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mit-b0-finetuned-sidewalk-semantic

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  1. README.md +50 -222
  2. config.json +36 -208
  3. model.safetensors +2 -2
  4. preprocessor_config.json +2 -2
  5. runs/Apr13_17-20-43_1de1ffca588a/events.out.tfevents.1713028852.1de1ffca588a.5437.0 +3 -0
  6. runs/Apr13_17-25-21_1de1ffca588a/events.out.tfevents.1713029133.1de1ffca588a.5437.1 +3 -0
  7. runs/Apr13_17-29-57_1de1ffca588a/events.out.tfevents.1713029406.1de1ffca588a.5437.2 +3 -0
  8. runs/Apr13_17-37-04_1de1ffca588a/events.out.tfevents.1713029837.1de1ffca588a.5437.3 +3 -0
  9. runs/Apr13_17-38-05_1de1ffca588a/events.out.tfevents.1713029893.1de1ffca588a.5437.4 +3 -0
  10. runs/Apr13_17-53-19_1de1ffca588a/events.out.tfevents.1713030809.1de1ffca588a.14450.0 +3 -0
  11. training_args.bin +1 -1
README.md CHANGED
@@ -15,218 +15,46 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.6018
19
- - Mean Iou: 0.0608
20
- - Mean Accuracy: 0.1072
21
- - Overall Accuracy: 0.3589
22
  - Accuracy Background: nan
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- - Accuracy Candy: nan
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- - Accuracy Egg tart: nan
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- - Accuracy French fries: 0.0813
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- - Accuracy Chocolate: 0.0
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- - Accuracy Biscuit: 0.0
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- - Accuracy Popcorn: nan
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- - Accuracy Pudding: 0.0
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- - Accuracy Ice cream: 0.3875
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- - Accuracy Cheese butter: 0.0
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- - Accuracy Cake: 0.0576
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- - Accuracy Wine: 0.0
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- - Accuracy Milkshake: 0.0
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- - Accuracy Coffee: 0.0
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- - Accuracy Juice: 0.0
37
- - Accuracy Milk: 0.0
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- - Accuracy Tea: 0.0
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- - Accuracy Almond: nan
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- - Accuracy Red beans: nan
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- - Accuracy Cashew: nan
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- - Accuracy Dried cranberries: nan
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- - Accuracy Soy: 0.0
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- - Accuracy Walnut: nan
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- - Accuracy Peanut: nan
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- - Accuracy Egg: 0.0
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- - Accuracy Apple: 0.0
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- - Accuracy Date: nan
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- - Accuracy Apricot: nan
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- - Accuracy Avocado: 0.0
51
- - Accuracy Banana: 0.0
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- - Accuracy Strawberry: 0.0771
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- - Accuracy Cherry: 0.0
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- - Accuracy Blueberry: 0.0
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- - Accuracy Raspberry: 0.0
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- - Accuracy Mango: 0.0
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- - Accuracy Olives: 0.0
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- - Accuracy Peach: nan
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- - Accuracy Lemon: 0.0515
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- - Accuracy Pear: nan
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- - Accuracy Fig: nan
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- - Accuracy Pineapple: 0.0
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- - Accuracy Grape: 0.0
64
- - Accuracy Kiwi: 0.0
65
- - Accuracy Melon: 0.0
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- - Accuracy Orange: 0.0
67
- - Accuracy Watermelon: 0.0
68
- - Accuracy Steak: 0.5884
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- - Accuracy Pork: 0.0003
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- - Accuracy Chicken duck: 0.6623
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- - Accuracy Sausage: 0.0
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- - Accuracy Fried meat: 0.0
