End of training

README.md

@@ -1,199 +1,275 @@
 ---
 library_name: transformers
-tags: []
+license: other
+base_model: nvidia/mit-b0
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+model-index:
+- name: segformer-b0-finetuned-Eduardo-food103-GOOGLE100
+  results: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
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-## Uses
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-### Direct Use
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-## How to Get Started with the Model
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-## Training Details
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-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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-[More Information Needed]
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
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-## Technical Specifications [optional]
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+<!-- 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. -->
+
+# segformer-b0-finetuned-Eduardo-food103-GOOGLE100
+
+This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the EduardoPacheco/FoodSeg103 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.1878
+- Mean Iou: 0.0726
+- Mean Accuracy: 0.1651
+- Overall Accuracy: 0.2308
+- Accuracy Background: nan
+- Accuracy Candy: nan
+- Accuracy Egg tart: nan
+- Accuracy French fries: 0.0
+- Accuracy Chocolate: 0.0
+- Accuracy Biscuit: 0.0
+- Accuracy Popcorn: nan
+- Accuracy Pudding: nan
+- Accuracy Ice cream: 0.0
+- Accuracy Cheese butter: 0.0
+- Accuracy Cake: 0.0
+- Accuracy Wine: nan
+- Accuracy Milkshake: nan
+- Accuracy Coffee: nan
+- Accuracy Juice: nan
+- 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: 0.0
+- Accuracy Apple: nan
+- Accuracy Date: nan
+- Accuracy Apricot: nan
+- Accuracy Avocado: 0.0
+- Accuracy Banana: 0.0
+- Accuracy Strawberry: 0.0028
+- Accuracy Cherry: nan
+- Accuracy Blueberry: nan
+- Accuracy Raspberry: nan
+- Accuracy Mango: nan
+- Accuracy Olives: nan
+- Accuracy Peach: nan
+- Accuracy Lemon: 0.0
+- Accuracy Pear: nan
+- Accuracy Fig: nan
+- Accuracy Pineapple: 0.0
+- Accuracy Grape: nan
+- Accuracy Kiwi: nan
+- Accuracy Melon: nan
+- Accuracy Orange: 0.0129
+- Accuracy Watermelon: nan
+- Accuracy Steak: 0.2722
+- Accuracy Pork: 0.0
+- Accuracy Chicken duck: 0.4405
+- Accuracy Sausage: nan
+- Accuracy Fried meat: 0.0
+- Accuracy Lamb: nan
+- Accuracy Sauce: 0.0336
+- Accuracy Crab: nan
+- Accuracy Fish: 0.0
+- Accuracy Shellfish: nan
+- Accuracy Shrimp: nan
+- Accuracy Soup: 0.0
+- Accuracy Bread: 0.9251
+- Accuracy Corn: 0.8612
+- Accuracy Hamburg: nan
+- Accuracy Pizza: nan
+- Accuracy  hanamaki baozi: nan
+- Accuracy Wonton dumplings: nan
+- Accuracy Pasta: nan
+- Accuracy Noodles: nan
+- Accuracy Rice: 0.9597
+- Accuracy Pie: 0.0803
+- Accuracy Tofu: nan
+- Accuracy Eggplant: nan
+- Accuracy Potato: 0.0025
+- Accuracy Garlic: nan
+- Accuracy Cauliflower: 0.0
+- Accuracy Tomato: 0.9197
+- Accuracy Kelp: nan
+- Accuracy Seaweed: nan
+- Accuracy Spring onion: nan
+- Accuracy Rape: nan
+- Accuracy Ginger: nan
+- Accuracy Okra: nan
+- Accuracy Lettuce: 0.0
+- Accuracy Pumpkin: nan
+- Accuracy Cucumber: 0.0
+- Accuracy White radish: nan
+- Accuracy Carrot: 0.7713
+- Accuracy Asparagus: 0.0
+- Accuracy Bamboo shoots: nan
+- Accuracy Broccoli: 0.8238
+- Accuracy Celery stick: 0.0
+- Accuracy Cilantro mint: 0.0042
+- Accuracy Snow peas: nan
+- Accuracy  cabbage: nan
+- Accuracy Bean sprouts: nan
+- Accuracy Onion: 0.0
+- Accuracy Pepper: nan
+- Accuracy Green beans: nan
+- Accuracy French beans: 0.0
+- 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: nan
+- Iou Background: 0.0
+- Iou Candy: nan
+- Iou Egg tart: nan
+- Iou French fries: 0.0
+- Iou Chocolate: 0.0
+- 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: nan
+- 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: 0.0
+- Iou Apple: nan
+- Iou Date: nan
+- Iou Apricot: nan
+- Iou Avocado: 0.0
+- Iou Banana: 0.0
+- Iou Strawberry: 0.0028
+- Iou Cherry: nan
+- Iou Blueberry: nan
+- Iou Raspberry: nan
+- Iou Mango: nan
+- Iou Olives: nan
+- Iou Peach: nan
+- Iou Lemon: 0.0
+- Iou Pear: nan
