 | | 0.2681 | 2.68 | 300 | 0.2977 | | 0.2334 | 3.57 | 400 | 0.2875 | | 0.2093 | 4.46 | 500 | 0.2840 | | 0.1989 | 5.36 | 600 | 0.2798 | | 0.1801 | 6.25 | 700 | 0.2785 | | 0.1707 | 7.14 | 800 | 0.2771 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2

[ "female", "male", "age0to5", "age6to10", "age11to15", "age16to25", "age26to35", "age36to49", "age50to69", "age70to99" ]

platzi/platzi-vit-model-javi-javiai

# platzi-vit-model-javi-javiai This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0623 - Accuracy: 0.9774 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0532 | 3.85 | 500 | 0.0623 | 0.9774 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

[ "angular_leaf_spot", "bean_rust", "healthy" ]

ivensamdh/genderage_convnext

# genderage_convnext This model is a fine-tuned version of [facebook/convnext-base-224](https://huggingface.co/facebook/convnext-base-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3896 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 11 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.5114 | 0.89 | 100 | 0.4998 | | 0.425 | 1.79 | 200 | 0.4246 | | 0.4124 | 2.68 | 300 | 0.4100 | | 0.4034 | 3.57 | 400 | 0.4037 | | 0.3938 | 4.46 | 500 | 0.3992 | | 0.4021 | 5.36 | 600 | 0.3952 | | 0.3843 | 6.25 | 700 | 0.3918 | | 0.3804 | 7.14 | 800 | 0.3896 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2

[ "female", "male", "age0to5", "age6to10", "age11to15", "age16to25", "age26to35", "age36to49", "age50to69", "age70to99" ]

Arm627/WaspImageRecComap

# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 2977085925 - CO2 Emissions (in grams): 0.8069 ## Validation Metrics - Loss: 0.647 - Accuracy: 0.697 - Precision: 0.065 - Recall: 0.667 - AUC: 0.601 - F1: 0.118

[ "negative", "positive" ]

sbrandeis-test-org/autotrain-retrain-db16d58-2983986070

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 2983986070 - CO2 Emissions (in grams): 0.5760 ## Validation Metrics - Loss: 0.001 - Accuracy: 1.000 - Macro F1: 1.000 - Micro F1: 1.000 - Weighted F1: 1.000 - Macro Precision: 1.000 - Micro Precision: 1.000 - Weighted Precision: 1.000 - Macro Recall: 1.000 - Micro Recall: 1.000 - Weighted Recall: 1.000

[ "adonis", "african giant swallowtail", "american snoot" ]

fernando232s/vit-model1

# vit-model1 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0584 - Accuracy: 0.9774 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1296 | 3.85 | 500 | 0.0584 | 0.9774 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

[ "angular_leaf_spot", "bean_rust", "healthy" ]

jayanta/microsoft-resnet-50-cartoon-emotion-detection

# microsoft-resnet-50-cartoon-emotion-detection This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4801 - Accuracy: 0.8165 - Precision: 0.8182 - Recall: 0.8165 - F1: 0.8173 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00012 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 80 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 0.97 | 8 | 1.3855 | 0.2294 | 0.2697 | 0.2294 | 0.2165 | | 1.4222 | 1.97 | 16 | 1.3792 | 0.2569 | 0.2808 | 0.2569 | 0.2543 | | 1.4183 | 2.97 | 24 | 1.3646 | 0.3853 | 0.4102 | 0.3853 | 0.3511 | | 1.4097 | 3.97 | 32 | 1.3563 | 0.4128 | 0.5062 | 0.4128 | 0.3245 | | 1.3944 | 4.97 | 40 | 1.3462 | 0.4037 | 0.3927 | 0.4037 | 0.2939 | | 1.3944 | 5.97 | 48 | 1.3223 | 0.4037 | 0.5152 | 0.4037 | 0.2841 | | 1.411 | 6.97 | 56 | 1.3040 | 0.4128 | 0.4404 | 0.4128 | 0.2985 | | 1.346 | 7.97 | 64 | 1.2700 | 0.4954 | 0.4960 | 0.4954 | 0.4093 | | 1.3031 | 8.97 | 72 | 1.2150 | 0.5596 | 0.5440 | 0.5596 | 0.4672 | | 1.2371 | 9.97 | 80 | 1.1580 | 0.5963 | 0.5659 | 0.5963 | 0.5101 | | 1.2371 | 10.97 | 88 | 1.0670 | 0.6055 | 0.7279 | 0.6055 | 0.5211 | | 1.1736 | 11.97 | 96 | 0.9856 | 0.6606 | 0.5537 | 0.6606 | 0.5772 | | 1.0457 | 12.97 | 104 | 0.8963 | 0.6697 | 0.7631 | 0.6697 | 0.5965 | | 0.953 | 13.97 | 112 | 0.8547 | 0.6697 | 0.6885 | 0.6697 | 0.6081 | | 0.8579 | 14.97 | 120 | 0.7849 | 0.7156 | 0.7396 | 0.7156 | 0.6643 | | 0.8579 | 15.97 | 128 | 0.7564 | 0.7431 | 0.7372 | 0.7431 | 0.7119 | | 0.8167 | 16.97 | 136 | 0.7133 | 0.7615 | 0.7507 | 0.7615 | 0.7211 | | 0.7273 | 17.97 | 144 | 0.6888 | 0.7523 | 0.7379 | 0.7523 | 0.7202 | | 0.6547 | 18.97 | 152 | 0.6592 | 0.7798 | 0.7773 | 0.7798 | 0.7577 | | 0.5963 | 19.97 | 160 | 0.6136 | 0.7706 | 0.7642 | 0.7706 | 0.7551 | | 0.5963 | 20.97 | 168 | 0.5723 | 0.7890 | 0.7802 | 0.7890 | 0.7787 | | 0.551 | 21.97 | 176 | 0.5686 | 0.7890 | 0.7761 | 0.7890 | 0.7781 | | 0.4929 | 22.97 | 184 | 0.5597 | 0.7706 | 0.7649 | 0.7706 | 0.7651 | | 0.4309 | 23.97 | 192 | 0.5234 | 0.7890 | 0.7774 | 0.7890 | 0.7810 | | 0.3945 | 24.97 | 200 | 0.5008 | 0.7890 | 0.7837 | 0.7890 | 0.7813 | | 0.3945 | 25.97 | 208 | 0.5289 | 0.7523 | 0.7537 | 0.7523 | 0.7529 | | 0.3704 | 26.97 | 216 | 0.4399 | 0.7982 | 0.7958 | 0.7982 | 0.7963 | | 0.3267 | 27.97 | 224 | 0.4539 | 0.8073 | 0.7983 | 0.8073 | 0.8005 | | 0.2966 | 28.97 | 232 | 0.4735 | 0.7798 | 0.7892 | 0.7798 | 0.7837 | | 0.2645 | 29.97 | 240 | 0.4594 | 0.7706 | 0.7706 | 0.7706 | 0.7706 | | 0.2645 | 30.97 | 248 | 0.4699 | 0.7523 | 0.7554 | 0.7523 | 0.7533 | | 0.2527 | 31.97 | 256 | 0.4551 | 0.7890 | 0.7856 | 0.7890 | 0.7857 | | 0.2202 | 32.97 | 264 | 0.4458 | 0.8165 | 0.8198 | 0.8165 | 0.8170 | | 0.2006 | 33.97 | 272 | 0.4632 | 0.7798 | 0.7941 | 0.7798 | 0.7850 | | 0.1589 | 34.97 | 280 | 0.4651 | 0.7890 | 0.7993 | 0.7890 | 0.7925 | | 0.1589 | 35.97 | 288 | 0.4595 | 0.7798 | 0.7824 | 0.7798 | 0.7804 | | 0.153 | 36.97 | 296 | 0.4584 | 0.7615 | 0.7691 | 0.7615 | 0.7633 | | 0.1427 | 37.97 | 304 | 0.4608 | 0.7798 | 0.7830 | 0.7798 | 0.7796 | | 0.113 | 38.97 | 312 | 0.4571 | 0.7890 | 0.7922 | 0.7890 | 0.7899 | | 0.1146 | 39.97 | 320 | 0.5270 | 0.7615 | 0.7651 | 0.7615 | 0.7613 | | 0.1146 | 40.97 | 328 | 0.4888 | 0.7706 | 0.7782 | 0.7706 | 0.7710 | | 0.1275 | 41.97 | 336 | 0.4523 | 0.7890 | 0.7809 | 0.7890 | 0.7837 | | 0.0959 | 42.97 | 344 | 0.4697 | 0.7798 | 0.7753 | 0.7798 | 0.7767 | | 0.0882 | 43.97 | 352 | 0.4286 | 0.7706 | 0.7686 | 0.7706 | 0.7686 | | 0.0847 | 44.97 | 360 | 0.5317 | 0.7890 | 0.7993 | 0.7890 | 0.7925 | | 0.0847 | 45.97 | 368 | 0.5431 | 0.7615 | 0.7700 | 0.7615 | 0.7647 | | 0.0813 | 46.97 | 376 | 0.4432 | 0.8257 | 0.8435 | 0.8257 | 0.8284 | | 0.0768 | 47.97 | 384 | 0.4886 | 0.7982 | 0.8005 | 0.7982 | 0.7956 | | 0.0627 | 48.97 | 392 | 0.5373 | 0.7982 | 0.8072 | 0.7982 | 0.8010 | | 0.0688 | 49.97 | 400 | 0.5897 | 0.7798 | 0.7892 | 0.7798 | 0.7822 | | 0.0688 | 50.97 | 408 | 0.5115 | 0.7982 | 0.8015 | 0.7982 | 0.7992 | | 0.0676 | 51.97 | 416 | 0.4881 | 0.7982 | 0.7998 | 0.7982 | 0.7978 | | 0.0539 | 52.97 | 424 | 0.4820 | 0.8073 | 0.8139 | 0.8073 | 0.8077 | | 0.0596 | 53.97 | 432 | 0.4450 | 0.8257 | 0.8246 | 0.8257 | 0.8244 | | 0.0611 | 54.97 | 440 | 0.5057 | 0.7890 | 0.8008 | 0.7890 | 0.7924 | | 0.0611 | 55.97 | 448 | 0.4918 | 0.7982 | 0.8056 | 0.7982 | 0.8008 | | 0.0643 | 56.97 | 456 | 0.5946 | 0.7523 | 0.7587 | 0.7523 | 0.7545 | | 0.0605 | 57.97 | 464 | 0.4888 | 0.8073 | 0.8239 | 0.8073 | 0.8121 | | 0.063 | 58.97 | 472 | 0.5917 | 0.7890 | 0.8051 | 0.7890 | 0.7937 | | 0.0595 | 59.97 | 480 | 0.5117 | 0.7890 | 0.7904 | 0.7890 | 0.7894 | | 0.0595 | 60.97 | 488 | 0.5497 | 0.7615 | 0.7692 | 0.7615 | 0.7635 | | 0.0554 | 61.97 | 496 | 0.4742 | 0.8165 | 0.8101 | 0.8165 | 0.8126 | | 0.0557 | 62.97 | 504 | 0.5369 | 0.7890 | 0.7886 | 0.7890 | 0.7886 | | 0.0539 | 63.97 | 512 | 0.5440 | 0.7890 | 0.7922 | 0.7890 | 0.7899 | | 0.048 | 64.97 | 520 | 0.5924 | 0.7890 | 0.7878 | 0.7890 | 0.7883 | | 0.048 | 65.97 | 528 | 0.4863 | 0.8440 | 0.8440 | 0.8440 | 0.8440 | | 0.045 | 66.97 | 536 | 0.5850 | 0.8073 | 0.8076 | 0.8073 | 0.8047 | | 0.047 | 67.97 | 544 | 0.4939 | 0.8257 | 0.8212 | 0.8257 | 0.8227 | | 0.0412 | 68.97 | 552 | 0.4850 | 0.7890 | 0.7912 | 0.7890 | 0.7900 | | 0.0392 | 69.97 | 560 | 0.5066 | 0.8257 | 0.8265 | 0.8257 | 0.8258 | | 0.0392 | 70.97 | 568 | 0.4965 | 0.8073 | 0.8053 | 0.8073 | 0.8058 | | 0.0423 | 71.97 | 576 | 0.4717 | 0.8349 | 0.8376 | 0.8349 | 0.8351 | | 0.0471 | 72.97 | 584 | 0.4845 | 0.8257 | 0.8378 | 0.8257 | 0.8296 | | 0.0322 | 73.97 | 592 | 0.5188 | 0.7706 | 0.7689 | 0.7706 | 0.7693 | | 0.042 | 74.97 | 600 | 0.5242 | 0.7706 | 0.7699 | 0.7706 | 0.7701 | | 0.042 | 75.97 | 608 | 0.5945 | 0.7798 | 0.7824 | 0.7798 | 0.7804 | | 0.0416 | 76.97 | 616 | 0.5432 | 0.7982 | 0.8038 | 0.7982 | 0.7993 | | 0.0399 | 77.97 | 624 | 0.5381 | 0.7982 | 0.8072 | 0.7982 | 0.7994 | | 0.0439 | 78.97 | 632 | 0.6181 | 0.7798 | 0.7878 | 0.7798 | 0.7827 | | 0.0462 | 79.97 | 640 | 0.4801 | 0.8165 | 0.8182 | 0.8165 | 0.8173 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.11.0