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- - Accuracy Lamb: 0.0
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- - Accuracy Sauce: 0.1892
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- - Accuracy Crab: 0.0
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- - Accuracy Fish: 0.0
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- - Accuracy Shellfish: 0.0
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- - Accuracy Shrimp: 0.0
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- - Accuracy Soup: 0.0
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- - Accuracy Bread: 0.8206
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- - Accuracy Corn: 0.7645
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- - Accuracy Hamburg: nan
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- - Accuracy Pizza: 0.0
84
- - Accuracy hanamaki baozi: nan
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- - Accuracy Wonton dumplings: 0.0
86
- - Accuracy Pasta: 0.0
87
- - Accuracy Noodles: 0.0852
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- - Accuracy Rice: 0.6730
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- - Accuracy Pie: 0.0233
90
- - Accuracy Tofu: nan
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- - Accuracy Eggplant: nan
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- - Accuracy Potato: 0.4689
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- - Accuracy Garlic: 0.0
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- - Accuracy Cauliflower: 0.0
95
- - Accuracy Tomato: 0.6533
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- - Accuracy Kelp: nan
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- - Accuracy Seaweed: nan
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- - Accuracy Spring onion: 0.0
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- - Accuracy Rape: 0.0
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- - Accuracy Ginger: 0.0
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- - Accuracy Okra: nan
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- - Accuracy Lettuce: 0.0739
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- - Accuracy Pumpkin: 0.0
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- - Accuracy Cucumber: 0.0540
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- - Accuracy White radish: 0.0
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- - Accuracy Carrot: 0.8574
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- - Accuracy Asparagus: 0.0
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- - Accuracy Bamboo shoots: nan
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- - Accuracy Broccoli: 0.9624
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- - Accuracy Celery stick: 0.0
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- - Accuracy Cilantro mint: 0.0126
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- - Accuracy Snow peas: 0.0
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- - Accuracy cabbage: 0.0
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- - Accuracy Bean sprouts: nan
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- - Accuracy Onion: 0.0
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- - Accuracy Pepper: 0.0
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- - Accuracy Green beans: 0.0
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- - Accuracy French beans: 0.7100
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- - Accuracy King oyster mushroom: nan
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- - Accuracy Shiitake: 0.0
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- - Accuracy Enoki mushroom: nan
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- - Accuracy Oyster mushroom: nan
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- - Accuracy White button mushroom: 0.0
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- - Accuracy Salad: 0.0
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- - Accuracy Other ingredients: 0.0
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  - Iou Background: 0.0
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- - Iou Candy: nan
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- - Iou Egg tart: nan
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- - Iou French fries: 0.0723
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- - Iou Chocolate: 0.0
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- - Iou Biscuit: 0.0
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- - Iou Popcorn: nan
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- - Iou Pudding: 0.0
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- - Iou Ice cream: 0.2640
