End of training

README.md

@@ -1,201 +1,70 @@
 ---
-library_name: transformers
-tags: []
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+model-index:
+- name: segformer-b4-wall
+  results: []
 ---
 
-# Model Card for Model ID
+<!-- 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. -->
 
-<!-- Provide a quick summary of what the model is/does. -->
+# segformer-b4-wall
 
+This model was trained from scratch on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1537
+- Mean Accuracy: 0.9448
+- Mean Iou: 0.8993
+- Overall Accuracy: 0.9558
+- Per Category Accuracy: [0.9648476610683054, 0.9680509025433003, 0.9015647356112896, nan]
+- Per Category Iou: [0.9294668192886654, 0.9344825387850888, 0.8340281823830938, nan]
 
+## Model description
 
-## Model Details
+More information needed
 
-### Model Description
+## Intended uses & limitations
 
-<!-- Provide a longer summary of what this model is. -->
+More information needed
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+## Training and evaluation data
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+More information needed
 
-### Model Sources [optional]
+## Training procedure
 
-<!-- Provide the basic links for the model. -->
+### Training hyperparameters
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+The following hyperparameters were used during training:
+- learning_rate: 6e-05
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 50
 
-## Uses
+### Training results
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+| Training Loss | Epoch   | Step | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy | Per Category Accuracy                                             | Per Category Iou                                                  |
+|:-------------:|:-------:|:----:|:---------------:|:-------------:|:--------:|:----------------:|:-----------------------------------------------------------------:|:-----------------------------------------------------------------:|
+| 0.1398        | 5.3476  | 1000 | 0.1477          | 0.9424        | 0.8733   | 0.9420           | [0.9375947027923643, 0.962438818648652, 0.9270677962243152, nan]  | [0.9071928258269675, 0.9154732958813474, 0.7971633247503161, nan] |
+| 0.1114        | 10.6952 | 2000 | 0.1329          | 0.9426        | 0.8878   | 0.9498           | [0.9551513266050631, 0.9606741248023447, 0.9120448217426163, nan] | [0.9197608920879746, 0.9255854097692368, 0.818153830444766, nan]  |
+| 0.0683        | 16.0428 | 3000 | 0.1353          | 0.9473        | 0.8921   | 0.9516           | [0.9527839457434386, 0.9691455504455139, 0.9198476394516605, nan] | [0.922537499674425, 0.926305870761282, 0.8273726843249476, nan]   |
+| 0.0753        | 21.3904 | 4000 | 0.1311          | 0.9437        | 0.8959   | 0.9540           | [0.9633835386385788, 0.9611760655179852, 0.9066569940696604, nan] | [0.9267602358926313, 0.9312805978213234, 0.8297698871401628, nan] |
+| 0.0505        | 26.7380 | 5000 | 0.1397          | 0.9442        | 0.8971   | 0.9545           | [0.9627544499461427, 0.967327419780526, 0.9024453947068249, nan]  | [0.9272910775593762, 0.9304849186604474, 0.8333807013974415, nan] |
+| 0.0427        | 32.0856 | 6000 | 0.1414          | 0.9455        | 0.8992   | 0.9555           | [0.9640187847053339, 0.9652081246861538, 0.9074073950598316, nan] | [0.9289147168722637, 0.9321577805497577, 0.8366507705917902, nan] |
+| 0.0556        | 37.4332 | 7000 | 0.1477          | 0.9452        | 0.8984   | 0.9552           | [0.9629165900233977, 0.9697602413261539, 0.9029026554269718, nan] | [0.9285106797857617, 0.9331322728249959, 0.833620894806762, nan]  |
+| 0.0424        | 42.7807 | 8000 | 0.1484          | 0.9439        | 0.8990   | 0.9557           | [0.9653151526182964, 0.96949089540134, 0.8967977175922358, nan]   | [0.9292691886525306, 0.9343666443212755, 0.83323737535253, nan]   |
+| 0.053         | 48.1283 | 9000 | 0.1537          | 0.9448        | 0.8993   | 0.9558           | [0.9648476610683054, 0.9680509025433003, 0.9015647356112896, nan] | [0.9294668192886654, 0.9344825387850888, 0.8340281823830938, nan] |
 
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
 
+### Framework versions
 
+- Transformers 4.40.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.19.1

config.json

@@ -0,0 +1,82 @@
+{
+  "_name_or_path": "pretrained_checkpoints/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": "wall",
+    "3": "ceiling",
+    "4": "floor"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "background": 0,
+    "ceiling": 3,
+    "floor": 4,
+    "wall": 1
+  },
+  "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": "4.40.2"
+}

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