[ "angry1", "happy1", "neutral1", "sad1" ]

jayanta/microsoft-resnet-50-cartoon-face-recognition

# microsoft-resnet-50-cartoon-face-recognition This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.8508 - Accuracy: 0.7755 - Precision: 0.7715 - Recall: 0.7755 - F1: 0.7676 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00012 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 0.89 | 6 | 3.1774 | 0.0370 | 0.0069 | 0.0370 | 0.0098 | | 3.4185 | 1.89 | 12 | 3.1739 | 0.0301 | 0.0100 | 0.0301 | 0.0126 | | 3.4185 | 2.89 | 18 | 3.1668 | 0.0440 | 0.0805 | 0.0440 | 0.0340 | | 3.6463 | 3.89 | 24 | 3.1583 | 0.0370 | 0.0180 | 0.0370 | 0.0151 | | 3.3899 | 4.89 | 30 | 3.1425 | 0.0741 | 0.0610 | 0.0741 | 0.0453 | | 3.3899 | 5.89 | 36 | 3.1262 | 0.0856 | 0.0334 | 0.0856 | 0.0405 | | 3.5947 | 6.89 | 42 | 3.1055 | 0.1019 | 0.0784 | 0.1019 | 0.0481 | | 3.5947 | 7.89 | 48 | 3.0841 | 0.1181 | 0.1071 | 0.1181 | 0.0500 | | 3.5553 | 8.89 | 54 | 3.0650 | 0.1065 | 0.0216 | 0.1065 | 0.0343 | | 3.2713 | 9.89 | 60 | 3.0351 | 0.1273 | 0.0323 | 0.1273 | 0.0418 | | 3.2713 | 10.89 | 66 | 3.0069 | 0.1227 | 0.0311 | 0.1227 | 0.0390 | | 3.4382 | 11.89 | 72 | 2.9754 | 0.1204 | 0.0353 | 0.1204 | 0.0366 | | 3.4382 | 12.89 | 78 | 2.9455 | 0.1227 | 0.0224 | 0.1227 | 0.0338 | | 3.3573 | 13.89 | 84 | 2.9167 | 0.1204 | 0.0213 | 0.1204 | 0.0332 | | 3.0549 | 14.89 | 90 | 2.8841 | 0.1227 | 0.0474 | 0.1227 | 0.0408 | | 3.0549 | 15.89 | 96 | 2.8534 | 0.1412 | 0.1174 | 0.1412 | 0.0540 | | 3.1853 | 16.89 | 102 | 2.8143 | 0.1505 | 0.1595 | 0.1505 | 0.0667 | | 3.1853 | 17.89 | 108 | 2.7771 | 0.1667 | 0.1693 | 0.1667 | 0.0719 | | 3.0871 | 18.89 | 114 | 2.7400 | 0.1759 | 0.1454 | 0.1759 | 0.0896 | | 2.7666 | 19.89 | 120 | 2.7048 | 0.2014 | 0.0927 | 0.2014 | 0.1051 | | 2.7666 | 20.89 | 126 | 2.6458 | 0.2315 | 0.1622 | 0.2315 | 0.1250 | | 2.846 | 21.89 | 132 | 2.5803 | 0.2569 | 0.2386 | 0.2569 | 0.1470 | | 2.846 | 22.89 | 138 | 2.5291 | 0.2639 | 0.2725 | 0.2639 | 0.1523 | | 2.7428 | 23.89 | 144 | 2.4916 | 0.2870 | 0.2114 | 0.2870 | 0.1811 | | 2.4183 | 24.89 | 150 | 2.4273 | 0.3079 | 0.2322 | 0.3079 | 0.2048 | | 2.4183 | 25.89 | 156 | 2.3923 | 0.3194 | 0.2937 | 0.3194 | 0.2238 | | 2.5064 | 26.89 | 162 | 2.3349 | 0.3403 | 0.3183 | 0.3403 | 0.2494 | | 2.5064 | 27.89 | 168 | 2.2977 | 0.3542 | 0.3554 | 0.3542 | 0.2663 | | 2.4046 | 28.89 | 174 | 2.2363 | 0.3773 | 0.3214 | 0.3773 | 0.2981 | | 2.1201 | 29.89 | 180 | 2.1791 | 0.3889 | 0.4024 | 0.3889 | 0.3179 | | 2.1201 | 30.89 | 186 | 2.1448 | 0.4144 | 0.4079 | 0.4144 | 0.3455 | | 2.1705 | 31.89 | 192 | 2.0969 | 0.4306 | 0.4214 | 0.4306 | 0.3583 | | 2.1705 | 32.89 | 198 | 2.0535 | 0.4468 | 0.4448 | 0.4468 | 0.3797 | | 2.0295 | 33.89 | 204 | 1.9940 | 0.4745 | 0.4877 | 0.4745 | 0.4133 | | 1.8114 | 34.89 | 210 | 1.9467 | 0.4861 | 0.4952 | 0.4861 | 0.4261 | | 1.8114 | 35.89 | 216 | 1.8896 | 0.4931 | 0.4510 | 0.4931 | 0.4321 | | 1.8048 | 36.89 | 222 | 1.8404 | 0.5046 | 0.4859 | 0.5046 | 0.4507 | | 1.8048 | 37.89 | 228 | 1.7999 | 0.5278 | 0.5142 | 0.5278 | 0.4816 | | 1.6862 | 38.89 | 234 | 1.7363 | 0.5324 | 0.5169 | 0.5324 | 0.4844 | | 1.4545 | 39.89 | 240 | 1.7104 | 0.5440 | 0.5100 | 0.5440 | 0.4971 | | 1.4545 | 40.89 | 246 | 1.6492 | 0.5648 | 0.5239 | 0.5648 | 0.5138 | | 1.4444 | 41.89 | 252 | 1.6076 | 0.5671 | 0.5329 | 0.5671 | 0.5260 | | 1.4444 | 42.89 | 258 | 1.5784 | 0.5741 | 0.5708 | 0.5741 | 0.5424 | | 1.3124 | 43.89 | 264 | 1.5259 | 0.6019 | 0.5977 | 0.6019 | 0.5619 | | 1.1645 | 44.89 | 270 | 1.4814 | 0.6181 | 0.6033 | 0.6181 | 0.5880 | | 1.1645 | 45.89 | 276 | 1.4697 | 0.6088 | 0.6033 | 0.6088 | 0.5803 | | 1.1307 | 46.89 | 282 | 1.4380 | 0.6088 | 0.6015 | 0.6088 | 0.5769 | | 1.1307 | 47.89 | 288 | 1.3872 | 0.6227 | 0.6085 | 0.6227 | 0.5917 | | 1.0347 | 48.89 | 294 | 1.3709 | 0.6157 | 0.6039 | 0.6157 | 0.5880 | | 0.8962 | 49.89 | 300 | 1.3415 | 0.6296 | 0.6120 | 0.6296 | 0.6057 | | 0.8962 | 50.89 | 306 | 1.3290 | 0.6389 | 0.6327 | 0.6389 | 0.6134 | | 0.8898 | 51.89 | 312 | 1.2836 | 0.6389 | 0.6192 | 0.6389 | 0.6119 | | 0.8898 | 52.89 | 318 | 1.2665 | 0.6412 | 0.6186 | 0.6412 | 0.6162 | | 0.7886 | 53.89 | 324 | 1.2272 | 0.6551 | 0.6431 | 0.6551 | 0.6319 | | 0.6794 | 54.89 | 330 | 1.2144 | 0.6806 | 0.6643 | 0.6806 | 0.6629 | | 0.6794 | 55.89 | 336 | 1.1817 | 0.6806 | 0.6666 | 0.6806 | 0.6642 | | 0.6459 | 56.89 | 342 | 1.1702 | 0.6782 | 0.6591 | 0.6782 | 0.6574 | | 0.6459 | 57.89 | 348 | 1.0947 | 0.7037 | 0.6863 | 0.7037 | 0.6883 | | 0.6075 | 58.89 | 354 | 1.1227 | 0.7037 | 0.6874 | 0.7037 | 0.6867 | | 0.4979 | 59.89 | 360 | 1.0849 | 0.7083 | 0.6813 | 0.7083 | 0.6895 | | 0.4979 | 60.89 | 366 | 1.0742 | 0.7153 | 0.6924 | 0.7153 | 0.6976 | | 0.4895 | 61.89 | 372 | 1.0452 | 0.7245 | 0.7020 | 0.7245 | 0.7057 | | 0.4895 | 62.89 | 378 | 1.0435 | 0.7361 | 0.7316 | 0.7361 | 0.7235 | | 0.456 | 63.89 | 384 | 1.0698 | 0.6921 | 0.6835 | 0.6921 | 0.6783 | | 0.3816 | 64.89 | 390 | 1.0126 | 0.7222 | 0.7064 | 0.7222 | 0.7091 | | 0.3816 | 65.89 | 396 | 0.9934 | 0.7361 | 0.7247 | 0.7361 | 0.7205 | | 0.3599 | 66.89 | 402 | 0.9960 | 0.7292 | 0.7213 | 0.7292 | 0.7170 | | 0.3599 | 67.89 | 408 | 1.0141 | 0.7222 | 0.7148 | 0.7222 | 0.7087 | | 0.3484 | 68.89 | 414 | 0.9934 | 0.7222 | 0.7125 | 0.7222 | 0.7107 | | 0.2939 | 69.89 | 420 | 0.9835 | 0.7431 | 0.7417 | 0.7431 | 0.7349 | | 0.2939 | 70.89 | 426 | 0.9870 | 0.7315 | 0.7275 | 0.7315 | 0.7217 | | 0.285 | 71.89 | 432 | 0.9656 | 0.7431 | 0.7411 | 0.7431 | 0.7340 | | 0.285 | 72.89 | 438 | 0.9462 | 0.7338 | 0.7320 | 0.7338 | 0.7267 | | 0.2463 | 73.89 | 444 | 0.9513 | 0.7454 | 0.7467 | 0.7454 | 0.7384 | | 0.2328 | 74.89 | 450 | 0.9334 | 0.7361 | 0.7389 | 0.7361 | 0.7286 | | 0.2328 | 75.89 | 456 | 0.9375 | 0.7384 | 0.7278 | 0.7384 | 0.7291 | | 0.2208 | 76.89 | 462 | 0.9332 | 0.7407 | 0.7357 | 0.7407 | 0.7322 | | 0.2208 | 77.89 | 468 | 0.9408 | 0.7384 | 0.7406 | 0.7384 | 0.7346 | | 0.2177 | 78.89 | 474 | 0.9059 | 0.7222 | 0.7183 | 0.7222 | 0.7136 | | 0.1734 | 79.89 | 480 | 0.9517 | 0.7315 | 0.7371 | 0.7315 | 0.7257 | | 0.1734 | 80.89 | 486 | 0.9063 | 0.7523 | 0.7462 | 0.7523 | 0.7424 | | 0.1791 | 81.89 | 492 | 0.9171 | 0.7454 | 0.7461 | 0.7454 | 0.7386 | | 0.1791 | 82.89 | 498 | 0.8846 | 0.7523 | 0.7561 | 0.7523 | 0.7485 | | 0.1681 | 83.89 | 504 | 0.8871 | 0.7384 | 0.7431 | 0.7384 | 0.7320 | | 0.1573 | 84.89 | 510 | 0.9118 | 0.7454 | 0.7474 | 0.7454 | 0.7395 | | 0.1573 | 85.89 | 516 | 0.9006 | 0.7407 | 0.7432 | 0.7407 | 0.7366 | | 0.1439 | 86.89 | 522 | 0.8703 | 0.7616 | 0.7693 | 0.7616 | 0.7579 | | 0.1439 | 87.89 | 528 | 0.8988 | 0.7454 | 0.7570 | 0.7454 | 0.7401 | | 0.1362 | 88.89 | 534 | 0.9234 | 0.7454 | 0.7477 | 0.7454 | 0.7396 | | 0.1249 | 89.89 | 540 | 0.8860 | 0.75 | 0.7473 | 0.75 | 0.7425 | | 0.1249 | 90.89 | 546 | 0.8608 | 0.7546 | 0.7601 | 0.7546 | 0.7513 | | 0.1264 | 91.89 | 552 | 0.8871 | 0.7593 | 0.7640 | 0.7593 | 0.7560 | | 0.1264 | 92.89 | 558 | 0.8432 | 0.7639 | 0.7727 | 0.7639 | 0.7599 | | 0.1201 | 93.89 | 564 | 0.8654 | 0.7639 | 0.7698 | 0.7639 | 0.7569 | | 0.1117 | 94.89 | 570 | 0.8856 | 0.7454 | 0.7569 | 0.7454 | 0.7415 | | 0.1117 | 95.89 | 576 | 0.8668 | 0.7546 | 0.7686 | 0.7546 | 0.7535 | | 0.1128 | 96.89 | 582 | 0.8630 | 0.7662 | 0.7698 | 0.7662 | 0.7619 | | 0.1128 | 97.89 | 588 | 0.8551 | 0.7731 | 0.7826 | 0.7731 | 0.7696 | | 0.1155 | 98.89 | 594 | 0.8697 | 0.7708 | 0.7738 | 0.7708 | 0.7643 | | 0.0987 | 99.89 | 600 | 0.8613 | 0.7546 | 0.7518 | 0.7546 | 0.7484 | | 0.0987 | 100.89 | 606 | 0.8742 | 0.7569 | 0.7597 | 0.7569 | 0.7524 | | 0.1063 | 101.89 | 612 | 0.8498 | 0.7755 | 0.7807 | 0.7755 | 0.7712 | | 0.1063 | 102.89 | 618 | 0.8557 | 0.7708 | 0.7749 | 0.7708 | 0.7655 | | 0.097 | 103.89 | 624 | 0.8764 | 0.7546 | 0.7634 | 0.7546 | 0.7527 | | 0.0947 | 104.89 | 630 | 0.8677 | 0.7616 | 0.7628 | 0.7616 | 0.7572 | | 0.0947 | 105.89 | 636 | 0.8909 | 0.75 | 0.7614 | 0.75 | 0.7469 | | 0.1013 | 106.89 | 642 | 0.8283 | 0.7639 | 0.7621 | 0.7639 | 0.7580 | | 0.1013 | 107.89 | 648 | 0.8471 | 0.7662 | 0.7864 | 0.7662 | 0.7651 | | 0.0963 | 108.89 | 654 | 0.8653 | 0.7593 | 0.7701 | 0.7593 | 0.7558 | | 0.0874 | 109.89 | 660 | 0.8479 | 0.7731 | 0.7834 | 0.7731 | 0.7692 | | 0.0874 | 110.89 | 666 | 0.8584 | 0.7639 | 0.7719 | 0.7639 | 0.7620 | | 0.0876 | 111.89 | 672 | 0.8714 | 0.7616 | 0.7600 | 0.7616 | 0.7550 | | 0.0876 | 112.89 | 678 | 0.8509 | 0.7731 | 0.7847 | 0.7731 | 0.7727 | | 0.0974 | 113.89 | 684 | 0.8688 | 0.7685 | 0.7741 | 0.7685 | 0.7648 | | 0.0869 | 114.89 | 690 | 0.8590 | 0.7847 | 0.7932 | 0.7847 | 0.7794 | | 0.0869 | 115.89 | 696 | 0.8687 | 0.7593 | 0.7703 | 0.7593 | 0.7579 | | 0.0877 | 116.89 | 702 | 0.8735 | 0.7593 | 0.7698 | 0.7593 | 0.7554 | | 0.0877 | 117.89 | 708 | 0.8566 | 0.7546 | 0.7732 | 0.7546 | 0.7518 | | 0.0883 | 118.89 | 714 | 0.8681 | 0.7569 | 0.7591 | 0.7569 | 0.7525 | | 0.0762 | 119.89 | 720 | 0.8508 | 0.7755 | 0.7715 | 0.7755 | 0.7676 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.11.0