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- - Iou Cheese butter: 0.0
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- - Iou Cake: 0.0479
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- - Iou Wine: 0.0
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- - Iou Milkshake: 0.0
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- - Iou Coffee: 0.0
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- - Iou Juice: 0.0
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- - Iou Milk: 0.0
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- - Iou Tea: 0.0
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- - Iou Almond: nan
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- - Iou Red beans: nan
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- - Iou Cashew: nan
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- - Iou Dried cranberries: nan
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- - Iou Soy: 0.0
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- - Iou Walnut: nan
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- - Iou Peanut: nan
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- - Iou Egg: 0.0
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- - Iou Apple: 0.0
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- - Iou Date: nan
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- - Iou Apricot: nan
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- - Iou Avocado: 0.0
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- - Iou Banana: 0.0
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- - Iou Strawberry: 0.0755
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- - Iou Cherry: 0.0
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- - Iou Blueberry: 0.0
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- - Iou Raspberry: 0.0
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- - Iou Mango: 0.0
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- - Iou Olives: 0.0
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- - Iou Peach: nan
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- - Iou Lemon: 0.0473
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- - Iou Pear: nan
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- - Iou Fig: nan
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- - Iou Pineapple: 0.0
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- - Iou Grape: 0.0
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- - Iou Kiwi: 0.0
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- - Iou Melon: 0.0
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- - Iou Orange: 0.0
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- - Iou Watermelon: 0.0
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- - Iou Steak: 0.3273
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- - Iou Pork: 0.0003
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- - Iou Chicken duck: 0.2441
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- - Iou Sausage: 0.0
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- - Iou Fried meat: 0.0
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- - Iou Lamb: 0.0
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- - Iou Sauce: 0.1344
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- - Iou Crab: 0.0
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- - Iou Fish: 0.0
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- - Iou Shellfish: 0.0
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- - Iou Shrimp: 0.0
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- - Iou Soup: 0.0
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- - Iou Bread: 0.3788
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- - Iou Corn: 0.5467
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- - Iou Hamburg: nan
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- - Iou Pizza: 0.0
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- - Iou hanamaki baozi: nan
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- - Iou Wonton dumplings: 0.0
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- - Iou Pasta: 0.0
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- - Iou Noodles: 0.0849
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- - Iou Rice: 0.4561
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- - Iou Pie: 0.0216
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- - Iou Tofu: nan
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- - Iou Eggplant: nan
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- - Iou Potato: 0.1776
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- - Iou Garlic: 0.0
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- - Iou Cauliflower: 0.0
