End of training

README.md

@@ -1,199 +1,80 @@
 ---
-library_name: transformers
-tags: []
+base_model: beit-base-finetuned-ade-640-640
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+model-index:
+- name: BEiT_beit-base-finetuned-ade-640-640_Clean-Set3-Grayscale_RGB
+  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. -->
+# BEiT_beit-base-finetuned-ade-640-640_Clean-Set3-Grayscale_RGB
 
+This model is a fine-tuned version of [beit-base-finetuned-ade-640-640](https://huggingface.co/beit-base-finetuned-ade-640-640) on the Hasano20/Clean-Set3-Grayscale dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0652
+- Mean Iou: 0.9446
+- Mean Accuracy: 0.9687
+- Overall Accuracy: 0.9855
+- Accuracy Background: 0.9893
+- Accuracy Melt: 0.9263
+- Accuracy Substrate: 0.9905
+- Iou Background: 0.9796
+- Iou Melt: 0.8757
+- Iou Substrate: 0.9785
 
+## 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: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 200
+- 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 Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Melt | Accuracy Substrate | Iou Background | Iou Melt | Iou Substrate |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
+| 0.1893        | 3.7037  | 100  | 0.1358          | 0.9180   | 0.9486        | 0.9815           | 0.9923              | 0.8651        | 0.9884             | 0.9821         | 0.7997   | 0.9723        |
+| 0.0951        | 7.4074  | 200  | 0.0746          | 0.9483   | 0.9791        | 0.9876           | 0.9909              | 0.9575        | 0.9890             | 0.9860         | 0.8775   | 0.9814        |
+| 0.0892        | 11.1111 | 300  | 0.0631          | 0.9489   | 0.9772        | 0.9866           | 0.9875              | 0.9536        | 0.9903             | 0.9817         | 0.8854   | 0.9796        |
+| 0.0878        | 14.8148 | 400  | 0.0692          | 0.9375   | 0.9687        | 0.9829           | 0.9853              | 0.9332        | 0.9877             | 0.9742         | 0.8632   | 0.9750        |
+| 0.07          | 18.5185 | 500  | 0.0631          | 0.9419   | 0.9700        | 0.9844           | 0.9865              | 0.9339        | 0.9897             | 0.9777         | 0.8714   | 0.9767        |
+| 0.0507        | 22.2222 | 600  | 0.0646          | 0.9379   | 0.9659        | 0.9829           | 0.9879              | 0.9230        | 0.9869             | 0.9738         | 0.8653   | 0.9747        |
+| 0.0523        | 25.9259 | 700  | 0.0569          | 0.9485   | 0.9724        | 0.9864           | 0.9903              | 0.9371        | 0.9898             | 0.9809         | 0.8855   | 0.9791        |
+| 0.0402        | 29.6296 | 800  | 0.0622          | 0.9422   | 0.9686        | 0.9848           | 0.9881              | 0.9279        | 0.9897             | 0.9777         | 0.8711   | 0.9778        |
+| 0.0406        | 33.3333 | 900  | 0.0631          | 0.9427   | 0.9683        | 0.9852           | 0.9894              | 0.9256        | 0.9899             | 0.9790         | 0.8707   | 0.9785        |
+| 0.0389        | 37.0370 | 1000 | 0.0650          | 0.9436   | 0.9695        | 0.9852           | 0.9879              | 0.9303        | 0.9904             | 0.9784         | 0.8739   | 0.9784        |
+| 0.0396        | 40.7407 | 1100 | 0.0634          | 0.9457   | 0.9700        | 0.9857           | 0.9900              | 0.9301        | 0.9899             | 0.9798         | 0.8787   | 0.9787        |
+| 0.036         | 44.4444 | 1200 | 0.0653          | 0.9443   | 0.9684        | 0.9855           | 0.9894              | 0.9254        | 0.9906             | 0.9797         | 0.8748   | 0.9786        |
+| 0.0224        | 48.1481 | 1300 | 0.0652          | 0.9446   | 0.9687        | 0.9855           | 0.9893              | 0.9263        | 0.9905             | 0.9796         | 0.8757   | 0.9785        |
 
-### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+### Framework versions
 
-[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]
+- Transformers 4.41.2
+- Pytorch 2.0.1+cu117
+- Datasets 2.19.2
+- Tokenizers 0.19.1

config.json

@@ -0,0 +1,86 @@
+{
+  "_name_or_path": "microsoft/beit-base-finetuned-ade-640-640",
+  "add_fpn": false,
+  "architectures": [
+    "BeitForSemanticSegmentation"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auxiliary_channels": 256,
+  "auxiliary_concat_input": false,
+  "auxiliary_loss_weight": 0.4,
+  "auxiliary_num_convs": 1,
+  "drop_path_rate": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "background",
+    "1": "melt",
+    "2": "substrate"
+  },
+  "image_size": 640,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "background": 0,
+    "melt": 1,
+    "substrate": 2
+  },
+  "layer_norm_eps": 1e-12,
+  "layer_scale_init_value": 0.1,
+  "model_type": "beit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "out_features": [
+    "stage3",
+    "stage5",
+    "stage7",
+    "stage11"
+  ],
+  "out_indices": [
+    3,
+    5,
+    7,
+    11
+  ],
+  "patch_size": 16,
+  "pool_scales": [
+    1,
+    2,
+    3,
+    6
+  ],
+  "reshape_hidden_states": true,
+  "segmentation_indices": [
+    3,
+    5,
+    7,
+    11
+  ],
+  "semantic_loss_ignore_index": 255,
+  "stage_names": [
+    "stem",
+    "stage1",
+    "stage2",
+    "stage3",
+    "stage4",
+    "stage5",
+    "stage6",
+    "stage7",
+    "stage8",
+    "stage9",
+    "stage10",
+    "stage11",
+    "stage12"
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "use_absolute_position_embeddings": false,
+  "use_auxiliary_head": true,
+  "use_mask_token": false,
+  "use_mean_pooling": true,
+  "use_relative_position_bias": true,
+  "use_shared_relative_position_bias": false,
+  "vocab_size": 8192
+}

model.safetensors

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+size 653146272

training_args.bin

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