[ "aishwarya_rai", "akhilesh_yadav", "ms_dhoni", "mahatma_gandhi", "mamata_banerjee", "mukesh_ambani", "narendra_modi", "rahul_gandhi", "ranveer_singh", "ratan_tata", "sachin_tendulkar", "shahrukh_khan", "akshay_kumar", "sonia_gandhi", "sundar_pichai", "virat_kohli", "yogi_adityanath", "amit_saha", "arijit_singh", "arvind_kejriwal", "baba_ramdev", "gautam_adani", "kishore_kumar", "lata_mangeshkar" ]

Devarshi/Brain_Tumor_Classification_using_swin

# Brain_Tumor_Classification_using_swin This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0123 - Accuracy: 0.9961 - F1: 0.9961 - Recall: 0.9961 - Precision: 0.9961 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| | 0.1234 | 1.0 | 180 | 0.0450 | 0.9840 | 0.9840 | 0.9840 | 0.9840 | | 0.0837 | 2.0 | 360 | 0.0198 | 0.9926 | 0.9926 | 0.9926 | 0.9926 | | 0.0373 | 3.0 | 540 | 0.0123 | 0.9961 | 0.9961 | 0.9961 | 0.9961 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.13.0 - Datasets 2.6.1 - Tokenizers 0.13.1

[ "gliomas tumor", "meningiomas tumor", "pituitary tumor" ]

jayanta/google-vit-base-patch16-224-cartoon-emotion-detection

# google-vit-base-patch16-224-cartoon-emotion-detection This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3706 - Accuracy: 0.8807 - Precision: 0.8769 - Recall: 0.8807 - F1: 0.8783 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00012 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 0.97 | 8 | 0.9902 | 0.5596 | 0.5506 | 0.5596 | 0.5360 | | 1.242 | 1.97 | 16 | 0.5157 | 0.8165 | 0.8195 | 0.8165 | 0.8132 | | 0.4438 | 2.97 | 24 | 0.3871 | 0.8440 | 0.8516 | 0.8440 | 0.8446 | | 0.1768 | 3.97 | 32 | 0.3531 | 0.8624 | 0.8653 | 0.8624 | 0.8585 | | 0.0661 | 4.97 | 40 | 0.3780 | 0.8716 | 0.8693 | 0.8716 | 0.8674 | | 0.0661 | 5.97 | 48 | 0.3747 | 0.8624 | 0.8649 | 0.8624 | 0.8632 | | 0.0375 | 6.97 | 56 | 0.3760 | 0.8991 | 0.8961 | 0.8991 | 0.8971 | | 0.0362 | 7.97 | 64 | 0.4092 | 0.8716 | 0.8684 | 0.8716 | 0.8681 | | 0.0322 | 8.97 | 72 | 0.3499 | 0.8899 | 0.8880 | 0.8899 | 0.8888 | | 0.029 | 9.97 | 80 | 0.3706 | 0.8807 | 0.8769 | 0.8807 | 0.8783 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.11.0

[ "angry", "happy", "neutral", "sad" ]

sbrandeis-test-org/autotrain-auto-retrain-190471b-3020886685

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3020886685 - CO2 Emissions (in grams): 0.5727 ## Validation Metrics - Loss: 0.002 - Accuracy: 1.000 - Macro F1: 1.000 - Micro F1: 1.000 - Weighted F1: 1.000 - Macro Precision: 1.000 - Micro Precision: 1.000 - Weighted Precision: 1.000 - Macro Recall: 1.000 - Micro Recall: 1.000 - Weighted Recall: 1.000

[ "adonis", "african giant swallowtail", "american snoot" ]

DunnBC22/vit-base-patch16-224-in21k_Human_Activity_Recognition

# vit-base-patch16-224-in21k_Human_Activity_Recognition This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k). It achieves the following results on the evaluation set: - Loss: 0.7403 - Accuracy: 0.8381 - F1 - Weighted: 0.8388 - Micro: 0.8381 - Macro: 0.8394 - Recall - Weighted: 0.8381 - Micro: 0.8381 - Macro: 0.8390 - Precision - Weighted: 0.8421 - Micro: 0.8381 - Macro: 0.8424 ## Model description This is a multiclass image classification model of humans doing different activities. For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Human%20Activity%20Recognition/ViT-Human%20Action_Recogniton.ipynb ## Intended uses & limitations This model is intended to demonstrate my ability to solve a complex problem using technology. You are welcome to test and experiment with this model, but it is at your own risk/peril. ## Training and evaluation data Dataset Source: https://www.kaggle.com/datasets/meetnagadia/human-action-recognition-har-dataset _Sample Images From Dataset:_ ![Sample Images](https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/raw/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Human%20Activity%20Recognition/Images/Sample%20Images.png) ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted F1 | Micro F1 | Macro F1 | Weighted Recall | Micro Recall | Macro Recall | Weighted Precision | Micro Precision | Macro Precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| | 1.0814 | 1.0 | 630 | 0.7368 | 0.7794 | 0.7795 | 0.7794 | 0.7798 | 0.7794 | 0.7794 | 0.7797 | 0.7896 | 0.7794 | 0.7896 | | 0.5149 | 2.0 | 1260 | 0.6439 | 0.8060 | 0.8049 | 0.8060 | 0.8036 | 0.8060 | 0.8060 | 0.8051 | 0.8136 | 0.8060 | 0.8130 | | 0.3023 | 3.0 | 1890 | 0.7026 | 0.8254 | 0.8272 | 0.8254 | 0.8278 | 0.8254 | 0.8254 | 0.8256 | 0.8335 | 0.8254 | 0.8345 | | 0.0507 | 4.0 | 2520 | 0.7414 | 0.8317 | 0.8342 | 0.8317 | 0.8348 | 0.8317 | 0.8317 | 0.8321 | 0.8427 | 0.8317 | 0.8438 | | 0.0128 | 5.0 | 3150 | 0.7403 | 0.8381 | 0.8388 | 0.8381 | 0.8394 | 0.8381 | 0.8381 | 0.8390 | 0.8421 | 0.8381 | 0.8424 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.8.0 - Tokenizers 0.12.1

[ "calling", "clapping", "running", "sitting", "sleeping", "texting", "using_laptop", "cycling", "dancing", "drinking", "eating", "fighting", "hugging", "laughing", "listening_to_music" ]

alexrods/vit_model

# vit_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0125 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1336 | 3.85 | 500 | 0.0125 | 1.0 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

[ "angular_leaf_spot", "bean_rust", "healthy" ]

agudelozc/cristian-vit

# cristian-vit This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0101 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1425 | 3.85 | 500 | 0.0101 | 1.0 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

[ "angular_leaf_spot", "bean_rust", "healthy" ]

surajjoshi/Brain_Tumor_Classification_using_swin_transformer

# Brain_Tumor_Classification_using_swin_transformer This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0118 - Accuracy: 0.9949 - F1: 0.9949 - Recall: 0.9949 - Precision: 0.9949 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| | 0.081 | 1.0 | 180 | 0.0557 | 0.9832 | 0.9832 | 0.9832 | 0.9832 | | 0.0816 | 2.0 | 360 | 0.0187 | 0.9937 | 0.9937 | 0.9937 | 0.9937 | | 0.0543 | 3.0 | 540 | 0.0118 | 0.9949 | 0.9949 | 0.9949 | 0.9949 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.13.0 - Datasets 2.6.1 - Tokenizers 0.13.1

[ "gliomas tumor", "meningiomas tumor", "pituitary tumor" ]

Prachi1234/corn_leaf_detector

# corn_leaf_detector This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1631 - Accuracy: 0.9154 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.903 | 0.96 | 20 | 3.7614 | 0.8792 | | 3.5402 | 1.96 | 40 | 3.4920 | 0.9063 | | 3.348 | 2.96 | 60 | 3.3117 | 0.9154 | | 3.1824 | 3.96 | 80 | 3.2024 | 0.9154 | | 3.1366 | 4.96 | 100 | 3.1631 | 0.9154 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

[ "3", "3", "3", "3", "3", "3", "2", "0", "3", "3", "3", "3", "3", "3", "3", "3", "3", "0", "3", "0", "3", "1", "3", "3", "3", "3", "3", "2", "3", "3", "3", "3", "2", "3", "3", "3", "3", "3", "3", "0", "2", "1", "3", "3", "0", "3", "0", "3", "3", "3", "3", "3", "3", "3", "3", "2", "3", "3", "3", "3", "3", "3", "3", "3", "3", "0", "3", "3", "3", "3", "3", "0", "3", "3", "3", "3", "2", "3", "0", "3", "3", "0", "3", "3", "3", "3", "0", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "1", "3", "3", "3", "3", "0", "0", "3", "3", "3", "3", "3", "0", "3", "2", "3", "3", "3", "3", "0", "3", "3", "3", "3", "0", "2", "0", "3", "3", "2", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "1", "2", "3", "3", "3", "3", "2", "3", "3", "0", "3", "2", "3", "2", "0", "2", "0", "3", "3", "3", "2", "1", "3", "3", "1", "3", "0", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "2", "0", "3", "0", "3", "3", "2", "1", "3", "3", "3", "3", "3", "3", "3", "3", "3", "0", "0", "3", "3", "0", "3", 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Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05-16

# beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05-16 This model is a fine-tuned version of [Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05](https://huggingface.co/Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.8185 - Accuracy: 0.7154 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7923 | 1.0 | 224 | 0.8570 | 0.7009 | | 0.6737 | 2.0 | 448 | 0.8185 | 0.7154 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]

Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05-32

# beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05-32 This model is a fine-tuned version of [Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05](https://huggingface.co/Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.8037 - Accuracy: 0.7201 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.8058 | 1.0 | 112 | 0.8260 | 0.7056 | | 0.6999 | 2.0 | 224 | 0.8037 | 0.7201 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]

Celal11/resnet-50-finetuned-FER2013-0.001

# resnet-50-finetuned-FER2013-0.001 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.9002 - Accuracy: 0.6847 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.4723 | 1.0 | 224 | 1.3382 | 0.4887 | | 1.2236 | 2.0 | 448 | 1.1090 | 0.5751 | | 1.1728 | 3.0 | 672 | 1.0262 | 0.6158 | | 1.1545 | 4.0 | 896 | 0.9717 | 0.6339 | | 1.0776 | 5.0 | 1120 | 0.9885 | 0.6360 | | 1.0183 | 6.0 | 1344 | 0.9475 | 0.6560 | | 0.9856 | 7.0 | 1568 | 0.9114 | 0.6700 | | 0.953 | 8.0 | 1792 | 0.9074 | 0.6767 | | 0.9151 | 9.0 | 2016 | 0.9076 | 0.6833 | | 0.9355 | 10.0 | 2240 | 0.9002 | 0.6847 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]

Celal11/resnet-50-finetuned-FER2013-0.003

# resnet-50-finetuned-FER2013-0.003 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.9036 - Accuracy: 0.6971 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.4393 | 1.0 | 224 | 1.2746 | 0.5173 | | 1.2564 | 2.0 | 448 | 1.1456 | 0.5542 | | 1.218 | 3.0 | 672 | 1.1102 | 0.5816 | | 1.1919 | 4.0 | 896 | 1.0255 | 0.6151 | | 1.1222 | 5.0 | 1120 | 1.0257 | 0.6167 | | 1.0925 | 6.0 | 1344 | 0.9676 | 0.6317 | | 1.0241 | 7.0 | 1568 | 0.9406 | 0.6510 | | 1.0015 | 8.0 | 1792 | 0.9465 | 0.6532 | | 0.987 | 9.0 | 2016 | 0.9002 | 0.6748 | | 0.9768 | 10.0 | 2240 | 0.9086 | 0.6737 | | 0.9408 | 11.0 | 2464 | 0.8975 | 0.6793 | | 0.8907 | 12.0 | 2688 | 0.8966 | 0.6769 | | 0.8051 | 13.0 | 2912 | 0.9142 | 0.6826 | | 0.8169 | 14.0 | 3136 | 0.9082 | 0.6870 | | 0.7729 | 15.0 | 3360 | 0.9036 | 0.6971 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]

Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05-finetuned-FER2013-7e-05

# beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05-finetuned-FER2013-7e-05 This model is a fine-tuned version of [Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05](https://huggingface.co/Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.9121 - Accuracy: 0.7116 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4564 | 1.0 | 224 | 0.9463 | 0.7014 | | 0.6463 | 2.0 | 448 | 0.9121 | 0.7116 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]

Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05-4-64

# beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05-4-64 This model is a fine-tuned version of [Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05](https://huggingface.co/Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.8708 - Accuracy: 0.7201 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5721 | 1.0 | 112 | 0.9867 | 0.7052 | | 0.6816 | 2.0 | 224 | 0.9131 | 0.7062 | | 0.6785 | 3.0 | 336 | 0.8935 | 0.7091 | | 0.6251 | 4.0 | 448 | 0.8708 | 0.7201 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]

uisikdag/vit-base-patch16-224-in21k-fog-or-smog-classification

# fogsmog_hfclass This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3700 - Accuracy: 0.91 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6766 | 1.0 | 25 | 0.6299 | 0.795 | | 0.3444 | 2.0 | 50 | 0.3701 | 0.8625 | | 0.2456 | 3.0 | 75 | 0.2988 | 0.885 | | 0.1402 | 4.0 | 100 | 0.3076 | 0.905 | | 0.1275 | 5.0 | 125 | 0.4505 | 0.8525 | | 0.0909 | 6.0 | 150 | 0.3739 | 0.8825 | | 0.0792 | 7.0 | 175 | 0.3642 | 0.885 | | 0.0482 | 8.0 | 200 | 0.3812 | 0.885 | | 0.0451 | 9.0 | 225 | 0.3290 | 0.9 | | 0.0526 | 10.0 | 250 | 0.4004 | 0.8825 | | 0.0575 | 11.0 | 275 | 0.2842 | 0.925 | | 0.0457 | 12.0 | 300 | 0.3952 | 0.895 | | 0.0505 | 13.0 | 325 | 0.4411 | 0.885 | | 0.0324 | 14.0 | 350 | 0.4185 | 0.8925 | | 0.0354 | 15.0 | 375 | 0.3347 | 0.9025 | | 0.0443 | 16.0 | 400 | 0.2949 | 0.915 | | 0.0305 | 17.0 | 425 | 0.3603 | 0.905 | | 0.0234 | 18.0 | 450 | 0.3858 | 0.8875 | | 0.0219 | 19.0 | 475 | 0.3541 | 0.91 | | 0.0284 | 20.0 | 500 | 0.3700 | 0.91 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "fog", "smog" ]

torresflo/Poke-Model

# Poké Model Poké Model is a Pokémon Classifier created to be used with [Pokédex AI](https://github.com/torresflo/Pokedex-AI). It is a fine-tuned model of google/vit-base-patch16-224 to classify Pokémon of the first generation. More information on how to generate and how to use the model can be found on this [dedicated repository](https://github.com/torresflo/Poke-Model). ## License Distributed under the GNU General Public License v3.0. See [here](https://www.gnu.org/licenses/gpl-3.0.en.html) for more information.

[ "bulbasaur", "ivysaur", "venusaur", "charmander", "charmeleon", "charizard", "squirtle", "wartortle", "blastoise", "caterpie", "metapod", "butterfree", "weedle", "kakuna", "beedrill", "pidgey", "pidgeotto", "pidgeot", "rattata", "raticate", "spearow", "fearow", "ekans", "arbok", "pikachu", "raichu", "sandshrew", "sandslash", "nidoran-f", "nidorina", "nidoqueen", "nidoran-m", "nidorino", "nidoking", "clefairy", "clefable", "vulpix", "ninetales", "jigglypuff", "wigglytuff", "zubat", "golbat", "oddish", "gloom", "vileplume", "paras", "parasect", "venonat", "venomoth", "diglett", "dugtrio", "meowth", "persian", "psyduck", "golduck", "mankey", "primeape", "growlithe", "arcanine", "poliwag", "poliwhirl", "poliwrath", "abra", "kadabra", "alakazam", "machop", "machoke", "machamp", "bellsprout", "weepinbell", "victreebel", "tentacool", "tentacruel", "geodude", "graveler", "golem", "ponyta", "rapidash", "slowpoke", "slowbro", "magnemite", "magneton", "farfetchd", "doduo", "dodrio", "seel", "dewgong", "grimer", "muk", "shellder", "cloyster", "gastly", "haunter", "gengar", "onix", "drowzee", "hypno", "krabby", "kingler", "voltorb", "electrode", "exeggcute", "exeggutor", "cubone", "marowak", "hitmonlee", "hitmonchan", "lickitung", "koffing", "weezing", "rhyhorn", "rhydon", "chansey", "tangela", "kangaskhan", "horsea", "seadra", "goldeen", "seaking", "staryu", "starmie", "mr-mime", "scyther", "jynx", "electabuzz", "magmar", "pinsir", "tauros", "magikarp", "gyarados", "lapras", "ditto", "eevee", "vaporeon", "jolteon", "flareon", "porygon", "omanyte", "omastar", "kabuto", "kabutops", "aerodactyl", "snorlax", "articuno", "zapdos", "moltres", "dratini", "dragonair", "dragonite", "mewtwo", "mew" ]

swordsman1/swin-tiny-patch4-window7-224-finetuned-eurosat

# swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0583 - Accuracy: 0.9822 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2655 | 1.0 | 190 | 0.1039 | 0.9707 | | 0.1519 | 2.0 | 380 | 0.0866 | 0.9715 | | 0.1402 | 3.0 | 570 | 0.0583 | 0.9822 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "annualcrop", "forest", "herbaceousvegetation", "highway", "industrial", "pasture", "permanentcrop", "residential", "river", "sealake" ]

Lloviant/autotrain-ex-and-pt-3122688386

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3122688386 - CO2 Emissions (in grams): 0.6203 ## Validation Metrics - Loss: 1.338 - Accuracy: 0.571 - Macro F1: 0.389 - Micro F1: 0.571 - Weighted F1: 0.429 - Macro Precision: 0.333 - Micro Precision: 0.571 - Weighted Precision: 0.357 - Macro Recall: 0.500 - Micro Recall: 0.571 - Weighted Recall: 0.571

[ "ex and pt", "ex and pt logo", "ex and pt mutant", "ex and pt mutants", "ex and pt tcg", "vagitron" ]

Lloviant/autotrain-ex-and-pt-3122688387

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3122688387 - CO2 Emissions (in grams): 0.5722 ## Validation Metrics - Loss: 1.749 - Accuracy: 0.571 - Macro F1: 0.444 - Micro F1: 0.571 - Weighted F1: 0.476 - Macro Precision: 0.417 - Micro Precision: 0.571 - Weighted Precision: 0.429 - Macro Recall: 0.500 - Micro Recall: 0.571 - Weighted Recall: 0.571

[ "ex and pt", "ex and pt logo", "ex and pt mutant", "ex and pt mutants", "ex and pt tcg", "vagitron" ]

Lloviant/autotrain-ex-and-pt-3122688388

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3122688388 - CO2 Emissions (in grams): 0.2158 ## Validation Metrics - Loss: 1.818 - Accuracy: 0.000 - Macro F1: 0.000 - Micro F1: 0.000 - Weighted F1: 0.000 - Macro Precision: 0.000 - Micro Precision: 0.000 - Weighted Precision: 0.000 - Macro Recall: 0.000 - Micro Recall: 0.000 - Weighted Recall: 0.000

[ "ex and pt", "ex and pt logo", "ex and pt mutant", "ex and pt mutants", "ex and pt tcg", "vagitron" ]

Lloviant/autotrain-ex-and-pt-3122688389

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3122688389 - CO2 Emissions (in grams): 0.7206 ## Validation Metrics - Loss: 1.599 - Accuracy: 0.286 - Macro F1: 0.250 - Micro F1: 0.286 - Weighted F1: 0.286 - Macro Precision: 0.250 - Micro Precision: 0.286 - Weighted Precision: 0.286 - Macro Recall: 0.250 - Micro Recall: 0.286 - Weighted Recall: 0.286

[ "ex and pt", "ex and pt logo", "ex and pt mutant", "ex and pt mutants", "ex and pt tcg", "vagitron" ]

Lloviant/autotrain-ex-and-pt-3122688390

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3122688390 - CO2 Emissions (in grams): 0.4229 ## Validation Metrics - Loss: 1.919 - Accuracy: 0.286 - Macro F1: 0.214 - Micro F1: 0.286 - Weighted F1: 0.184 - Macro Precision: 0.194 - Micro Precision: 0.286 - Weighted Precision: 0.167 - Macro Recall: 0.333 - Micro Recall: 0.286 - Weighted Recall: 0.286

[ "ex and pt", "ex and pt logo", "ex and pt mutant", "ex and pt mutants", "ex and pt tcg", "vagitron" ]

tadeous/vit-model-beimer

# vit-model-beimer This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0637 - Accuracy: 0.9850 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1394 | 3.85 | 500 | 0.0637 | 0.9850 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "angular_leaf_spot", "bean_rust", "healthy" ]

SaintGermain/is-this-furry

This detects furry images, mostly profile pictures, although it may be able detect any sort of furry picture (I haven't tried it, though). # Dataset Info This was trained on scraped pfp images from Mastodon, with some non-pfp images thrown in for "balancing" (i.e ensuring pokemon, kemonomimi (catgirls/foxgirls/etc), and normal animals weren't classified as 'furry') **Furry images**: 551 **Non-furry images**: 641 # Disclaimer Please do not ruin this by using this to harass anyone. This is *not* intended to be used for targeted harrassement, and I will explicitly condemn any use that attempts to do so. If you're wondering why I made this public in the first place? I believe in freedom of *information* - this image classification model has various perfectly valid uses, and it's kinda useless to keep it private. # Statistics ## Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 2890884434 - CO2 Emissions (in grams): 2.8752 ## Validation Metrics - Loss: 0.175 - Accuracy: 0.933 - Precision: 0.938 - Recall: 0.938 - AUC: 0.975 - F1: 0.938

[ "bad", "good" ]

lixiqi/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05-finetuned-SFEW-7e-05

# beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05-finetuned-SFEW-7e-05 This model is a fine-tuned version of [Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05](https://huggingface.co/Celal11/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 1.5629 - Accuracy: 0.4960 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.1509 | 0.97 | 14 | 1.6920 | 0.3725 | | 1.6764 | 1.97 | 28 | 1.5035 | 0.4694 | | 1.2723 | 2.97 | 42 | 1.5061 | 0.4694 | | 1.1746 | 3.97 | 56 | 1.5421 | 0.4729 | | 0.9954 | 4.97 | 70 | 1.5657 | 0.4787 | | 1.0029 | 5.97 | 84 | 1.5867 | 0.4844 | | 0.9139 | 6.97 | 98 | 1.5943 | 0.4879 | | 0.8335 | 7.97 | 112 | 1.6003 | 0.4890 | | 0.8382 | 8.97 | 126 | 1.5629 | 0.4960 | | 0.7169 | 9.97 | 140 | 1.5772 | 0.4856 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]

learnsolana/autotrain-chest-xray-demo-3129688461

# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3129688461 - CO2 Emissions (in grams): 8.0323 ## Validation Metrics - Loss: 0.445 - Accuracy: 0.774 - Precision: 0.738 - Recall: 0.990 - AUC: 0.920 - F1: 0.846

[ "normal", "pneumonia" ]

Kaludi/Food-Classification

# Food Classification This is a Food Image Classifier model that has been trained by [Kaludi](https://huggingface.co/Kaludi) to recognize 7 different types of popular foods, including **apple pie**, **falafel**, **french toast**, **ice cream**, **ramen**, **sushi**, and **tiramisu**. It can accurately classify an image of food into one of these categories by analyzing its visual features. This model can be used by food bloggers, restaurants, and recipe websites to quickly categorize and sort their food images, making it easier to manage their content and provide a better user experience. ### Gradio Tis model supports a [Gradio](https://github.com/gradio-app/gradio) Web UI to run the data-food-classification model: [![Open In HF Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/Kaludi/Food-Classification_App) ## Validation Metrics - Loss: 0.094 - Accuracy: 0.977 - Macro F1: 0.977 - Micro F1: 0.977 - Weighted F1: 0.977 - Macro Precision: 0.978 - Micro Precision: 0.977 - Weighted Precision: 0.978 - Macro Recall: 0.977 - Micro Recall: 0.977 - Weighted Recall: 0.977

[ "apple_pie", "falafel", "french_toast", "ice_cream", "ramen", "sushi", "tiramisu" ]

Gokulapriyan/swin-tiny-patch4-window7-224-finetuned-eurosat

# swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0684 - Accuracy: 0.9742 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4378 | 1.0 | 527 | 0.3668 | 0.8507 | | 0.3133 | 2.0 | 1054 | 0.1586 | 0.9430 | | 0.2065 | 3.0 | 1581 | 0.1049 | 0.9607 | | 0.22 | 4.0 | 2108 | 0.0838 | 0.9701 | | 0.173 | 5.0 | 2635 | 0.0684 | 0.9742 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "fnh_frames", "hcc_frames", "hhe_frames", "healthy_frames" ]

StatsGary/VIT-food101-image-classifier

# VIT-food101-image-classifier This model was trained from scratch on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 0.5661 - Accuracy: 0.933 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1716 | 0.99 | 62 | 1.2149 | 0.896 | | 0.7758 | 1.99 | 124 | 0.8727 | 0.906 | | 0.6269 | 2.99 | 186 | 0.6833 | 0.928 | | 0.5495 | 3.99 | 248 | 0.6041 | 0.931 | | 0.4973 | 4.99 | 310 | 0.5661 | 0.933 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

[ "apple_pie", "baby_back_ribs", "baklava", "beef_carpaccio", "beef_tartare", "beet_salad", "beignets", "bibimbap", "bread_pudding", "breakfast_burrito", "bruschetta", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare", "waffles" ]