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- - Iou Tomato: 0.3098
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- - Iou Kelp: nan
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- - Iou Seaweed: nan
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- - Iou Spring onion: 0.0
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- - Iou Rape: 0.0
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- - Iou Ginger: 0.0
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- - Iou Okra: nan
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- - Iou Lettuce: 0.0659
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- - Iou Pumpkin: 0.0
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- - Iou Cucumber: 0.0510
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- - Iou White radish: 0.0
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- - Iou Carrot: 0.5396
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- - Iou Asparagus: 0.0
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- - Iou Bamboo shoots: nan
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- - Iou Broccoli: 0.4439
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- - Iou Celery stick: 0.0
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- - Iou Cilantro mint: 0.0124
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- - Iou Snow peas: 0.0
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- - Iou cabbage: 0.0
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- - Iou Bean sprouts: nan
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- - Iou Onion: 0.0
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- - Iou Pepper: 0.0
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- - Iou Green beans: 0.0
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- - Iou French beans: 0.4446
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- - Iou King oyster mushroom: nan
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- - Iou Shiitake: 0.0
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- - Iou Enoki mushroom: nan
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- - Iou Oyster mushroom: nan
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- - Iou White button mushroom: 0.0
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- - Iou Salad: 0.0
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- - Iou Other ingredients: 0.0
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  ## Model description
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@@ -257,18 +85,18 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Candy | Accuracy Egg tart | Accuracy French fries | Accuracy Chocolate | Accuracy Biscuit | Accuracy Popcorn | Accuracy Pudding | Accuracy Ice cream | Accuracy Cheese butter | Accuracy Cake | Accuracy Wine | Accuracy Milkshake | Accuracy Coffee | Accuracy Juice | Accuracy Milk | Accuracy Tea | Accuracy Almond | Accuracy Red beans | Accuracy Cashew | Accuracy Dried cranberries | Accuracy Soy | Accuracy Walnut | Accuracy Peanut | Accuracy Egg | Accuracy Apple | Accuracy Date | Accuracy Apricot | Accuracy Avocado | Accuracy Banana | Accuracy Strawberry | Accuracy Cherry | Accuracy Blueberry | Accuracy Raspberry | Accuracy Mango | Accuracy Olives | Accuracy Peach | Accuracy Lemon | Accuracy Pear | Accuracy Fig | Accuracy Pineapple | Accuracy Grape | Accuracy Kiwi | Accuracy Melon | Accuracy Orange | Accuracy Watermelon | Accuracy Steak | Accuracy Pork | Accuracy Chicken duck | Accuracy Sausage | Accuracy Fried meat | Accuracy Lamb | Accuracy Sauce | Accuracy Crab | Accuracy Fish | Accuracy Shellfish | Accuracy Shrimp | Accuracy Soup | Accuracy Bread | Accuracy Corn | Accuracy Hamburg | Accuracy Pizza | Accuracy hanamaki baozi | Accuracy Wonton dumplings | Accuracy Pasta | Accuracy Noodles | Accuracy Rice | Accuracy Pie | Accuracy Tofu | Accuracy Eggplant | Accuracy Potato | Accuracy Garlic | Accuracy Cauliflower | Accuracy Tomato | Accuracy Kelp | Accuracy Seaweed | Accuracy Spring onion | Accuracy Rape | Accuracy Ginger | Accuracy Okra | Accuracy Lettuce | Accuracy Pumpkin | Accuracy Cucumber | Accuracy White radish | Accuracy Carrot | Accuracy Asparagus | Accuracy Bamboo shoots | Accuracy Broccoli | Accuracy Celery stick | Accuracy Cilantro mint | Accuracy Snow peas | Accuracy cabbage | Accuracy Bean sprouts | Accuracy Onion | Accuracy Pepper | Accuracy Green beans | Accuracy French beans | Accuracy King oyster mushroom | Accuracy Shiitake | Accuracy Enoki mushroom | Accuracy Oyster mushroom | Accuracy White button mushroom | Accuracy Salad | Accuracy Other ingredients | Iou Background | Iou Candy | Iou Egg tart | Iou French fries | Iou Chocolate | Iou Biscuit | Iou Popcorn | Iou Pudding | Iou Ice cream | Iou Cheese