+- Iou Fig: nan
+- Iou Pineapple: 0.0
+- Iou Grape: nan
+- Iou Kiwi: nan
+- Iou Melon: nan
+- Iou Orange: 0.0124
+- Iou Watermelon: nan
+- Iou Steak: 0.1972
+- Iou Pork: 0.0
+- Iou Chicken duck: 0.1551
+- Iou Sausage: nan
+- Iou Fried meat: 0.0
+- Iou Lamb: nan
+- Iou Sauce: 0.0217
+- Iou Crab: nan
+- Iou Fish: 0.0
+- Iou Shellfish: nan
+- Iou Shrimp: nan
+- Iou Soup: 0.0
+- Iou Bread: 0.3244
+- Iou Corn: 0.8612
+- Iou Hamburg: nan
+- Iou Pizza: nan
+- Iou  hanamaki baozi: nan
+- Iou Wonton dumplings: nan
+- Iou Pasta: nan
+- Iou Noodles: nan
+- Iou Rice: 0.1760
+- Iou Pie: 0.0180
+- Iou Tofu: nan
+- Iou Eggplant: nan
+- Iou Potato: 0.0011
+- Iou Garlic: nan
+- Iou Cauliflower: 0.0
+- Iou Tomato: 0.1271
+- Iou Kelp: nan
+- Iou Seaweed: nan
+- Iou Spring onion: nan
+- Iou Rape: nan
+- Iou Ginger: nan
+- Iou Okra: nan
+- Iou Lettuce: 0.0
+- Iou Pumpkin: nan
+- Iou Cucumber: 0.0
+- Iou White radish: nan
+- Iou Carrot: 0.6337
+- Iou Asparagus: 0.0
+- Iou Bamboo shoots: nan
+- Iou Broccoli: 0.3690
+- Iou Celery stick: 0.0
+- Iou Cilantro mint: 0.0042
+- Iou Snow peas: nan
+- Iou  cabbage: nan
+- Iou Bean sprouts: nan
+- Iou Onion: 0.0
+- Iou Pepper: 0.0
+- Iou Green beans: nan
+- Iou French beans: 0.0
+- 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: nan
+
+## 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: 6e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- 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
+
+| 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:-----------------:|:---------------------:|:------------------:|:----------------:|:----------------:|:----------------:|:------------------:|:----------------------:|:-------------:|:-------------:|:------------------:|:---------------:|:--------------:|:-------------:|:------------:|:---------------:|:------------------:|:---------------:|:--------------------------:|:------------:|:---------------:|:---------------:|:------------:|:--------------:|:-------------:|:----------------:|:----------------:|:---------------:|:-------------------:|:---------------:|:------------------:|:------------------:|:--------------:|:---------------:|:--------------:|:--------------:|:-------------:|:------------:|:------------------:|:--------------:|:-------------:|:--------------:|:---------------:|:-------------------:|:--------------:|:-------------:|:---------------------:|:----------------:|:-------------------:|:-------------:|:--------------:|:-------------:|:-------------:|:------------------:|:---------------:|:-------------:|:--------------:|:-------------:|:----------------:|:--------------:|:------------------------:|:-------------------------:|:--------------:|:----------------:|:-------------:|:------------:|:-------------:|:-----------------:|:---------------:|:---------------:|:--------------------:|:---------------:|:-------------:|:----------------:|:---------------------:|:-------------:|:---------------:|:-------------:|:----------------:|:----------------:|:-----------------:|:---------------------:|:---------------:|:------------------:|:----------------------:|:-----------------:|:---------------------:|:----------------------:|:------------------:|:-----------------:|:---------------------:|:--------------:|:---------------:|:--------------------:|:---------------------:|:-----------------------------:|:-----------------:|:-----------------------:|:------------------------:|:------------------------------:|:--------------:|:--------------------------:|:--------------:|:---------:|:------------:|:----------------:|:-------------:|:-----------:|:-----------:|:-----------:|:-------------:|:-----------------:|:--------:|:--------:|:-------------:|:----------:|:---------:|:--------:|:-------:|:----------:|:-------------:|:----------:|:---------------------:|:-------:|:----------:|:----------:|:-------:|:---------:|:--------:|:-----------:|:-----------:|:----------:|:--------------:|:----------:|:-------------:|:-------------:|:---------:|:----------:|:---------:|:---------:|:--------:|:-------:|:-------------:|:---------:|:--------:|:---------:|:----------:|:--------------:|:---------:|:--------:|:----------------:|:-----------:|:--------------:|:--------