Thabet/swin-tiny-patch4-window7-224-finetuned-aiornot-baseline

# swin-tiny-patch4-window7-224-finetuned-aiornot-baseline This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2231 - Accuracy: 0.9119 - F1: 0.9086 - Log Loss: 3.0422 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Log Loss | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:| | 0.2538 | 0.98 | 32 | 0.2327 | 0.9055 | 0.9018 | 3.2647 | | 0.1735 | 1.98 | 64 | 0.2029 | 0.9151 | 0.9122 | 2.9309 | | 0.1562 | 2.98 | 96 | 0.2231 | 0.9119 | 0.9086 | 3.0422 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

[ "no_ai", "ai" ]

platzi/platzi-vit-model-orlando-murcia

# platzi-vit-model-orlando-murcia This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0532 - Accuracy: 0.9850 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0776 | 3.85 | 500 | 0.0532 | 0.9850 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "angular_leaf_spot", "bean_rust", "healthy" ]

Celal11/resnet-50-finetuned-FER2013CKPlus-0.003

# resnet-50-finetuned-FER2013CKPlus-0.003 This model is a fine-tuned version of [Celal11/resnet-50-finetuned-FER2013-0.003](https://huggingface.co/Celal11/resnet-50-finetuned-FER2013-0.003) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.0073 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.8084 | 0.97 | 27 | 0.2004 | 0.9289 | | 0.362 | 1.97 | 54 | 0.0828 | 0.9848 | | 0.2972 | 2.97 | 81 | 0.0185 | 0.9949 | | 0.1917 | 3.97 | 108 | 0.0132 | 1.0 | | 0.1572 | 4.97 | 135 | 0.0073 | 1.0 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]

Celal11/resnet-50-finetuned-FER2013-0.003-CKPlus

# resnet-50-finetuned-FER2013-0.003-CKPlus This model is a fine-tuned version of [Celal11/resnet-50-finetuned-FER2013-0.003](https://huggingface.co/Celal11/resnet-50-finetuned-FER2013-0.003) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.0614 - Accuracy: 0.9848 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6689 | 0.97 | 27 | 0.1123 | 0.9797 | | 0.2929 | 1.97 | 54 | 0.0614 | 0.9848 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]

ashutoshmondal/pneumo_v3

# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3177689678 - CO2 Emissions (in grams): 1.9594 ## Validation Metrics - Loss: 0.017 - Accuracy: 1.000 - Precision: 1.000 - Recall: 1.000 - AUC: 1.000 - F1: 1.000

[ "normal", "pneumonia" ]

ashutoshmondal/autotrain-pneumo-v3-3180589690

# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3180589690 - CO2 Emissions (in grams): 3.4021 ## Validation Metrics - Loss: 0.131 - Accuracy: 0.964 - Precision: 0.964 - Recall: 0.964 - AUC: 0.994 - F1: 0.964

[ "normal", "pneumonia" ]

Kanr1u/rose_charlotte

# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3206689984 - CO2 Emissions (in grams): 2.1410 ## Validation Metrics - Loss: 0.303 - Accuracy: 0.846 - Precision: 0.846 - Recall: 0.846 - AUC: 0.929 - F1: 0.846

[ "emma roberts", "emma watson" ]

anaghasavit/swin-tiny-patch4-window7-224-finetuned-eurosat

# swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0071 - Accuracy: 0.9980 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0209 | 1.0 | 426 | 0.0071 | 0.9980 | ### Framework versions - Transformers 4.27.4 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.2

[ "dot", "horizontal_bar", "line", "scatter", "vertical_bar" ]

Kaludi/csgo-weapon-classification

# CSGO Weapon Classification This is a CSGO Weapon Classifier Model that has been trained by [Kaludi](https://huggingface.co/Kaludi) to recognize **11** different types of Counter-Strike: Global Offensive (CSGO) Weapons, which include **AK-47,AWP,Famas,Galil-AR,Glock,M4A1,M4A4,P-90,SG-553,UMP,USP**. The model is capable of accurately classifying the weapon name present in an image. With its deep understanding of the characteristics of each weapon in the game, the model is a valuable tool for players and fans of CSGO. ### Gradio Tis model supports a [Gradio](https://github.com/gradio-app/gradio) Web UI to run the csgo-weapon-classification model: [![Open In HF Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/Kaludi/CSGO-Weapon-Classification_App) ## Validation Metrics - Loss: 0.282 - Accuracy: 0.945 - Macro F1: 0.946 - Micro F1: 0.945 - Weighted F1: 0.946 - Macro Precision: 0.948 - Micro Precision: 0.945 - Weighted Precision: 0.948 - Macro Recall: 0.945 - Micro Recall: 0.945 - Weighted Recall: 0.945

[ "ak-47", "awp", "usp", "famas", "galil-ar", "glock", "m4a1", "m4a4", "p-90", "sg-553", "ump" ]

Kaludi/food-category-classification

# Food Category Classification This is a Food Category Image Classifier model that has been trained by [Kaludi](https://huggingface.co/Kaludi) to recognize 11 different categories of foods, including **Bread**, **Dairy Product**, **Dessert**, **Egg**, **Fried Food**, **Meat**, **Noodles-Pasta**, **Rice**, **Seafood**, **Soup**, and **Vegetable-Fruit**. It can accurately classify an image of food into one of these categories by analyzing its visual features. This model can be used by food bloggers, restaurants, and recipe websites to quickly categorize and sort their food images, making it easier to manage their content and provide a better user experience. ### Gradio Tis model supports a [Gradio](https://github.com/gradio-app/gradio) Web UI to run the data-food-classification model: [![Open In HF Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/Kaludi/Food-Category-Classification_App) ## Validation Metrics - Loss: 0.079 - Accuracy: 0.978 - Macro F1: 0.978 - Micro F1: 0.978 - Weighted F1: 0.978 - Macro Precision: 0.979 - Micro Precision: 0.978 - Weighted Precision: 0.979 - Macro Recall: 0.978 - Micro Recall: 0.978 - Weighted Recall: 0.978

[ "bread", "dairy product", "vegetable-fruit", "dessert", "egg", "fried food", "meat", "noodles-pasta", "rice", "seafood", "soup" ]

NehaBardeDUKE/autotrain-ai-generated-image-classification-3250490787

# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3250490787 - CO2 Emissions (in grams): 0.0106 ## Validation Metrics - Loss: 0.217 - Accuracy: 0.941 - Precision: 0.929 - Recall: 1.000 - AUC: 1.000 - F1: 0.963

[ "artificial", "human" ]

Ailyth/2_Labels

# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3268491180 - CO2 Emissions (in grams): 2.0388 ## Validation Metrics - Loss: 0.044 - Accuracy: 0.970 - Precision: 0.966 - Recall: 0.982 - AUC: 0.998 - F1: 0.974

[ "cuisine", "zhazu" ]

adielsa/swin-tiny-patch4-window7-224-finetuned-eurosat

# swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1627 - Accuracy: 0.9464 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2486 | 0.98 | 36 | 0.2120 | 0.9100 | | 0.1844 | 1.98 | 72 | 0.3417 | 0.8563 | | 0.1646 | 2.98 | 108 | 0.1627 | 0.9464 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "normal", "pneumonia" ]

adielsa/vit-base-patch16-224-finetuned-chest

# vit-base-patch16-224-finetuned-chest This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0318 - Accuracy: 0.9900 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0947 | 0.98 | 36 | 0.0785 | 0.9732 | | 0.048 | 1.98 | 72 | 0.0678 | 0.9732 | | 0.0352 | 2.98 | 108 | 0.0329 | 0.9887 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "normal", "pneumonia" ]

hamdan07/UltraSound-Lung

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3310291874 - CO2 Emissions (in grams): 1.3971 ## Validation Metrics - Loss: 0.001 - Accuracy: 1.000 - Macro F1: 1.000 - Micro F1: 1.000 - Weighted F1: 1.000 - Macro Precision: 1.000 - Micro Precision: 1.000 - Weighted Precision: 1.000 - Macro Recall: 1.000 - Micro Recall: 1.000 - Weighted Recall: 1.000

[ "covid", "pneumonia", "regular" ]

sayakpaul/vit-base-patch16-224-in21k-finetuned-lora-food101

# vit-base-patch16-224-in21k-finetuned-lora-food101 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 0.1448 - Accuracy: 0.96 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 9 | 0.5069 | 0.896 | | 2.1627 | 2.0 | 18 | 0.1891 | 0.946 | | 0.3451 | 3.0 | 27 | 0.1448 | 0.96 | | 0.2116 | 4.0 | 36 | 0.1509 | 0.958 | | 0.1711 | 5.0 | 45 | 0.1498 | 0.958 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "apple_pie", "baby_back_ribs", "baklava", "beef_carpaccio", "beef_tartare", "beet_salad", "beignets", "bibimbap", "bread_pudding", "breakfast_burrito", "bruschetta", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare", "waffles" ]

anaghasavit/beit-base-patch16-224-pt22k-ft22k-finetunedt

# beit-base-patch16-224-pt22k-ft22k-finetunedt This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0147 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4714 | 1.0 | 25 | 0.0147 | 1.0 | | 0.0089 | 2.0 | 50 | 0.0008 | 1.0 | | 0.0101 | 3.0 | 75 | 0.0003 | 1.0 | | 0.0021 | 4.0 | 100 | 0.0002 | 1.0 | | 0.0028 | 5.0 | 125 | 0.0001 | 1.0 | | 0.0016 | 6.0 | 150 | 0.0001 | 1.0 | | 0.0044 | 7.0 | 175 | 0.0001 | 1.0 | | 0.0007 | 8.0 | 200 | 0.0001 | 1.0 | | 0.0013 | 9.0 | 225 | 0.0001 | 1.0 | | 0.0004 | 10.0 | 250 | 0.0001 | 1.0 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "apple", "banana", "onion", "orange", "package", "potato", "watermelon" ]

davanstrien/autotrain-encyclopaedia-illustrations-blog-post-3327992158

# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3327992158 - CO2 Emissions (in grams): 13.4530 ## Validation Metrics - Loss: 0.025 - Accuracy: 0.992 - Precision: 0.998 - Recall: 0.994 - AUC: 0.998 - F1: 0.996

[ "illustrated", "not-illustrated" ]

davanstrien/autotrain-encyclopaedia-illustrations-blog-post-3327992159

# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3327992159 - CO2 Emissions (in grams): 4.6084 ## Validation Metrics - Loss: 0.040 - Accuracy: 0.992 - Precision: 0.994 - Recall: 0.998 - AUC: 0.993 - F1: 0.996

[ "illustrated", "not-illustrated" ]