butter | Iou Cake | Iou Wine | Iou Milkshake | Iou Coffee | Iou Juice | Iou Milk | Iou Tea | Iou Almond | Iou Red beans | Iou Cashew | Iou Dried cranberries | Iou Soy | Iou Walnut | Iou Peanut | Iou Egg | Iou Apple | Iou Date | Iou Apricot | Iou Avocado | Iou Banana | Iou Strawberry | Iou Cherry | Iou Blueberry | Iou Raspberry | Iou Mango | Iou Olives | Iou Peach | Iou Lemon | Iou Pear | Iou Fig | Iou Pineapple | Iou Grape | Iou Kiwi | Iou Melon | Iou Orange | Iou Watermelon | Iou Steak | Iou Pork | Iou Chicken duck | Iou Sausage | Iou Fried meat | Iou Lamb | Iou Sauce | Iou Crab | Iou Fish | Iou Shellfish | Iou Shrimp | Iou Soup | Iou Bread | Iou Corn | Iou Hamburg | Iou Pizza | Iou hanamaki baozi | Iou Wonton dumplings | Iou Pasta | Iou Noodles | Iou Rice | Iou Pie | Iou Tofu | Iou Eggplant | Iou Potato | Iou Garlic | Iou Cauliflower | Iou Tomato | Iou Kelp | Iou Seaweed | Iou Spring onion | Iou Rape | Iou Ginger | Iou Okra | Iou Lettuce | Iou Pumpkin | Iou Cucumber | Iou White radish | Iou Carrot | Iou Asparagus | Iou Bamboo shoots | Iou Broccoli | Iou Celery stick | Iou Cilantro mint | Iou Snow peas | Iou cabbage | Iou Bean sprouts | Iou Onion | Iou Pepper | Iou Green beans | Iou French beans | Iou King oyster mushroom | Iou Shiitake | Iou Enoki mushroom | Iou Oyster mushroom | Iou White button mushroom | Iou Salad | Iou Other ingredients |
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- | 2.9942 | 1.0 | 130 | 2.9276 | 0.0157 | 0.0439 | 0.1923 | nan | nan | nan | 0.0074 | 0.0 | 0.0051 | nan | 0.0 | 0.0043 | 0.0 | 0.0022 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0004 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0674 | 0.0254 | 0.6035 | 0.0 | 0.0 | 0.0 | 0.0005 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7081 | 0.1418 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0056 | 0.0034 | nan | nan | 0.3157 | 0.0 | 0.0 | 0.0372 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0248 | 0.0 | 0.0047 | 0.0 | 0.5461 | 0.0 | nan | 0.8757 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0005 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0071 | 0.0 | 0.0048 | nan | 0.0 | 0.0042 | 0.0 | 0.0021 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | 0.0 | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0004 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0567 | 0.0200 | 0.1814 | 0.0 | 0.0 | 0.0 | 0.0005 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2348 | 0.0879 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0055 | 0.0033 | nan | 0.0 | 0.0754 | 0.0 | 0.0 | 0.0343 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0204 | 0.0 | 0.0046 | 0.0 | 0.2160 | 0.0 | nan | 0.3144 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0005 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 |
263
- | 2.499 | 2.0 | 260 | 2.4282 | 0.0234 | 0.0566 | 0.2411 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0019 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3184 | 0.0098 | 0.6748 | 0.0 | 0.0 | 0.0 | 0.0050 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8102 | 0.3264 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0123 | 0.0000 | nan | nan | 0.2648 | 0.0 | 0.0 | 0.1984 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0058 | 0.0 | 0.0018 | 0.0 | 0.7864 | 0.0 | nan | 0.9428 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0002 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0019 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2004 | 0.0085 | 0.1904 | 0.0 | 0.0 | 0.0 | 0.0049 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2533 | 0.2684 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0120 | 0.0000 | nan | nan | 0.0798 | 0.0 | 0.0 | 0.1345 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0056 | 0.0 | 0.0018 | 0.0 | 0.3549 | 0.0 | nan | 0.3067 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0002 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 |
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- | 2.2864 | 3.0 | 390 | 2.0222 | 0.0315 | 0.0668 | 0.2639 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0274 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0009 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4212 | 0.0074 | 0.5674 | 0.0 | 0.0 | 0.0 | 0.0278 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8271 | 0.6221 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.1007 | 0.0001 | nan | nan | 0.4216 | 0.0 | 0.0 | 0.3613 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0030 | 0.0 | 0.0 | 0.0 | 0.7950 | 0.0 | nan | 0.9375 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0231 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0264 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0009 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2271 | 0.0069 | 0.1974 | 0.0 | 0.0 | 0.0 | 0.0258 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2921 | 0.5016 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0907 | 0.0001 | nan | nan | 0.1111 | 0.0 | 0.0 | 0.1978 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0029 | 0.0 | 0.0 | 0.0 | 0.4319 | 0.0 | nan | 0.3234 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0227 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 |