:|:---------:|:--------:|:--------:|:-------------:|:----------:|:--------:|:---------:|:--------:|:-----------:|:---------:|:-------------------:|:--------------------:|:---------:|:-----------:|:--------:|:-------:|:--------:|:------------:|:----------:|:----------:|:---------------:|:----------:|:--------:|:-----------:|:----------------:|:--------:|:----------:|:--------:|:-----------:|:-----------:|:------------:|:----------------:|:----------:|:-------------:|:-----------------:|:------------:|:----------------:|:-----------------:|:-------------:|:------------:|:----------------:|:---------:|:----------:|:---------------:|:----------------:|:------------------------:|:------------:|:------------------:|:-------------------:|:-------------------------:|:---------:|:---------------------:|
+| 3.1829        | 10.0  | 100  | 2.9969          | 0.0442   | 0.1289        | 0.1903           | nan                 | nan            | nan               | 0.0                   | 0.0                | 0.0              | nan              | nan              | 0.0011             | 0.0                    | 0.0           | nan           | nan                | nan             | nan            | nan           | nan          | nan             | nan                | nan             | nan                        | nan          | nan             | nan             | 0.0          | nan            | nan           | nan              | 0.0              | 0.0             | 0.0149              | nan             | nan                | nan                | nan            | nan             | nan            | 0.0            | nan           | nan          | 0.0                | nan            | nan           | nan            | 0.0439          | nan                 | 0.0011         | 0.0           | 0.2629                | nan              | 0.0                 | nan           | 0.0226         | nan           | 0.0           | nan                | nan             | 0.0           | 0.9498         | 0.7303        | nan              | nan            | nan                      | nan                       | nan            | nan              | 0.5457        | 0.0050       | nan           | nan               | 0.0             | nan             | 0.0                  | 0.5701          | nan           | nan              | nan                   | nan           | nan             | nan           | 0.0120           | nan              | 0.0               | nan                   | 0.5662          | 0.0                | nan                    | 0.7966            | 0.0                   | 0.0018                 | nan                | nan               | nan                   | 0.0            | nan             | nan                  | 0.2437                | nan                           | nan               | nan                     | nan                      | 0.0021                         | nan            | nan                        | 0.0            | nan       | nan          | 0.0              | 0.0           | 0.0         | nan         | nan         | 0.0011        | 0.0               | 0.0      | nan      | nan           | nan        | 0.0       | nan      | nan     | nan        | nan           | nan        | nan                   | nan     | nan        | nan        | 0.0     | nan       | nan      | nan         | 0.0         | 0.0        | 0.0143         | nan        | nan           | nan           | nan       | nan        | nan       | 0.0       | nan      | nan     | 0.0           | nan       | nan      | nan       | 0.0167     | nan            | 0.0008    | 0.0      | 0.0953           | 0.0         | 0.0            | nan      | 0.0210    | nan      | 0.0      | 0.0           | 0.0        | 0.0      | 0.1913    | 0.7303   | nan         | nan       | nan                 | nan                  | nan       | 0.0         | 0.0686   | 0.0032  | nan      | nan          | 0.0        | nan        | 0.0             | 0.1095     | nan      | 0.0         | nan              | nan      | nan        | nan      | 0.0074      | nan         | 0.0          | nan              | 0.4240     | 0.0           | nan               | 0.3995       | 0.0              | 0.0018            | nan           | nan          | nan              | 0.0       | 0.0        | 0.0             | 0.0352           | nan                      | nan          | nan                | 0.0                 | 0.0020                    | 0.0       | nan                   |