muhammaddjunas/cvt-13-finetuned-waste

# cvt-13-finetuned-waste This model is a fine-tuned version of [microsoft/cvt-13](https://huggingface.co/microsoft/cvt-13) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1715 | 0.99 | 117 | 0.0000 | 1.0 | | 0.1194 | 1.99 | 234 | 0.0000 | 1.0 | | 0.1496 | 2.99 | 351 | 0.0000 | 1.0 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "tench, tinca tinca", "goldfish, carassius auratus", "great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias", "tiger shark, galeocerdo cuvieri", "hammerhead, hammerhead shark", "electric ray, crampfish, numbfish, torpedo", "stingray", "cock", "hen", "ostrich, struthio camelus", "brambling, fringilla montifringilla", "goldfinch, carduelis carduelis", "house finch, linnet, carpodacus mexicanus", "junco, snowbird", "indigo bunting, indigo finch, indigo bird, passerina cyanea", "robin, american robin, turdus migratorius", "bulbul", "jay", "magpie", "chickadee", "water ouzel, dipper", "kite", "bald eagle, american eagle, haliaeetus leucocephalus", "vulture", "great grey owl, great gray owl, strix nebulosa", "european fire salamander, salamandra salamandra", "common newt, triturus vulgaris", "eft", "spotted salamander, ambystoma maculatum", "axolotl, mud puppy, ambystoma mexicanum", "bullfrog, rana catesbeiana", "tree frog, tree-frog", "tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui", "loggerhead, loggerhead turtle, caretta caretta", "leatherback turtle, leatherback, leathery turtle, dermochelys coriacea", "mud turtle", "terrapin", "box turtle, box tortoise", "banded gecko", "common iguana, iguana, iguana iguana", "american chameleon, anole, anolis carolinensis", "whiptail, whiptail lizard", "agama", "frilled lizard, chlamydosaurus kingi", "alligator lizard", "gila monster, heloderma suspectum", "green lizard, lacerta viridis", "african chameleon, chamaeleo chamaeleon", "komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis", "african crocodile, nile crocodile, crocodylus niloticus", "american alligator, alligator mississipiensis", "triceratops", "thunder snake, worm snake, carphophis amoenus", "ringneck snake, ring-necked snake, ring snake", "hognose snake, puff adder, sand viper", "green snake, grass snake", "king snake, kingsnake", "garter snake, grass snake", "water snake", "vine snake", "night snake, hypsiglena torquata", "boa constrictor, constrictor constrictor", "rock python, rock snake, python sebae", "indian cobra, naja naja", "green mamba", "sea snake", "horned viper, cerastes, sand viper, horned asp, cerastes cornutus", "diamondback, diamondback rattlesnake, crotalus adamanteus", "sidewinder, horned rattlesnake, crotalus cerastes", "trilobite", "harvestman, daddy longlegs, phalangium opilio", "scorpion", "black and gold garden spider, argiope aurantia", "barn spider, araneus cavaticus", "garden spider, aranea diademata", "black widow, latrodectus mactans", "tarantula", "wolf spider, hunting spider", "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse, partridge, bonasa umbellus", "prairie chicken, prairie grouse, prairie fowl", "peacock", "quail", "partridge", "african grey, african gray, psittacus erithacus", "macaw", "sulphur-crested cockatoo, kakatoe galerita, cacatua galerita", "lorikeet", "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "drake", "red-breasted merganser, mergus serrator", "goose", "black swan, cygnus atratus", "tusker", "echidna, spiny anteater, anteater", "platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus", "wallaby, brush kangaroo", "koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus", "wombat", "jellyfish", "sea anemone, anemone", "brain coral", "flatworm, platyhelminth", "nematode, nematode worm, roundworm", "conch", "snail", "slug", "sea slug, nudibranch", "chiton, coat-of-mail shell, sea cradle, polyplacophore", "chambered nautilus, pearly nautilus, nautilus", "dungeness crab, cancer magister", "rock crab, cancer irroratus", "fiddler crab", "king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica", "american lobster, northern lobster, maine lobster, homarus americanus", "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "crayfish, crawfish, crawdad, crawdaddy", "hermit crab", "isopod", "white stork, ciconia ciconia", "black stork, ciconia nigra", "spoonbill", "flamingo", "little blue heron, egretta caerulea", "american egret, great white heron, egretta albus", "bittern", "crane", "limpkin, aramus pictus", "european gallinule, porphyrio porphyrio", "american coot, marsh hen, mud hen, water hen, fulica americana", "bustard", "ruddy turnstone, arenaria interpres", "red-backed sandpiper, dunlin, erolia alpina", "redshank, tringa totanus", "dowitcher", "oystercatcher, oyster catcher", "pelican", "king penguin, aptenodytes patagonica", "albatross, mollymawk", "grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus", "killer whale, killer, orca, grampus, sea wolf, orcinus orca", "dugong, dugong dugon", "sea lion", "chihuahua", "japanese spaniel", "maltese dog, maltese terrier, maltese", "pekinese, pekingese, peke", "shih-tzu", "blenheim spaniel", "papillon", "toy terrier", "rhodesian ridgeback", "afghan hound, afghan", "basset, basset hound", "beagle", "bloodhound, sleuthhound", "bluetick", "black-and-tan coonhound", "walker hound, walker foxhound", "english foxhound", "redbone", "borzoi, russian wolfhound", "irish wolfhound", "italian greyhound", "whippet", "ibizan hound, ibizan podenco", "norwegian elkhound, elkhound", "otterhound, otter hound", "saluki, gazelle hound", "scottish deerhound, deerhound", "weimaraner", "staffordshire bullterrier, staffordshire bull terrier", "american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier", "bedlington terrier", "border terrier", "kerry blue terrier", "irish terrier", "norfolk terrier", "norwich terrier", "yorkshire terrier", "wire-haired fox terrier", "lakeland terrier", "sealyham terrier, sealyham", "airedale, airedale terrier", "cairn, cairn terrier", "australian terrier", "dandie dinmont, dandie dinmont terrier", "boston bull, boston terrier", "miniature schnauzer", "giant schnauzer", "standard schnauzer", "scotch terrier, scottish terrier, scottie", "tibetan terrier, chrysanthemum dog", "silky terrier, sydney silky", "soft-coated wheaten terrier", "west highland white terrier", "lhasa, lhasa apso", "flat-coated retriever", "curly-coated retriever", "golden retriever", "labrador retriever", "chesapeake bay retriever", "german short-haired pointer", "vizsla, hungarian pointer", "english setter", "irish setter, red setter", "gordon setter", "brittany spaniel", "clumber, clumber spaniel", "english springer, english springer spaniel", "welsh springer spaniel", "cocker spaniel, english cocker spaniel, cocker", "sussex spaniel", "irish water spaniel", "kuvasz", "schipperke", "groenendael", "malinois", "briard", "kelpie", "komondor", "old english sheepdog, bobtail", "shetland sheepdog, shetland sheep dog, shetland", "collie", "border collie", "bouvier des flandres, bouviers des flandres", "rottweiler", "german shepherd, german shepherd dog, german police dog, alsatian", "doberman, doberman pinscher", "miniature pinscher", "greater swiss mountain dog", "bernese mountain dog", "appenzeller", "entlebucher", "boxer", "bull mastiff", "tibetan mastiff", "french bulldog", "great dane", "saint bernard, st bernard", "eskimo dog, husky", "malamute, malemute, alaskan malamute", "siberian husky", "dalmatian, coach dog, carriage dog", "affenpinscher, monkey pinscher, monkey dog", "basenji", "pug, pug-dog", "leonberg", "newfoundland, newfoundland dog", "great pyrenees", "samoyed, samoyede", "pomeranian", "chow, chow chow", "keeshond", "brabancon griffon", "pembroke, pembroke welsh corgi", "cardigan, cardigan welsh corgi", "toy poodle", "miniature poodle", "standard poodle", "mexican hairless", "timber wolf, grey wolf, gray wolf, canis lupus", "white wolf, arctic wolf, canis lupus tundrarum", "red wolf, maned wolf, canis rufus, canis niger", "coyote, prairie wolf, brush wolf, canis latrans", "dingo, warrigal, warragal, canis dingo", "dhole, cuon alpinus", "african hunting dog, hyena dog, cape hunting dog, lycaon pictus", "hyena, hyaena", "red fox, vulpes vulpes", "kit fox, vulpes macrotis", "arctic fox, white fox, alopex lagopus", "grey fox, gray fox, urocyon cinereoargenteus", "tabby, tabby cat", "tiger cat", "persian cat", "siamese cat, siamese", "egyptian cat", "cougar, puma, catamount, mountain lion, painter, panther, felis concolor", "lynx, catamount", "leopard, panthera pardus", "snow leopard, ounce, panthera uncia", "jaguar, panther, panthera onca, felis onca", "lion, king of beasts, panthera leo", "tiger, panthera tigris", "cheetah, chetah, acinonyx jubatus", "brown bear, bruin, ursus arctos", "american black bear, black bear, ursus americanus, euarctos americanus", "ice bear, polar bear, ursus maritimus, thalarctos maritimus", "sloth bear, melursus ursinus, ursus ursinus", "mongoose", "meerkat, mierkat", "tiger beetle", "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "ground beetle, carabid beetle", "long-horned beetle, longicorn, longicorn beetle", "leaf beetle, chrysomelid", "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant, emmet, pismire", "grasshopper, hopper", "cricket", "walking stick, walkingstick, stick insect", "cockroach, roach", "mantis, mantid", "cicada, cicala", "leafhopper", "lacewing, lacewing fly", "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "damselfly", "admiral", "ringlet, ringlet butterfly", "monarch, monarch butterfly, milkweed butterfly, danaus plexippus", "cabbage butterfly", "sulphur butterfly, sulfur butterfly", "lycaenid, lycaenid butterfly", "starfish, sea star", "sea urchin", "sea cucumber, holothurian", "wood rabbit, cottontail, cottontail rabbit", "hare", "angora, angora rabbit", "hamster", "porcupine, hedgehog", "fox squirrel, eastern fox squirrel, sciurus niger", "marmot", "beaver", "guinea pig, cavia cobaya", "sorrel", "zebra", "hog, pig, grunter, squealer, sus scrofa", "wild boar, boar, sus scrofa", "warthog", "hippopotamus, hippo, river horse, hippopotamus amphibius", "ox", "water buffalo, water ox, asiatic buffalo, bubalus bubalis", "bison", "ram, tup", "bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis", "ibex, capra ibex", "hartebeest", "impala, aepyceros melampus", "gazelle", "arabian camel, dromedary, camelus dromedarius", "llama", "weasel", "mink", "polecat, fitch, foulmart, foumart, mustela putorius", "black-footed ferret, ferret, mustela nigripes", "otter", "skunk, polecat, wood pussy", "badger", "armadillo", "three-toed sloth, ai, bradypus tridactylus", "orangutan, orang, orangutang, pongo pygmaeus", "gorilla, gorilla gorilla", "chimpanzee, chimp, pan troglodytes", "gibbon, hylobates lar", "siamang, hylobates syndactylus, symphalangus syndactylus", "guenon, guenon monkey", "patas, hussar monkey, erythrocebus patas", "baboon", "macaque", "langur", "colobus, colobus monkey", "proboscis monkey, nasalis larvatus", "marmoset", "capuchin, ringtail, cebus capucinus", "howler monkey, howler", "titi, titi monkey", "spider monkey, ateles geoffroyi", "squirrel monkey, saimiri sciureus", "madagascar cat, ring-tailed lemur, lemur catta", "indri, indris, indri indri, indri brevicaudatus", "indian elephant, elephas maximus", "african elephant, loxodonta africana", "lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens", "giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca", "barracouta, snoek", "eel", "coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch", "rock beauty, holocanthus tricolor", "anemone fish", "sturgeon", "gar, garfish, garpike, billfish, lepisosteus osseus", "lionfish", "puffer, pufferfish, blowfish, globefish", "abacus", "abaya", "academic gown, academic robe, judge's robe", "accordion, piano accordion, squeeze box", "acoustic guitar", "aircraft carrier, carrier, flattop, attack aircraft carrier", "airliner", "airship, dirigible", "altar", "ambulance", "amphibian, amphibious vehicle", "analog clock", "apiary, bee house", "apron", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "assault rifle, assault gun", "backpack, back pack, knapsack, packsack, rucksack, haversack", "bakery, bakeshop, bakehouse", "balance beam, beam", "balloon", "ballpoint, ballpoint pen, ballpen, biro", "band aid", "banjo", "bannister, banister, balustrade, balusters, handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel, cask", "barrow, garden cart, lawn cart, wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "bathing cap, swimming cap", "bath towel", "bathtub, bathing tub, bath, tub", "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "beacon, lighthouse, beacon light, pharos", "beaker", "bearskin, busby, shako", "beer bottle", "beer glass", "bell cote, bell cot", "bib", "bicycle-built-for-two, tandem bicycle, tandem", "bikini, two-piece", "binder, ring-binder", "binoculars, field glasses, opera glasses", "birdhouse", "boathouse", "bobsled, bobsleigh, bob", "bolo tie, bolo, bola tie, bola", "bonnet, poke bonnet", "bookcase", "bookshop, bookstore, bookstall", "bottlecap", "bow", "bow tie, bow-tie, bowtie", "brass, memorial tablet, plaque", "brassiere, bra, bandeau", "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "breastplate, aegis, egis", "broom", "bucket, pail", "buckle", "bulletproof vest", "bullet train, bullet", "butcher shop, meat market", "cab, hack, taxi, taxicab", "caldron, cauldron", "candle, taper, wax light", "cannon", "canoe", "can opener, tin opener", "cardigan", "car mirror", "carousel, carrousel, merry-go-round, roundabout, whirligig", "carpenter's kit, tool kit", "carton", "car wheel", "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm", "cassette", "cassette player", "castle", "catamaran", "cd player", "cello, violoncello", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "chain", "chainlink fence", "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "chain saw, chainsaw", "chest", "chiffonier, commode", "chime, bell, gong", "china cabinet, china closet", "christmas stocking", "church, church building", "cinema, movie theater, movie theatre, movie house, picture palace", "cleaver, meat cleaver, chopper", "cliff dwelling", "cloak", "clog, geta, patten, sabot", "cocktail shaker", "coffee mug", "coffeepot", "coil, spiral, volute, whorl, helix", "combination lock", "computer keyboard, keypad", "confectionery, confectionary, candy store", "container ship, containership, container vessel", "convertible", "corkscrew, bottle screw", "cornet, horn, trumpet, trump", "cowboy boot", "cowboy hat, ten-gallon hat", "cradle", "crane", "crash helmet", "crate", "crib, cot", "crock pot", "croquet ball", "crutch", "cuirass", "dam, dike, dyke", "desk", "desktop computer", "dial telephone, dial phone", "diaper, nappy, napkin", "digital clock", "digital watch", "dining table, board", "dishrag, dishcloth", "dishwasher, dish washer, dishwashing machine", "disk brake, disc brake", "dock, dockage, docking facility", "dogsled, dog sled, dog sleigh", "dome", "doormat, welcome mat", "drilling platform, offshore rig", "drum, membranophone, tympan", "drumstick", "dumbbell", "dutch oven", "electric fan, blower", "electric guitar", "electric locomotive", "entertainment center", "envelope", "espresso maker", "face powder", "feather boa, boa", "file, file cabinet, filing cabinet", "fireboat", "fire engine, fire truck", "fire screen, fireguard", "flagpole, flagstaff", "flute, transverse flute", "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster", "freight car", "french horn, horn", "frying pan, frypan, skillet", "fur coat", "garbage truck, dustcart", "gasmask, respirator, gas helmet", "gas pump, gasoline pump, petrol pump, island dispenser", "goblet", "go-kart", "golf ball", "golfcart, golf cart", "gondola", "gong, tam-tam", "gown", "grand piano, grand", "greenhouse, nursery, glasshouse", "grille, radiator grille", "grocery store, grocery, food market, market", "guillotine", "hair slide", "hair spray", "half track", "hammer", "hamper", "hand blower, blow dryer, blow drier, hair dryer, hair drier", "hand-held computer, hand-held microcomputer", "handkerchief, hankie, hanky, hankey", "hard disc, hard disk, fixed disk", "harmonica, mouth organ, harp, mouth harp", "harp", "harvester, reaper", "hatchet", "holster", "home theater, home theatre", "honeycomb", "hook, claw", "hoopskirt, crinoline", "horizontal bar, high bar", "horse cart, horse-cart", "hourglass", "ipod", "iron, smoothing iron", "jack-o'-lantern", "jean, blue jean, denim", "jeep, landrover", "jersey, t-shirt, tee shirt", "jigsaw puzzle", "jinrikisha, ricksha, rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat, laboratory coat", "ladle", "lampshade, lamp shade", "laptop, laptop computer", "lawn mower, mower", "lens cap, lens cover", "letter opener, paper knife, paperknife", "library", "lifeboat", "lighter, light, igniter, ignitor", "limousine, limo", "liner, ocean liner", "lipstick, lip rouge", "loafer", "lotion", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "loupe, jeweler's loupe", "lumbermill, sawmill", "magnetic compass", "mailbag, postbag", "mailbox, letter box", "maillot", "maillot, tank suit", "manhole cover", "maraca", "marimba, xylophone", "mask", "matchstick", "maypole", "maze, labyrinth", "measuring cup", "medicine chest, medicine cabinet", "megalith, megalithic structure", "microphone, mike", "microwave, microwave oven", "military uniform", "milk can", "minibus", "miniskirt, mini", "minivan", "missile", "mitten", "mixing bowl", "mobile home, manufactured home", "model t", "modem", "monastery", "monitor", "moped", "mortar", "mortarboard", "mosque", "mosquito net", "motor scooter, scooter", "mountain bike, all-terrain bike, off-roader", "mountain tent", "mouse, computer mouse", "mousetrap", "moving van", "muzzle", "nail", "neck brace", "necklace", "nipple", "notebook, notebook computer", "obelisk", "oboe, hautboy, hautbois", "ocarina, sweet potato", "odometer, hodometer, mileometer, milometer", "oil filter", "organ, pipe organ", "oscilloscope, scope, cathode-ray oscilloscope, cro", "overskirt", "oxcart", "oxygen mask", "packet", "paddle, boat paddle", "paddlewheel, paddle wheel", "padlock", "paintbrush", "pajama, pyjama, pj's, jammies", "palace", "panpipe, pandean pipe, syrinx", "paper towel", "parachute, chute", "parallel bars, bars", "park bench", "parking meter", "passenger car, coach, carriage", "patio, terrace", "pay-phone, pay-station", "pedestal, plinth, footstall", "pencil box, pencil case", "pencil sharpener", "perfume, essence", "petri dish", "photocopier", "pick, plectrum, plectron", "pickelhaube", "picket fence, paling", "pickup, pickup truck", "pier", "piggy bank, penny bank", "pill bottle", "pillow", "ping-pong ball", "pinwheel", "pirate, pirate ship", "pitcher, ewer", "plane, carpenter's plane, woodworking plane", "planetarium", "plastic bag", "plate rack", "plow, plough", "plunger, plumber's helper", "polaroid camera, polaroid land camera", "pole", "police van, police wagon, paddy wagon, patrol wagon, wagon, black maria", "poncho", "pool table, billiard table, snooker table", "pop bottle, soda bottle", "pot, flowerpot", "potter's wheel", "power drill", "prayer rug, prayer mat", "printer", "prison, prison house", "projectile, missile", "projector", "puck, hockey puck", "punching bag, punch bag, punching ball, punchball", "purse", "quill, quill pen", "quilt, comforter, comfort, puff", "racer, race car, racing car", "racket, racquet", "radiator", "radio, wireless", "radio telescope, radio reflector", "rain barrel", "recreational vehicle, rv, r.v.", "reel", "reflex camera", "refrigerator, icebox", "remote control, remote", "restaurant, eating house, eating place, eatery", "revolver, six-gun, six-shooter", "rifle", "rocking chair, rocker", "rotisserie", "rubber eraser, rubber, pencil eraser", "rugby ball", "rule, ruler", "running shoe", "safe", "safety pin", "saltshaker, salt shaker", "sandal", "sarong", "sax, saxophone", "scabbard", "scale, weighing machine", "school bus", "schooner", "scoreboard", "screen, crt screen", "screw", "screwdriver", "seat belt, seatbelt", "sewing machine", "shield, buckler", "shoe shop, shoe-shop, shoe store", "shoji", "shopping basket", "shopping cart", "shovel", "shower cap", "shower curtain", "ski", "ski mask", "sleeping bag", "slide rule, slipstick", "sliding door", "slot, one-armed bandit", "snorkel", "snowmobile", "snowplow, snowplough", "soap dispenser", "soccer ball", "sock", "solar dish, solar collector, solar furnace", "sombrero", "soup bowl", "space bar", "space heater", "space shuttle", "spatula", "speedboat", "spider web, spider's web", "spindle", "sports car, sport car", "spotlight, spot", "stage", "steam locomotive", "steel arch bridge", "steel drum", "stethoscope", "stole", "stone wall", "stopwatch, stop watch", "stove", "strainer", "streetcar, tram, tramcar, trolley, trolley car", "stretcher", "studio couch, day bed", "stupa, tope", "submarine, pigboat, sub, u-boat", "suit, suit of clothes", "sundial", "sunglass", "sunglasses, dark glasses, shades", "sunscreen, sunblock, sun blocker", "suspension bridge", "swab, swob, mop", "sweatshirt", "swimming trunks, bathing trunks", "swing", "switch, electric switch, electrical switch", "syringe", "table lamp", "tank, army tank, armored combat vehicle, armoured combat vehicle", "tape player", "teapot", "teddy, teddy bear", "television, television system", "tennis ball", "thatch, thatched roof", "theater curtain, theatre curtain", "thimble", "thresher, thrasher, threshing machine", "throne", "tile roof", "toaster", "tobacco shop, tobacconist shop, tobacconist", "toilet seat", "torch", "totem pole", "tow truck, tow car, wrecker", "toyshop", "tractor", "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "tray", "trench coat", "tricycle, trike, velocipede", "trimaran", "tripod", "triumphal arch", "trolleybus, trolley coach, trackless trolley", "trombone", "tub, vat", "turnstile", "typewriter keyboard", "umbrella", "unicycle, monocycle", "upright, upright piano", "vacuum, vacuum cleaner", "vase", "vault", "velvet", "vending machine", "vestment", "viaduct", "violin, fiddle", "volleyball", "waffle iron", "wall clock", "wallet, billfold, notecase, pocketbook", "wardrobe, closet, press", "warplane, military plane", "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "washer, automatic washer, washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle", "wig", "window screen", "window shade", "windsor tie", "wine bottle", "wing", "wok", "wooden spoon", "wool, woolen, woollen", "worm fence, snake fence, snake-rail fence, virginia fence", "wreck", "yawl", "yurt", "web site, website, internet site, site", "comic book", "crossword puzzle, crossword", "street sign", "traffic light, traffic signal, stoplight", "book jacket, dust cover, dust jacket, dust wrapper", "menu", "plate", "guacamole", "consomme", "hot pot, hotpot", "trifle", "ice cream, icecream", "ice lolly, lolly, lollipop, popsicle", "french loaf", "bagel, beigel", "pretzel", "cheeseburger", "hotdog, hot dog, red hot", "mashed potato", "head cabbage", "broccoli", "cauliflower", "zucchini, courgette", "spaghetti squash", "acorn squash", "butternut squash", "cucumber, cuke", "artichoke, globe artichoke", "bell pepper", "cardoon", "mushroom", "granny smith", "strawberry", "orange", "lemon", "fig", "pineapple, ananas", "banana", "jackfruit, jak, jack", "custard apple", "pomegranate", "hay", "carbonara", "chocolate sauce, chocolate syrup", "dough", "meat loaf, meatloaf", "pizza, pizza pie", "potpie", "burrito", "red wine", "espresso", "cup", "eggnog", "alp", "bubble", "cliff, drop, drop-off", "coral reef", "geyser", "lakeside, lakeshore", "promontory, headland, head, foreland", "sandbar, sand bar", "seashore, coast, seacoast, sea-coast", "valley, vale", "volcano", "ballplayer, baseball player", "groom, bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum", "corn", "acorn", "hip, rose hip, rosehip", "buckeye, horse chestnut, conker", "coral fungus", "agaric", "gyromitra", "stinkhorn, carrion fungus", "earthstar", "hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa", "bolete", "ear, spike, capitulum", "toilet tissue, toilet paper, bathroom tissue" ]

