segformer-b0-finetuned-Eduardo-food103-GOOGLE100

This model is a fine-tuned version of nvidia/mit-b0 on the EduardoPacheco/FoodSeg103 dataset. It achieves the following results on the evaluation set:

• Loss: 2.0865
• Mean Iou: 0.0515
• Mean Accuracy: 0.1257
• Overall Accuracy: 0.2045
• Accuracy Background: nan
• Accuracy Candy: nan
• Accuracy Egg tart: nan
• Accuracy French fries: 0.0
• Accuracy Chocolate: nan
• Accuracy Biscuit: nan
• Accuracy Popcorn: nan
• Accuracy Pudding: nan
• Accuracy Ice cream: 0.0
• Accuracy Cheese butter: 0.0
• Accuracy Cake: 0.0
• Accuracy Wine: 0.0
• Accuracy Milkshake: nan
• Accuracy Coffee: nan
• Accuracy Juice: 0.0
• Accuracy Milk: nan
• Accuracy Tea: nan
• Accuracy Almond: nan
• Accuracy Red beans: nan
• Accuracy Cashew: nan
• Accuracy Dried cranberries: nan
• Accuracy Soy: nan
• Accuracy Walnut: nan
• Accuracy Peanut: nan
• Accuracy Egg: nan
• Accuracy Apple: nan
• Accuracy Date: nan
• Accuracy Apricot: nan
• Accuracy Avocado: nan
• Accuracy Banana: nan
• Accuracy Strawberry: nan
• Accuracy Cherry: nan
• Accuracy Blueberry: nan
• Accuracy Raspberry: nan
• Accuracy Mango: nan
• Accuracy Olives: nan
• Accuracy Peach: 0.0
• Accuracy Lemon: nan
• Accuracy Pear: nan
• Accuracy Fig: nan
• Accuracy Pineapple: nan
• Accuracy Grape: nan
• Accuracy Kiwi: nan
• Accuracy Melon: nan
• Accuracy Orange: 0.0
• Accuracy Watermelon: nan
• Accuracy Steak: 0.8548
• Accuracy Pork: 0.5362
• Accuracy Chicken duck: 0.3458
• Accuracy Sausage: 0.0
• Accuracy Fried meat: nan
• Accuracy Lamb: 0.0
• Accuracy Sauce: 0.0
• Accuracy Crab: nan
• Accuracy Fish: nan
• Accuracy Shellfish: 0.0
• Accuracy Shrimp: 0.0
• Accuracy Soup: 0.0
• Accuracy Bread: 0.0157
• Accuracy Corn: 0.0
• Accuracy Hamburg: nan
• Accuracy Pizza: nan
• Accuracy hanamaki baozi: 0.0
• Accuracy Wonton dumplings: nan
• Accuracy Pasta: nan
• Accuracy Noodles: 0.2191
• Accuracy Rice: 0.3396
• Accuracy Pie: 0.0
• Accuracy Tofu: 0.0
• Accuracy Eggplant: nan
• Accuracy Potato: 0.6707
• Accuracy Garlic: nan
• Accuracy Cauliflower: 0.0
• Accuracy Tomato: 0.0295
• Accuracy Kelp: nan
• Accuracy Seaweed: nan
• Accuracy Spring onion: 0.0
• Accuracy Rape: 0.0
• Accuracy Ginger: nan
• Accuracy Okra: 0.0
• Accuracy Lettuce: 0.0014
• Accuracy Pumpkin: nan
• Accuracy Cucumber: 0.2728
• Accuracy White radish: 0.0
• Accuracy Carrot: 0.9345
• Accuracy Asparagus: nan
• Accuracy Bamboo shoots: nan
• Accuracy Broccoli: 0.7618
• Accuracy Celery stick: 0.0400
• Accuracy Cilantro mint: 0.0
• Accuracy Snow peas: nan
• Accuracy cabbage: nan
• Accuracy Bean sprouts: nan
• Accuracy Onion: 0.0075
• Accuracy Pepper: nan