+| 2.4226        | 20.0  | 200  | 2.4832          | 0.0572   | 0.1463        | 0.2036           | nan                 | nan            | nan               | 0.0                   | 0.0                | 0.0              | nan              | nan              | 0.0038             | 0.0                    | 0.0           | nan           | nan                | nan             | nan            | nan           | nan          | nan             | nan                | nan             | nan                        | nan          | nan             | nan             | 0.0          | nan            | nan           | nan              | 0.0              | 0.0             | 0.0006              | nan             | nan                | nan                | nan            | nan             | nan            | 0.0            | nan           | nan          | 0.0                | nan            | nan           | nan            | 0.0517          | nan                 | 0.0            | 0.0           | 0.3696                | nan              | 0.0                 | nan           | 0.0362         | nan           | 0.0           | nan                | nan             | 0.0           | 0.9217         | 0.8128        | nan              | nan            | nan                      | nan                       | nan            | nan              | 0.9160        | 0.0170       | nan           | nan               | 0.0045          | nan             | 0.0                  | 0.8978          | nan           | nan              | nan                   | nan           | nan             | nan           | 0.0277           | nan              | 0.0               | nan                   | 0.5100          | 0.0                | nan                    | 0.8429            | 0.0                   | 0.0                    | nan                | nan               | nan                   | 0.0            | nan             | nan                  | 0.0005                | nan                           | nan               | nan                     | nan                      | 0.0                            | nan            | nan                        | 0.0            | nan       | nan          | 0.0              | 0.0           | 0.0         | nan         | nan         | 0.0038        | 0.0               | 0.0      | nan      | nan           | nan        | 0.0       | nan      | nan     | nan        | nan           | nan        | nan                   | nan     | nan        | nan        | 0.0     | nan       | nan      | nan         | 0.0         | 0.0        | 0.0006         | nan        | nan           | nan           | nan       | nan        | nan       | 0.0       | nan      | nan     | 0.0           | nan       | nan      | nan       | 0.0501     | nan            | 0.0       | 0.0      | 0.1509           | nan         | 0.0            | nan      | 0.0257    | nan      | 0.0      | nan           | nan        | 0.0      | 0.2285    | 0.8128   | nan         | nan       | nan                 | nan                  | nan       | nan         | 0.0974   | 0.0074  | nan      | nan          | 0.0045     | nan        | 0.0             | 0.1040     | nan      | nan         | nan              | nan      | nan        | nan      | 0.0170      | nan         | 0.0          | nan              | 0.3959     | 0.0           | nan               | 0.3894       | 0.0              | 0.0               | nan           | nan          | nan              | 0.0       | 0.0        | nan             | 0.0001           | nan                      | nan          | nan                | nan                 | 0.0                       | nan       | nan                   |
