End of training

README.md

@@ -1,201 +1,95 @@
 ---
-library_name: transformers
-tags: []
+license: other
+base_model: nvidia/mit-b0
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+model-index:
+- name: segformer-b0-finetuned-segments-sidewalk-oct-22
+  results: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- 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.
-
-- **Developed by:** [More Information Needed]
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-## Uses
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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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-## Evaluation
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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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+<!-- 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-segments-sidewalk-oct-22
+
+This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the jaradat/pidray-semantics dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6864
+- Mean Iou: 0.3640
+- Mean Accuracy: 0.7280
+- Overall Accuracy: 0.7280
+- Accuracy Baton: nan
+- Accuracy Pliers: 0.7280
+- Accuracy Hammer: nan
+- Accuracy Powerbank: nan
+- Accuracy Scissors: nan
+- Accuracy Wrench: nan
+- Accuracy Gun: nan
+- Accuracy Bullet: nan
+- Accuracy Sprayer: nan
+- Accuracy Handcuffs: nan
+- Accuracy Knife: nan
+- Accuracy Lighter: nan
+- Iou Baton: 0.0
+- Iou Pliers: 0.7280
+- Iou Hammer: nan
+- Iou Powerbank: nan
+- Iou Scissors: nan
+- Iou Wrench: nan
+- Iou Gun: nan
+- Iou Bullet: nan
+- Iou Sprayer: nan
+- Iou Handcuffs: nan
+- Iou Knife: nan
+- Iou Lighter: 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: 2
+- eval_batch_size: 2
+- 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 | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Baton | Accuracy Pliers | Accuracy Hammer | Accuracy Powerbank | Accuracy Scissors | Accuracy Wrench | Accuracy Gun | Accuracy Bullet | Accuracy Sprayer | Accuracy Handcuffs | Accuracy Knife | Accuracy Lighter | Iou Baton | Iou Pliers | Iou Hammer | Iou Powerbank | Iou Scissors | Iou Wrench | Iou Gun | Iou Bullet | Iou Sprayer | Iou Handcuffs | Iou Knife | Iou Lighter |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:---------------:|:---------------:|:------------------:|:-----------------:|:---------------:|:------------:|:---------------:|:----------------:|:------------------:|:--------------:|:----------------:|:---------:|:----------:|:----------:|:-------------:|:------------:|:----------:|:-------:|:----------:|:-----------:|:-------------:|:---------:|:-----------:|
+| 1.3901        | 0.5   | 20   | 1.0681          | 0.2394   | 0.7182        | 0.7182           | nan            | 0.7182          | nan             | nan                | nan               | nan             | nan          | nan             | nan              | nan                | nan            | nan              | 0.0       | 0.7182     | nan        | nan           | nan          | nan        | nan     | nan        | nan         | 0.0           | nan       | nan         |
+| 1.0757        | 1.0   | 40   | 1.0174          | 0.2673   | 0.8019        | 0.8019           | nan            | 0.8019          | nan             | nan                | nan               | nan             | nan          | nan             | nan              | nan                | nan            | nan              | 0.0       | 0.8019     | nan        | nan           | nan          | nan        | nan     | nan        | nan         | 0.0           | nan       | nan         |
+| 0.9644        | 1.5   | 60   | 0.9465          | 0.3957   | 0.7914        | 0.7914           | nan            | 0.7914          | nan             | nan                | nan               | nan             | nan          | nan             | nan              | nan                | nan            | nan              | 0.0       | 0.7914     | nan        | nan           | nan          | nan        | nan     | nan        | nan         | nan           | nan       | nan         |
+| 0.9893        | 2.0   | 80   | 0.9100          | 0.3731   | 0.7462        | 0.7462           | nan            | 0.7462          | nan             | nan                | nan               | nan             | nan          | nan             | nan              | nan                | nan            | nan              | 0.0       | 0.7462     | nan        | nan           | nan          | nan        | nan     | nan        | nan         | nan           | nan       | nan         |
+| 0.8518        | 2.5   | 100  | 0.8205          | 0.3708   | 0.7416        | 0.7416           | nan            | 0.7416          | nan             | nan                | nan               | nan             | nan          | nan             | nan              | nan                | nan            | nan              | 0.0       | 0.7416     | nan        | nan           | nan          | nan        | nan     | nan        | nan         | nan           | nan       | nan         |
+| 0.8305        | 3.0   | 120  | 0.7641          | 0.3670   | 0.7339        | 0.7339           | nan            | 0.7339          | nan             | nan                | nan               | nan             | nan          | nan             | nan              | nan                | nan            | nan              | 0.0       | 0.7339     | nan        | nan           | nan          | nan        | nan     | nan        | nan         | nan           | nan       | nan         |
+| 0.7404        | 3.5   | 140  | 0.7041          | 0.3557   | 0.7113        | 0.7113           | nan            | 0.7113          | nan             | nan                | nan               | nan             | nan          | nan             | nan              | nan                | nan            | nan              | 0.0       | 0.7113     | nan        | nan           | nan          | nan        | nan     | nan        | nan         | nan           | nan       | nan         |
+| 0.8427        | 4.0   | 160  | 0.7430          | 0.3803   | 0.7606        | 0.7606           | nan            | 0.7606          | nan             | nan                | nan               | nan             | nan          | nan             | nan              | nan                | nan            | nan              | 0.0       | 0.7606     | nan        | nan           | nan          | nan        | nan     | nan        | nan         | nan           | nan       | nan         |
+| 0.7279        | 4.5   | 180  | 0.7101          | 0.3707   | 0.7414        | 0.7414           | nan            | 0.7414          | nan             | nan                | nan               | nan             | nan          | nan             | nan              | nan                | nan            | nan              | 0.0       | 0.7414     | nan        | nan           | nan          | nan        | nan     | nan        | nan         | nan           | nan       | nan         |
+| 0.7587        | 5.0   | 200  | 0.6864          | 0.3640   | 0.7280        | 0.7280           | nan            | 0.7280          | nan             | nan                | nan               | nan             | nan          | nan             | nan              | nan                | nan            | nan              | 0.0       | 0.7280     | nan        | nan           | nan          | nan        | nan     | nan        | nan         | nan           | nan       | nan         |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

config.json

@@ -0,0 +1,98 @@
+{
+  "_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": {
+    "1": "Baton",
+    "2": "Pliers",
+    "3": "Hammer",
+    "4": "Powerbank",
+    "5": "scissors",
+    "6": "Wrench",
+    "7": "Gun",
+    "8": "Bullet",
+    "9": "Sprayer",
+    "10": "HandCuffs",
+    "11": "Knife",
+    "12": "Lighter"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "Baton": 1,
+    "Bullet": 8,
+    "Gun": 7,
+    "Hammer": 3,
+    "HandCuffs": 10,
+    "Knife": 11,
+    "Lighter": 12,
+    "Pliers": 2,
+    "Powerbank": 4,
+    "Sprayer": 9,
+    "Wrench": 6,
+    "scissors": 5
+  },
+  "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": "4.37.2"
+}

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