265
- | 1.905 | 4.0 | 520 | 1.9140 | 0.0385 | 0.0788 | 0.2991 | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.1371 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0036 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0001 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4552 | 0.0005 | 0.6890 | 0.0 | 0.0 | 0.0 | 0.0519 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7991 | 0.7101 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.4152 | 0.0 | nan | nan | 0.4644 | 0.0 | 0.0 | 0.3607 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0009 | 0.0 | 0.0 | 0.0 | 0.8803 | 0.0 | nan | 0.9656 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.1374 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.1285 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0036 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0001 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2663 | 0.0005 | 0.2170 | 0.0 | 0.0 | 0.0 | 0.0471 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3228 | 0.5485 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.3385 | 0.0 | nan | nan | 0.1305 | 0.0 | 0.0 | 0.1787 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0009 | 0.0 | 0.0 | 0.0 | 0.3855 | 0.0 | nan | 0.3162 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.1204 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 |
266
- | 2.0345 | 5.0 | 650 | 1.7634 | 0.0460 | 0.0899 | 0.3236 | nan | nan | nan | 0.0024 | 0.0 | 0.0 | nan | 0.0 | 0.2167 | 0.0 | 0.0012 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0122 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0003 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5472 | 0.0002 | 0.6359 | 0.0 | 0.0 | 0.0 | 0.1236 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8137 | 0.7439 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0012 | 0.5087 | 0.0033 | nan | nan | 0.5049 | 0.0 | 0.0 | 0.5107 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0163 | 0.0 | 0.0014 | 0.0 | 0.8746 | 0.0 | nan | 0.9726 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.4339 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0024 | 0.0 | 0.0 | nan | 0.0 | 0.1786 | 0.0 | 0.0011 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0122 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0003 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2872 | 0.0002 | 0.2219 | 0.0 | 0.0 | 0.0 | 0.1041 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3577 | 0.5178 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0012 | 0.3844 | 0.0032 | nan | nan | 0.1361 | 0.0 | 0.0 | 0.2411 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0154 | 0.0 | 0.0013 | 0.0 | 0.4571 | 0.0 | nan | 0.3375 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.3233 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 |
267
- | 1.9358 | 6.0 | 780 | 1.6950 | 0.0521 | 0.0978 | 0.3395 | nan | nan | nan | 0.0177 | 0.0 | 0.0 | nan | 0.0 | 0.3227 | 0.0 | 0.0020 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0294 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0053 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5861 | 0.0009 | 0.6474 | 0.0 | 0.0 | 0.0 | 0.1770 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7995 | 0.7529 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0127 | 0.5519 | 0.0023 | nan | nan | 0.4938 | 0.0 | 0.0 | 0.6809 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0349 | 0.0 | 0.0074 | 0.0 | 0.8447 | 0.0 | nan | 0.9651 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.5930 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0166 | 0.0 | 0.0 | nan | 0.0 | 0.2424 | 0.0 | 0.0019 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0293 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0051 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3126 | 0.0009 | 0.2285 | 0.0 | 0.0 | 0.0 | 0.1337 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3654 | 0.5170 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0127 | 0.4107 | 0.0022 | nan | nan | 0.1435 | 0.0 | 0.0 | 0.2982 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0309 | 0.0 | 0.0073 | 0.0 | 0.5130 | 0.0 | nan | 0.3837 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.4059 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 |
268