+| 2.0416        | 30.0  | 300  | 2.3190          | 0.0654   | 0.1581        | 0.2218           | nan                 | nan            | nan               | 0.0                   | 0.0                | 0.0              | nan              | nan              | 0.0008             | 0.0                    | 0.0           | nan           | nan                | nan             | nan            | nan           | nan          | nan             | nan                | nan             | nan                        | nan          | nan             | nan             | 0.0          | nan            | nan           | nan              | 0.0              | 0.0             | 0.0                 | nan             | nan                | nan                | nan            | nan             | nan            | 0.0            | nan           | nan          | 0.0                | nan            | nan           | nan            | 0.0011          | nan                 | 0.0561         | 0.0           | 0.4236                | nan              | 0.0                 | nan           | 0.0523         | nan           | 0.0           | nan                | nan             | 0.0           | 0.9272         | 0.8698        | nan              | nan            | nan                      | nan                       | nan            | nan              | 0.9557        | 0.0533       | nan           | nan               | 0.0029          | nan             | 0.0                  | 0.9382          | nan           | nan              | nan                   | nan           | nan             | nan           | 0.0539           | nan              | 0.0               | nan                   | 0.6930          | 0.0                | nan                    | 0.8183            | 0.0                   | 0.0036                 | nan                | nan               | nan                   | 0.0            | nan             | nan                  | 0.0001                | nan                           | nan               | nan                     | nan                      | 0.0                            | nan            | nan                        | 0.0            | nan       | nan          | 0.0              | 0.0           | 0.0         | nan         | nan         | 0.0008        | 0.0               | 0.0      | nan      | nan           | nan        | 0.0       | nan      | nan     | nan        | nan           | nan        | nan                   | nan     | nan        | nan        | 0.0     | nan       | nan      | nan         | 0.0         | 0.0        | 0.0            | nan        | nan           | nan           | nan       | nan        | nan       | 0.0       | nan      | nan     | 0.0           | nan       | nan      | nan       | 0.0011     | nan            | 0.0524    | 0.0      | 0.1551           | nan         | 0.0            | nan      | 0.0389    | nan      | 0.0      | nan           | nan        | 0.0      | 0.3134    | 0.8698   | nan         | nan       | nan                 | nan                  | nan       | nan         | 0.1152   | 0.0125  | nan      | nan          | 0.0019     | nan        | 0.0             | 0.1085     | nan      | nan         | nan              | nan      | nan        | nan      | 0.0408      | nan         | 0.0          | nan              | 0.5407     | 0.0           | nan               | 0.3600       | 0.0              | 0.0036            | nan           | nan          | nan              | 0.0       | 0.0        | nan             | 0.0000           | nan                      | nan          | nan                | nan                 | 0.0                       | nan       | nan                   |
+| 1.8426        | 40.0  | 400  | 2.2506          | 0.0690   | 0.1601        | 0.2263           | nan                 | nan            | nan               | 0.0                   | 0.0                | 0.0              | nan              | nan              | 0.0                | 0.0                    | 0.0           | nan           | nan                | nan             | nan            | nan           | nan          | nan             | nan                | nan             | nan                        | nan          | nan             | nan             | 0.0          | nan            | nan           | nan              | 0.0              | 0.0             | 0.0033              | nan             | nan                | nan                | nan            | nan             | nan            | 0.0            | nan           | nan          | 0.0                | nan            | nan           | nan            | 0.0001          | nan                 | 0.1714         | 0.0           | 0.4262                | nan              | 0.0                 | nan           | 0.0335         | nan           | 0.0           | nan                | nan             | 0.0           | 0.9367         | 0.8517        | nan              | nan            | nan                      | nan                       | nan            | nan              | 0.9650        | 0.0358       | nan           | nan               | 0.0             | nan             | 0.0                  | 0.9186          | nan           | nan              | nan                   | nan           | nan             | nan           | 0.0              | nan              | 0.0               | nan                   | 0.7250          | 0.0                | nan                    | 0.8542            | 0.0                   | 0.0030                 | nan                | nan               | nan                   | 0.0            | nan             | nan                  | 0.0                   | nan                           | nan               | nan                     | nan                      | 0.0                            | nan            | nan                        | 0.0            | nan       | nan          | 0.0              | 0.0           | 0.0         | nan         | nan         | 0.0           | 0.0               | 0.0      | nan      | nan           | nan        | 0.0       | nan      | nan     | nan        | nan           | nan        | nan                   | nan     | nan        | nan        | 0.0     | nan       | nan      | nan         | 0.0         | 0.0        | 0.0032         | nan        | nan           | nan           | nan       | nan        | nan       | 0.0       | nan      | nan     | 0.0           | nan       | nan      | nan       | 0.0001     | nan            | 0.1422    | 0.0      | 0.1508           | nan         | 0.0            | nan      | 0.0229    | nan      | 0.0      | nan           | nan        | 0.0      | 0.2999    | 0.8517   | nan         | nan       | nan                 | nan                  | nan       | nan         | 0.1834   | 0.0081  | nan      | nan          | 0.0        | nan        | 0.0             | 0.1171     | nan      | nan         | nan              | nan      | nan        | nan      | 0.0         | nan         | 0.0          | nan              | 0.6041     | 0.0           | nan               | 0.3724       | 0.0              | 0.0030            | nan           | nan          | nan              | 0.0       | 0.0        | nan             | 0.0              | nan                      | nan          | nan                | nan                 | 0.0                       | nan       | nan                   |
+| 1.7476        | 50.0  | 500  | 2.1878          | 0.0726   | 0.1651        | 0.2308           | nan                 | nan            | nan               | 0.0                   | 0.0                | 0.0              | nan              | nan              | 0.0                | 0.0                    | 0.0           | nan           | nan                | nan             | nan            | nan           | nan          | nan             | nan                | nan             | nan                        | nan          | nan             | nan             | 0.0          | nan            | nan           | nan              | 0.0              | 0.0             | 0.0028              | nan             | nan                | nan                | nan            | nan             | nan            | 0.0            | nan           | nan          | 0.0                | nan            | nan           | nan            | 0.0129          | nan                 | 0.2722         | 0.0           | 0.4405                | nan              | 0.0                 | nan           | 0.0336         | nan           | 0.0           | nan                | nan             | 0.0           | 0.9251         | 0.8612        | nan              | nan            | nan                      | nan                       | nan            | nan              | 0.9597        | 0.0803       | nan           | nan               | 0.0025          | nan             | 0.0                  | 0.9197          | nan           | nan              | nan                   | nan           | nan             | nan           | 0.0              | nan              | 0.0               | nan                   | 0.7713          | 0.0                | nan                    | 0.8238            | 0.0                   | 0.0042                 | nan                | nan               | nan                   | 0.0            | nan             | nan                  | 0.0                   | nan                           | nan               | nan                     | nan                      | 0.0                            | nan            | nan                        | 0.0            | nan       | nan          | 0.0              | 0.0           | 0.0         | nan         | nan         | 0.0           | 0.0               | 0.0      | nan      | nan           | nan        | 0.0       | nan      | nan     | nan        | nan           | nan        | nan                   | nan     | nan        | nan        | 0.0     | nan       | nan      | nan         | 0.0         | 0.0        | 0.0028         | nan        | nan           | nan           | nan       | nan        | nan       | 0.0       | nan      | nan     | 0.0           | nan       | nan      | nan       | 0.0124     | nan            | 0.1972    | 0.0      | 0.1551           | nan         | 0.0            | nan      | 0.0217    | nan      | 0.0      | nan           | nan        | 0.0      | 0.3244    | 0.8612   | nan         | nan       | nan                 | nan                  | nan       | nan         | 0.1760   | 0.0180  | nan      | nan          | 0.0011     | nan        | 0.0             | 0.1271     | nan      | nan         | nan              | nan      | nan        | nan      | 0.0         | nan         | 0.0          | nan              | 0.6337     | 0.0           | nan               | 0.3690       | 0.0              | 0.0042            | nan           | nan          | nan              | 0.0       | 0.0        | nan             | 0.0              | nan                      | nan          | nan                | nan                 | 0.0                       | nan       | nan                   |
+
+
+### Framework versions
+
+- Transformers 4.46.3
+- Pytorch 2.5.1+cu121
+- Datasets 3.2.0
+- Tokenizers 0.20.3

config.json

@@ -0,0 +1,282 @@
+{
+  "_name_or_path": "nvidia/mit-b0",
+  "architectures": [
+    "SegformerForSemanticSegmentation"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "classifier_dropout_prob": 0.1,
+  "decoder_hidden_size": 256,
+  "depths": [
+    2,
+    2,
+    2,
+    2
+  ],
+  "downsampling_rates": [
+    1,
+    4,
+    8,
+    16
+  ],
+  "drop_path_rate": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_sizes": [
+    32,
+    64,
+    160,
+    256
+  ],
+  "id2label": {
+    "0": "background",
+    "1": "candy",
+    "2": "egg tart",
+    "3": "french fries",
+    "4": "chocolate",
+    "5": "biscuit",
+    "6": "popcorn",
+    "7": "pudding",
+    "8": "ice cream",
+    "9": "cheese butter",
+    "10": "cake",
+    "11": "wine",
+    "12": "milkshake",
+    "13": "coffee",
+    "14": "juice",
+    "15": "milk",
+    "16": "tea",
+    "17": "almond",
+    "18": "red beans",
+    "19": "cashew",
+    "20": "dried cranberries",
+    "21": "soy",
+    "22": "walnut",
+    "23": "peanut",
+    "24": "egg",
+    "25": "apple",
+    "26": "date",
+    "27": "apricot",
+    "28": "avocado",
+    "29": "banana",
+    "30": "strawberry",
+    "31": "cherry",
+    "32": "blueberry",
+    "33": "raspberry",
+    "34": "mango",
+    "35": "olives",
+    "36": "peach",
+    "37": "lemon",
+    "38": "pear",
+    "39": "fig",
+    "40": "pineapple",
+    "41": "grape",
+    "42": "kiwi",
+    "43": "melon",
+    "44": "orange",
+    "45": "watermelon",
+    "46": "steak",
+    "47": "pork",
+    "48": "chicken duck",
+    "49": "sausage",
+    "50": "fried meat",
+    "51": "lamb",
+    "52": "sauce",
+    "53": "crab",
+    "54": "fish",
+    "55": "shellfish",
+    "56": "shrimp",
+    "57": "soup",
+    "58": "bread",
+    "59": "corn",
+    "60": "hamburg",
+    "61": "pizza",
+    "62": " hanamaki baozi",
+    "63": "wonton dumplings",