ernie-ai/finetuned-vit-image-text-classifier

# finetuned-vit-doc-text-classifer This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the ernie-ai/image-text-examples-ar-cn-latin-notext dataset. It achieves the following results on the evaluation set: - Loss: 0.3107 - Accuracy: 0.9030 ## Model description It is an image classificatin model fine-tuned to predict whether an images contains text and if that text is Latin script, Chinese or Arabic. It also classifies non-text images. ## Training and evaluation data Dataset: [ernie-ai/image-text-examples-ar-cn-latin-notext] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2719 | 2.08 | 100 | 0.4120 | 0.8657 | | 0.1027 | 4.17 | 200 | 0.3907 | 0.8881 | | 0.0723 | 6.25 | 300 | 0.3107 | 0.9030 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "ar_docs", "cn_docs", "latin_docs", "non-text" ]

ernie-ai/autotrain-document-text-language-ar-en-zh-3338392240

# finetuned-MS-swin-doc-text-classifer This model is a fine-tuned version of Microsoft’s Swin Transformer tiny-sized model [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the ernie-ai/image-text-examples-ar-cn-latin-notext dataset. It achieves the following results on the evaluation set: - Loss: 0.267 - Accuracy: 0.882 ## Model description It is an image classificatin model fine-tuned to predict whether an images contains text and if that text is Latin script, Chinese or Arabic. It also classifies non-text images. ## Training and evaluation data Dataset: [ernie-ai/image-text-examples-ar-cn-latin-notext] # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3338392240 - CO2 Emissions (in grams): 2.2267 ## Validation Metrics - Loss: 0.267 - Accuracy: 0.882 - Macro F1: 0.862 - Micro F1: 0.882 - Weighted F1: 0.880 - Macro Precision: 0.877 - Micro Precision: 0.882 - Weighted Precision: 0.883 - Macro Recall: 0.856 - Micro Recall: 0.882 - Weighted Recall: 0.882

[ "ar_docs", "cn_docs", "latin_docs", "non-text" ]

Gokulapriyan/swin-tiny-patch4-window7-224-finetuned-3e

# swin-tiny-patch4-window7-224-finetuned-3e This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1065 - Accuracy: 0.9606 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4549 | 1.0 | 527 | 0.2910 | 0.8857 | | 0.2838 | 2.0 | 1054 | 0.1524 | 0.9410 | | 0.254 | 3.0 | 1581 | 0.1065 | 0.9606 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "fnh_frames", "hcc_frames", "hhe_frames", "healthy_frames" ]

Ailyth/3_Labels

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3341092265 - CO2 Emissions (in grams): 2.6501 ## Validation Metrics - Loss: 0.133 - Accuracy: 0.950 - Macro F1: 0.951 - Micro F1: 0.950 - Weighted F1: 0.950 - Macro Precision: 0.951 - Micro Precision: 0.950 - Weighted Precision: 0.950 - Macro Recall: 0.951 - Micro Recall: 0.950 - Weighted Recall: 0.950

[ "cuisine", "versailles", "zhazu" ]

Gokulapriyan/swin-tiny-patch4-window7-224-finetuned-new_dataset_50e

# swin-tiny-patch4-window7-224-finetuned-new_dataset_50e This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6407 - Accuracy: 0.7973 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.94 | 4 | 0.7081 | 0.6081 | | No log | 1.94 | 8 | 0.7104 | 0.6351 | | 0.5516 | 2.94 | 12 | 0.6911 | 0.6351 | | 0.5516 | 3.94 | 16 | 0.7156 | 0.7027 | | 0.537 | 4.94 | 20 | 0.7345 | 0.7297 | | 0.537 | 5.94 | 24 | 0.6745 | 0.6892 | | 0.537 | 6.94 | 28 | 0.7146 | 0.7297 | | 0.5333 | 7.94 | 32 | 0.7057 | 0.6892 | | 0.5333 | 8.94 | 36 | 0.6531 | 0.7027 | | 0.4871 | 9.94 | 40 | 0.6405 | 0.7027 | | 0.4871 | 10.94 | 44 | 0.6126 | 0.6892 | | 0.4871 | 11.94 | 48 | 0.6303 | 0.7027 | | 0.4432 | 12.94 | 52 | 0.6264 | 0.7027 | | 0.4432 | 13.94 | 56 | 0.6347 | 0.7432 | | 0.3669 | 14.94 | 60 | 0.6698 | 0.6622 | | 0.3669 | 15.94 | 64 | 0.6346 | 0.7568 | | 0.3669 | 16.94 | 68 | 0.6510 | 0.6892 | | 0.3704 | 17.94 | 72 | 0.6491 | 0.6892 | | 0.3704 | 18.94 | 76 | 0.5947 | 0.7568 | | 0.3624 | 19.94 | 80 | 0.6248 | 0.7027 | | 0.3624 | 20.94 | 84 | 0.6580 | 0.7027 | | 0.3624 | 21.94 | 88 | 0.6345 | 0.7162 | | 0.3164 | 22.94 | 92 | 0.6092 | 0.7568 | | 0.3164 | 23.94 | 96 | 0.6498 | 0.7162 | | 0.2777 | 24.94 | 100 | 0.6915 | 0.7703 | | 0.2777 | 25.94 | 104 | 0.6482 | 0.7838 | | 0.2777 | 26.94 | 108 | 0.6407 | 0.7973 | | 0.2946 | 27.94 | 112 | 0.6135 | 0.7838 | | 0.2946 | 28.94 | 116 | 0.6819 | 0.7568 | | 0.2546 | 29.94 | 120 | 0.6401 | 0.7568 | | 0.2546 | 30.94 | 124 | 0.6370 | 0.7432 | | 0.2546 | 31.94 | 128 | 0.6488 | 0.7703 | | 0.2477 | 32.94 | 132 | 0.6429 | 0.7973 | | 0.2477 | 33.94 | 136 | 0.6540 | 0.7703 | | 0.1968 | 34.94 | 140 | 0.5895 | 0.7973 | | 0.1968 | 35.94 | 144 | 0.6242 | 0.7568 | | 0.1968 | 36.94 | 148 | 0.6575 | 0.7568 | | 0.2235 | 37.94 | 152 | 0.6263 | 0.7703 | | 0.2235 | 38.94 | 156 | 0.6225 | 0.7838 | | 0.2005 | 39.94 | 160 | 0.6731 | 0.7703 | | 0.2005 | 40.94 | 164 | 0.6844 | 0.7703 | | 0.2005 | 41.94 | 168 | 0.6550 | 0.7703 | | 0.2062 | 42.94 | 172 | 0.6700 | 0.7703 | | 0.2062 | 43.94 | 176 | 0.6661 | 0.7703 | | 0.1933 | 44.94 | 180 | 0.6606 | 0.7838 | | 0.1933 | 45.94 | 184 | 0.6757 | 0.7703 | | 0.1933 | 46.94 | 188 | 0.6889 | 0.7568 | | 0.1895 | 47.94 | 192 | 0.6940 | 0.7568 | | 0.1895 | 48.94 | 196 | 0.6919 | 0.7568 | | 0.1666 | 49.94 | 200 | 0.6899 | 0.7432 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "0", "1", "2" ]