- | 2.2618 | 7.0 | 910 | 1.6316 | 0.0561 | 0.1029 | 0.3512 | nan | nan | nan | 0.0282 | 0.0 | 0.0 | nan | 0.0 | 0.3805 | 0.0 | 0.0306 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0516 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0298 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6237 | 0.0010 | 0.6296 | 0.0 | 0.0 | 0.0 | 0.1887 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8137 | 0.7823 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0344 | 0.6540 | 0.0278 | nan | nan | 0.4327 | 0.0 | 0.0 | 0.6121 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0641 | 0.0 | 0.0294 | 0.0 | 0.8738 | 0.0 | nan | 0.9581 | 0.0 | 0.0014 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.6761 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0251 | 0.0 | 0.0 | nan | 0.0 | 0.2635 | 0.0 | 0.0262 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0511 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0284 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3261 | 0.0010 | 0.2476 | 0.0 | 0.0 | 0.0 | 0.1381 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3627 | 0.5086 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0344 | 0.4473 | 0.0260 | nan | nan | 0.1719 | 0.0 | 0.0 | 0.2870 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0571 | 0.0 | 0.0288 | 0.0 | 0.4822 | 0.0 | nan | 0.4253 | 0.0 | 0.0014 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.4380 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 |
269
- | 1.7526 | 8.0 | 1040 | 1.6168 | 0.0587 | 0.1057 | 0.3536 | nan | nan | nan | 0.0762 | 0.0 | 0.0 | nan | 0.0 | 0.3490 | 0.0 | 0.0232 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0762 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0573 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6438 | 0.0011 | 0.6572 | 0.0 | 0.0 | 0.0 | 0.1949 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7777 | 0.7634 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0454 | 0.5992 | 0.0115 | nan | nan | 0.5007 | 0.0 | 0.0 | 0.6932 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.1100 | 0.0 | 0.0445 | 0.0 | 0.8527 | 0.0 | nan | 0.9651 | 0.0 | 0.0086 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.6843 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0716 | 0.0 | 0.0 | nan | 0.0 | 0.2649 | 0.0 | 0.0206 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0752 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0524 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3209 | 0.0011 | 0.2411 | 0.0 | 0.0 | 0.0 | 0.1354 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3954 | 0.5393 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0454 | 0.4272 | 0.0111 | nan | nan | 0.1722 | 0.0 | 0.0 | 0.2829 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0850 | 0.0 | 0.0433 | 0.0 | 0.5333 | 0.0 | nan | 0.4235 | 0.0 | 0.0085 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.4284 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 |
270
- | 1.7176 | 9.0 | 1170 | 1.6061 | 0.0598 | 0.1068 | 0.3582 | nan | nan | nan | 0.0637 | 0.0 | 0.0 | nan | 0.0 | 0.3684 | 0.0 | 0.0268 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0759 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0600 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6416 | 0.0003 | 0.6461 | 0.0 | 0.0 | 0.0 | 0.1933 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8102 | 0.7791 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0672 | 0.6338 | 0.0135 | nan | nan | 0.5045 | 0.0 | 0.0 | 0.6435 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.1030 | 0.0 | 0.0538 | 0.0 | 0.8572 | 0.0 | nan | 0.9677 | 0.0 | 0.0108 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.7011 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0573 | 0.0 | 0.0 | nan | 0.0 | 0.2684 | 0.0 | 0.0241 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0749 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0540 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3269 | 0.0003 | 0.2425 | 0.0 | 0.0 | 0.0 | 0.1367 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3843 | 0.5203 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0671 | 0.4458 | 0.0130 | nan | nan | 0.1695 | 0.0 | 0.0 | 0.3028 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0844 | 0.0 | 0.0508 | 0.0 | 0.5497 | 0.0 | nan | 0.4357 | 0.0 | 0.0106 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.4425 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 |
271