• Accuracy Green beans: nan
• Accuracy French beans: nan
• Accuracy King oyster mushroom: nan
• Accuracy Shiitake: nan
• Accuracy Enoki mushroom: nan
• Accuracy Oyster mushroom: nan
• Accuracy White button mushroom: 0.0
• Accuracy Salad: nan
• Accuracy Other ingredients: 0.0
• Iou Background: 0.0
• Iou Candy: nan
• Iou Egg tart: nan
• Iou French fries: 0.0
• Iou Chocolate: nan
• Iou Biscuit: 0.0
• Iou Popcorn: nan
• Iou Pudding: nan
• Iou Ice cream: 0.0
• Iou Cheese butter: 0.0
• Iou Cake: 0.0
• Iou Wine: 0.0
• Iou Milkshake: nan
• Iou Coffee: nan
• Iou Juice: 0.0
• Iou Milk: nan
• Iou Tea: nan
• Iou Almond: nan
• Iou Red beans: nan
• Iou Cashew: nan
• Iou Dried cranberries: nan
• Iou Soy: nan
• Iou Walnut: nan
• Iou Peanut: nan
• Iou Egg: nan
• Iou Apple: nan
• Iou Date: nan
• Iou Apricot: nan
• Iou Avocado: nan
• Iou Banana: nan
• Iou Strawberry: nan
• Iou Cherry: nan
• Iou Blueberry: nan
• Iou Raspberry: nan
• Iou Mango: nan
• Iou Olives: nan
• Iou Peach: 0.0
• Iou Lemon: nan
• Iou Pear: nan
• Iou Fig: nan
• Iou Pineapple: nan
• Iou Grape: nan
• Iou Kiwi: nan
• Iou Melon: nan
• Iou Orange: 0.0
• Iou Watermelon: nan
• Iou Steak: 0.1109
• Iou Pork: 0.2326
• Iou Chicken duck: 0.1176
• Iou Sausage: 0.0
• Iou Fried meat: nan
• Iou Lamb: 0.0
• Iou Sauce: 0.0
• Iou Crab: nan
• Iou Fish: nan
• Iou Shellfish: 0.0
• Iou Shrimp: 0.0
• Iou Soup: 0.0
• Iou Bread: 0.0065
• Iou Corn: 0.0
• Iou Hamburg: nan
• Iou Pizza: nan
• Iou hanamaki baozi: 0.0
• Iou Wonton dumplings: nan
• Iou Pasta: nan
• Iou Noodles: 0.1944
• Iou Rice: 0.2630
• Iou Pie: 0.0
• Iou Tofu: 0.0
• Iou Eggplant: nan
• Iou Potato: 0.2078
• Iou Garlic: nan
• Iou Cauliflower: 0.0
• Iou Tomato: 0.0283
• Iou Kelp: nan
• Iou Seaweed: nan
• Iou Spring onion: 0.0
• Iou Rape: 0.0
• Iou Ginger: nan
• Iou Okra: 0.0
• Iou Lettuce: 0.0010
• Iou Pumpkin: nan
• Iou Cucumber: 0.1036
• Iou White radish: 0.0
• Iou Carrot: 0.4668
• Iou Asparagus: nan
• Iou Bamboo shoots: nan
• Iou Broccoli: 0.3830
• Iou Celery stick: 0.0400
• Iou Cilantro mint: 0.0
• Iou Snow peas: nan
• Iou cabbage: nan
• Iou Bean sprouts: nan
• Iou Onion: 0.0073
• Iou Pepper: nan
• Iou Green beans: nan
• Iou French beans: nan
• Iou King oyster mushroom: nan
• Iou Shiitake: nan
• Iou Enoki mushroom: nan
• Iou Oyster mushroom: nan
• Iou White button mushroom: 0.0
• Iou Salad: nan
• Iou Other ingredients: 0.0

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: 0.0001
• train_batch_size: 12
• eval_batch_size: 12
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Framework versions

• Transformers 4.46.3
• Pytorch 2.5.1+cu121
• Datasets 3.2.0
• Tokenizers 0.20.3