Kaludi/food-category-classification-v2.0

# Food Category Classification v2.0 This is an updated Food Category Image Classifier model of the [old](https://huggingface.co/Kaludi/food-category-classification) model that has been trained by [Kaludi](https://huggingface.co/Kaludi) to recognize **12** different categories of foods, which includes **Bread**, **Dairy**, **Dessert**, **Egg**, **Fried Food**, **Fruit**, **Meat**, **Noodles**, **Rice**, **Seafood**, **Soup**, and **Vegetable**. It can accurately classify an image of food into one of these categories by analyzing its visual features. This model can be used by food bloggers, restaurants, and recipe websites to quickly categorize and sort their food images, making it easier to manage their content and provide a better user experience. ### Gradio This model supports a [Gradio](https://github.com/gradio-app/gradio) Web UI to run the data-food-classification model: [![Open In HF Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/Kaludi/Food-Category-Classification_V2_App) ## Validation Metrics - Problem type: Multi-class Classification - Model ID: 3353292434 - CO2 Emissions (in grams): 12.4563 - Loss: 0.144 - Accuracy: 0.960 - Macro F1: 0.959 - Micro F1: 0.960 - Weighted F1: 0.959 - Macro Precision: 0.962 - Micro Precision: 0.960 - Weighted Precision: 0.962 - Macro Recall: 0.960 - Micro Recall: 0.960 - Weighted Recall: 0.960

[ "bread", "dairy", "soup", "vegetable", "dessert", "egg", "fried food", "fruit", "meat", "noodles", "rice", "seafood" ]

Mustafa21/my_awesome_food_model

Full notebook : https://github.com/MustafaAlahmid/hugging_face_models/blob/main/Vit-classifier_food_dataset.ipynb --- license: apache-2.0 tags: - generated_from_trainer datasets: - food101 metrics: - accuracy model-index: - name: my_awesome_food_model results: - task: name: Image Classification type: image-classification dataset: name: food101 type: food101 config: default split: train[:1000] args: default metrics: - name: Accuracy type: accuracy value: 0.985 ---  # my_awesome_food_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 1.2335 - Accuracy: 0.985 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.0523 | 1.0 | 50 | 1.9226 | 0.935 | | 1.3718 | 2.0 | 100 | 1.3422 | 0.995 | | 1.2298 | 3.0 | 150 | 1.2335 | 0.985 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "apple_pie", "baby_back_ribs", "bruschetta", "waffles", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "baklava", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "beef_carpaccio", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "beef_tartare", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "beet_salad", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "beignets", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "bibimbap", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "bread_pudding", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "breakfast_burrito", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare" ]

eormeno12/platzi_vit_model

# platzi_vit_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0328 - Accuracy: 0.9925 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1427 | 3.85 | 500 | 0.0328 | 0.9925 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "angular_leaf_spot", "bean_rust", "healthy" ]

AllanOuii/resnet50_mask_classification

# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3387392923 - CO2 Emissions (in grams): 1.5545 ## Validation Metrics - Loss: 0.138 - Accuracy: 0.977 - Precision: 0.958 - Recall: 1.000 - AUC: 0.996 - F1: 0.979

[ "mask", "no_mask" ]

JoffreyMa/autotrain-histopathological_image_classification-3393093035

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3393093035 - CO2 Emissions (in grams): 4.0129 ## Validation Metrics - Loss: 0.183 - Accuracy: 0.933 - Macro F1: 0.931 - Micro F1: 0.933 - Weighted F1: 0.933 - Macro Precision: 0.927 - Micro Precision: 0.933 - Weighted Precision: 0.935 - Macro Recall: 0.939 - Micro Recall: 0.933 - Weighted Recall: 0.933

[ "1", "2", "3", "4", "5", "6", "7", "8" ]

JoffreyMa/autotrain-histopathological_image_classification-3393093036

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3393093036 - CO2 Emissions (in grams): 4.8204 ## Validation Metrics - Loss: 0.186 - Accuracy: 0.933 - Macro F1: 0.933 - Micro F1: 0.933 - Weighted F1: 0.932 - Macro Precision: 0.929 - Micro Precision: 0.933 - Weighted Precision: 0.934 - Macro Recall: 0.941 - Micro Recall: 0.933 - Weighted Recall: 0.933

[ "1", "2", "3", "4", "5", "6", "7", "8" ]

JoffreyMa/autotrain-histopathological_image_classification-3393093038

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3393093038 - CO2 Emissions (in grams): 3.5031 ## Validation Metrics - Loss: 0.179 - Accuracy: 0.966 - Macro F1: 0.959 - Micro F1: 0.966 - Weighted F1: 0.966 - Macro Precision: 0.969 - Micro Precision: 0.966 - Weighted Precision: 0.969 - Macro Recall: 0.954 - Micro Recall: 0.966 - Weighted Recall: 0.966

[ "1", "2", "3", "4", "5", "6", "7", "8" ]

JoffreyMa/autotrain-histopathological_image_classification-3393093039

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3393093039 - CO2 Emissions (in grams): 3.9275 ## Validation Metrics - Loss: 0.243 - Accuracy: 0.910 - Macro F1: 0.928 - Micro F1: 0.910 - Weighted F1: 0.910 - Macro Precision: 0.929 - Micro Precision: 0.910 - Weighted Precision: 0.914 - Macro Recall: 0.929 - Micro Recall: 0.910 - Weighted Recall: 0.910

[ "1", "2", "3", "4", "5", "6", "7", "8" ]

JoffreyMa/autotrain-histopathological_image_classification-3393093037

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3393093037 - CO2 Emissions (in grams): 4.0015 ## Validation Metrics - Loss: 1.988 - Accuracy: 0.348 - Macro F1: 0.210 - Micro F1: 0.348 - Weighted F1: 0.279 - Macro Precision: 0.217 - Micro Precision: 0.348 - Weighted Precision: 0.278 - Macro Recall: 0.245 - Micro Recall: 0.348 - Weighted Recall: 0.348

[ "1", "2", "3", "4", "5", "6", "7", "8" ]

apatidar0/vit-base-beans_own

# vit-base-beans_own This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0558 - Accuracy: 0.9850 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "angular_leaf_spot", "bean_rust", "healthy" ]

dhairyakapadia/swin-tiny-patch4-window7-224-finetuned-skin-cancer

# swin-tiny-patch4-window7-224-finetuned-skin-cancer This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "actinic-keratoses", "basal-cell-carcinoma", "benign-keratosis-like-lesions", "dermatofibroma", "melanocytic-nevi", "melanoma", "vascular-lesions" ]

DunnBC22/vit-base-patch16-224-in21k-weather-images-classification

# vit-base-patch16-224-in21k-weather-images-classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2255 - Accuracy: 0.9340 - Weighted f1: 0.9341 - Micro f1: 0.9340 - Macro f1: 0.9372 - Weighted recall: 0.9340 - Micro recall: 0.9340 - Macro recall: 0.9354 - Weighted precision: 0.9347 - Micro precision: 0.9340 - Macro precision: 0.9398 ## Model description This is a classification model of weather images. For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Weather%20Images/Weather_Images_ViT.ipynb ## Intended uses & limitations This model is intended to demonstrate my ability to solve a complex problem using technology. ## Training and evaluation data Dataset Source: https://www.kaggle.com/datasets/jehanbhathena/weather-dataset ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| | 2.4333 | 1.0 | 337 | 0.3374 | 0.9036 | 0.9028 | 0.9036 | 0.9080 | 0.9036 | 0.9036 | 0.9002 | 0.9088 | 0.9036 | 0.9234 | | 0.4422 | 2.0 | 674 | 0.2504 | 0.9228 | 0.9226 | 0.9228 | 0.9285 | 0.9228 | 0.9228 | 0.9273 | 0.9248 | 0.9228 | 0.9318 | | 0.1051 | 3.0 | 1011 | 0.2255 | 0.9340 | 0.9341 | 0.9340 | 0.9372 | 0.9340 | 0.9340 | 0.9354 | 0.9347 | 0.9340 | 0.9398 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.8.0 - Tokenizers 0.12.1

[ "dew", "fogsmog", "snow", "frost", "glaze", "hail", "lightning", "rain", "rainbow", "rime", "sandstorm" ]

asaderu-ai/kebersihan_jalan_detection

## Validation Metrics - Loss: 0.004 - Accuracy: 1.000 - Precision: 1.000 - Recall: 1.000 - AUC: 1.000 - F1: 1.000

[ "bersih", "kotor" ]

Celal11/resnet-50-4-32

# resnet-50-4-32 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.9705 - Accuracy: 0.6410 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3833 | 1.0 | 224 | 1.2683 | 0.5134 | | 1.2404 | 2.0 | 448 | 1.1342 | 0.5659 | | 1.1492 | 3.0 | 672 | 1.0359 | 0.6087 | | 1.1433 | 4.0 | 896 | 0.9705 | 0.6410 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]

Celal11/resnet-50-0.007

# resnet-50-0.007 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.9735 - Accuracy: 0.6296 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.007 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.4221 | 1.0 | 224 | 1.2410 | 0.5274 | | 1.2521 | 2.0 | 448 | 1.1716 | 0.5499 | | 1.1609 | 3.0 | 672 | 1.0495 | 0.5968 | | 1.1457 | 4.0 | 896 | 0.9735 | 0.6296 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]

davanstrien/autotrain-ia_covers-3416193421

# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3416193421 - CO2 Emissions (in grams): 1.6972 ## Validation Metrics - Loss: 0.213 - Accuracy: 0.904 - Precision: 0.714 - Recall: 0.875 - AUC: 0.948 - F1: 0.787

[ "year-1923-not-very-useful-covers", "year-1923-useful-covers" ]

jsacex/vit-base-patch16-224-in21k-finetuned-lora-food101

# vit-base-patch16-224-in21k-finetuned-lora-food101 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 0.1408 - Accuracy: 0.964 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 9 | 0.5739 | 0.874 | | 2.1968 | 2.0 | 18 | 0.2064 | 0.944 | | 0.3323 | 3.0 | 27 | 0.1521 | 0.96 | | 0.2087 | 4.0 | 36 | 0.1408 | 0.964 | | 0.1678 | 5.0 | 45 | 0.1352 | 0.962 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.12.1

[ "apple_pie", "baby_back_ribs", "baklava", "beef_carpaccio", "beef_tartare", "beet_salad", "beignets", "bibimbap", "bread_pudding", "breakfast_burrito", "bruschetta", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels", "nachos", "omelette", "onion_rings", "oysters", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", "pho", "pizza", "pork_chop", "poutine", "prime_rib", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare", "waffles" ]

MPSTME/swin-tiny-patch4-window7-224-finetuned-skin-cancer

# swin-tiny-patch4-window7-224-finetuned-skin-cancer This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "actinic-keratoses", "basal-cell-carcinoma", "benign-keratosis-like-lesions", "dermatofibroma", "melanocytic-nevi", "melanoma", "vascular-lesions" ]

lixiqi/beit-base-patch16-224-pt22k-ft22k-finetuned-FER-5e-05-3

# beit-base-patch16-224-pt22k-ft22k-finetuned-FER-5e-05-3 This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.8598 - Accuracy: 0.6860 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1566 | 1.0 | 224 | 0.9830 | 0.6311 | | 1.0301 | 2.0 | 448 | 0.8939 | 0.6730 | | 0.991 | 3.0 | 672 | 0.8598 | 0.6860 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

[ "angry", "disgust", "fear", "happy", "neutral", "sad", "surprise" ]

asaderu-ai/kualitas_lemon

## Validation Metrics - Loss: 0.001 - Accuracy: 1.000 - Precision: 1.000 - Recall: 1.000 - AUC: 1.000 - F1: 1.000

[ "bagus", "tidak_bagus" ]

Gokulapriyan/swin-tiny-patch4-window7-224-finetuned-og_dataset_5e

# swin-tiny-patch4-window7-224-finetuned-og_dataset_5e This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0788 - Accuracy: 0.9705 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4626 | 1.0 | 546 | 0.3468 | 0.8578 | | 0.2915 | 2.0 | 1092 | 0.1998 | 0.9200 | | 0.2333 | 3.0 | 1638 | 0.1155 | 0.9566 | | 0.2019 | 4.0 | 2184 | 0.0977 | 0.9634 | | 0.1713 | 5.0 | 2730 | 0.0788 | 0.9705 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

[ "fnh", "hcc", "hhe", "healthy" ]

1024khandsom/autotrain-ant-bee-3482194557

# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3482194557 - CO2 Emissions (in grams): 0.7388 ## Validation Metrics - Loss: 0.013 - Accuracy: 1.000 - Precision: 1.000 - Recall: 1.000 - AUC: 1.000 - F1: 1.000

[ "ants", "bees" ]