+    "64": "pasta",
+    "65": "noodles",
+    "66": "rice",
+    "67": "pie",
+    "68": "tofu",
+    "69": "eggplant",
+    "70": "potato",
+    "71": "garlic",
+    "72": "cauliflower",
+    "73": "tomato",
+    "74": "kelp",
+    "75": "seaweed",
+    "76": "spring onion",
+    "77": "rape",
+    "78": "ginger",
+    "79": "okra",
+    "80": "lettuce",
+    "81": "pumpkin",
+    "82": "cucumber",
+    "83": "white radish",
+    "84": "carrot",
+    "85": "asparagus",
+    "86": "bamboo shoots",
+    "87": "broccoli",
+    "88": "celery stick",
+    "89": "cilantro mint",
+    "90": "snow peas",
+    "91": " cabbage",
+    "92": "bean sprouts",
+    "93": "onion",
+    "94": "pepper",
+    "95": "green beans",
+    "96": "French beans",
+    "97": "king oyster mushroom",
+    "98": "shiitake",
+    "99": "enoki mushroom",
+    "100": "oyster mushroom",
+    "101": "white button mushroom",
+    "102": "salad",
+    "103": "other ingredients"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    " cabbage": 91,
+    " hanamaki baozi": 62,
+    "French beans": 96,
+    "almond": 17,
+    "apple": 25,
+    "apricot": 27,
+    "asparagus": 85,
+    "avocado": 28,
+    "background": 0,
+    "bamboo shoots": 86,
+    "banana": 29,
+    "bean sprouts": 92,
+    "biscuit": 5,
+    "blueberry": 32,
+    "bread": 58,
+    "broccoli": 87,
+    "cake": 10,
+    "candy": 1,
+    "carrot": 84,
+    "cashew": 19,
+    "cauliflower": 72,
+    "celery stick": 88,
+    "cheese butter": 9,
+    "cherry": 31,
+    "chicken duck": 48,
+    "chocolate": 4,
+    "cilantro mint": 89,
+    "coffee": 13,
+    "corn": 59,
+    "crab": 53,
+    "cucumber": 82,
+    "date": 26,
+    "dried cranberries": 20,
+    "egg": 24,
+    "egg tart": 2,
+    "eggplant": 69,
+    "enoki mushroom": 99,
+    "fig": 39,
+    "fish": 54,
+    "french fries": 3,
+    "fried meat": 50,
+    "garlic": 71,
+    "ginger": 78,
+    "grape": 41,
+    "green beans": 95,
+    "hamburg": 60,
+    "ice cream": 8,
+    "juice": 14,
+    "kelp": 74,
+    "king oyster mushroom": 97,
+    "kiwi": 42,
+    "lamb": 51,
+    "lemon": 37,
+    "lettuce": 80,
+    "mango": 34,
+    "melon": 43,
+    "milk": 15,
+    "milkshake": 12,
+    "noodles": 65,
+    "okra": 79,
+    "olives": 35,
+    "onion": 93,
+    "orange": 44,
+    "other ingredients": 103,
+    "oyster mushroom": 100,
+    "pasta": 64,
+    "peach": 36,
+    "peanut": 23,
+    "pear": 38,
+    "pepper": 94,
+    "pie": 67,
+    "pineapple": 40,
+    "pizza": 61,
+    "popcorn": 6,
+    "pork": 47,
+    "potato": 70,
+    "pudding": 7,
+    "pumpkin": 81,
+    "rape": 77,
+    "raspberry": 33,
+    "red beans": 18,
+    "rice": 66,
+    "salad": 102,
+    "sauce": 52,
+    "sausage": 49,
+    "seaweed": 75,
+    "shellfish": 55,
+    "shiitake": 98,
+    "shrimp": 56,
+    "snow peas": 90,
+    "soup": 57,
+    "soy": 21,
+    "spring onion": 76,
+    "steak": 46,
+    "strawberry": 30,
+    "tea": 16,
+    "tofu": 68,
+    "tomato": 73,
+    "walnut": 22,
+    "watermelon": 45,
+    "white button mushroom": 101,
+    "white radish": 83,
+    "wine": 11,
+    "wonton dumplings": 63
+  },
+  "layer_norm_eps": 1e-06,
+  "mlp_ratios": [
+    4,
+    4,
+    4,
+    4
+  ],
+  "model_type": "segformer",
+  "num_attention_heads": [
+    1,
+    2,
+    5,
+    8
+  ],
+  "num_channels": 3,
+  "num_encoder_blocks": 4,
+  "patch_sizes": [
+    7,
+    3,
+    3,
+    3
+  ],
+  "reshape_last_stage": true,
+  "semantic_loss_ignore_index": 255,
+  "sr_ratios": [
+    8,
+    4,
+    2,
+    1
+  ],
+  "strides": [
+    4,
+    2,
+    2,
+    2
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.3"
+}

model.safetensors

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+oid sha256:d75ff8e50123b3175acc5343cdfb0454c3301d0a7aa3849023752f2b75ef053f
+size 14989656

runs/Dec16_12-20-59_ebd935bb5f12/events.out.tfevents.1734351702.ebd935bb5f12.183.0

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+oid sha256:14cda9b123a4bd02368b68c55fe4c1295281311d6e8458ad5de2442998f377d8
+size 74690

training_args.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b9bf9e25b5a8f8b5a431fa93a87c4c1c518a5e52d77c0b436c6184102165e8dc
+size 5368