- | 1.5898 | 10.0 | 1300 | 1.6018 | 0.0608 | 0.1072 | 0.3589 | nan | nan | nan | 0.0813 | 0.0 | 0.0 | nan | 0.0 | 0.3875 | 0.0 | 0.0576 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0771 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0515 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5884 | 0.0003 | 0.6623 | 0.0 | 0.0 | 0.0 | 0.1892 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8206 | 0.7645 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0852 | 0.6730 | 0.0233 | nan | nan | 0.4689 | 0.0 | 0.0 | 0.6533 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0739 | 0.0 | 0.0540 | 0.0 | 0.8574 | 0.0 | nan | 0.9624 | 0.0 | 0.0126 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.7100 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0723 | 0.0 | 0.0 | nan | 0.0 | 0.2640 | 0.0 | 0.0479 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0755 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0473 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3273 | 0.0003 | 0.2441 | 0.0 | 0.0 | 0.0 | 0.1344 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3788 | 0.5467 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0849 | 0.4561 | 0.0216 | nan | nan | 0.1776 | 0.0 | 0.0 | 0.3098 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.0659 | 0.0 | 0.0510 | 0.0 | 0.5396 | 0.0 | nan | 0.4439 | 0.0 | 0.0124 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.4446 | nan | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 |
272
 
273
 
274
  ### Framework versions
 
15
 
16
  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
17
  It achieves the following results on the evaluation set:
18
+ - Loss: 0.3204
19
+ - Mean Iou: 0.3879
20
+ - Mean Accuracy: 0.4943
21
+ - Overall Accuracy: 0.7036
22
  - Accuracy Background: nan
23
+ - Accuracy Hat: 0.0
24
+ - Accuracy Hair: 0.8304
25
+ - Accuracy Sunglasses: 0.0
26
+ - Accuracy Upper-clothes: 0.8535
27
+ - Accuracy Skirt: 0.6956
28
+ - Accuracy Pants: 0.8303
29
+ - Accuracy Dress: 0.4990
30
+ - Accuracy Belt: 0.0
31
+ - Accuracy Left-shoe: 0.1708
32
+ - Accuracy Right-shoe: 0.3445
33
+ - Accuracy Face: 0.8594
34
+ - Accuracy Left-leg: 0.7149
35
+ - Accuracy Right-leg: 0.7322
36
+ - Accuracy Left-arm: 0.6598
37
+ - Accuracy Right-arm: 0.6786
38
+ - Accuracy Bag: 0.5344
39
+ - Accuracy Scarf: 0.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
40
  - Iou Background: 0.0
41
+ - Iou Hat: 0.0
42
+ - Iou Hair: 0.7126
43
+ - Iou Sunglasses: 0.0
44
+ - Iou Upper-clothes: 0.6681
45
+ - Iou Skirt: 0.5240
46
+ - Iou Pants: 0.6700
47
+ - Iou Dress: 0.4029
48
+ - Iou Belt: 0.0
49
+ - Iou Left-shoe: 0.1600
50
+ - Iou Right-shoe: 0.2739
51
+ - Iou Face: 0.7169
52
+ - Iou Left-leg: 0.5757
53
+ - Iou Right-leg: 0.6008
54
+ - Iou Left-arm: 0.5868
55
+ - Iou Right-arm: 0.6012
56
+ - Iou Bag: 0.4902
57
+ - Iou Scarf: 0.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
58
 
59
  ## Model description
60
 
 
85
 
86
  ### Training results
87
 
88
+ | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Hat | Accuracy Hair | Accuracy Sunglasses | Accuracy Upper-clothes | Accuracy Skirt | Accuracy Pants | Accuracy Dress | Accuracy Belt | Accuracy Left-shoe | Accuracy Right-shoe | Accuracy Face | Accuracy Left-leg | Accuracy Right-leg | Accuracy Left-arm | Accuracy Right-arm | Accuracy Bag | Accuracy Scarf | Iou Background | Iou Hat | Iou Hair | Iou Sunglasses | Iou Upper-clothes | Iou Skirt | Iou Pants | Iou Dress | Iou Belt | Iou Left-shoe | Iou Right-shoe | Iou Face | Iou Left-leg | Iou Right-leg | Iou Left-arm | Iou Right-arm | Iou Bag | Iou Scarf |
89
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:------------:|:-------------:|:-------------------:|:----------------------:|:--------------:|:--------------:|:--------------:|:-------------:|:------------------:|:-------------------:|:-------------:|:-----------------:|:------------------:|:-----------------:|:------------------:|:------------:|:--------------:|:--------------:|:-------:|:--------:|:--------------:|:-----------------:|:---------:|:---------:|:---------:|:--------:|:-------------:|:--------------:|:--------:|:------------:|:-------------:|:------------:|:-------------:|:-------:|:---------:|
90
+ | 1.5584 | 1.0 | 100 | 1.4751 | 0.1357 | 0.2382 | 0.4526 | nan | 0.0 | 0.8771 | 0.0 | 0.8883 | 0.0443 | 0.7221 | 0.0035 | 0.0 | 0.0187 | 0.0055 | 0.2572 | 0.5884 | 0.5612 | 0.0822 | 0.0013 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5636 | 0.0 | 0.3813 | 0.0433 | 0.3814 | 0.0035 | 0.0 | 0.0182 | 0.0055 | 0.2523 | 0.3602 | 0.3582 | 0.0746 | 0.0013 | 0.0 | 0.0 |
91
+ | 1.1073 | 2.0 | 200 | 1.0997 | 0.2194 | 0.3308 | 0.5583 | nan | 0.0 | 0.9122 | 0.0 | 0.8933 | 0.5007 | 0.6982 | 0.1416 | 0.0 | 0.0076 | 0.0436 | 0.7573 | 0.6194 | 0.7115 | 0.2770 | 0.0608 | 0.0012 | 0.0 | 0.0 | 0.0 | 0.6610 | 0.0 | 0.4693 | 0.3429 | 0.5229 | 0.1246 | 0.0 | 0.0076 | 0.0416 | 0.6491 | 0.4038 | 0.4308 | 0.2338 | 0.0605 | 0.0012 | 0.0 |
92
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101
 
102
  ### Framework versions
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