End of training

README.md

@@ -1,199 +1,88 @@
 ---
 library_name: transformers
-tags: []
+license: other
+base_model: nvidia/mit-b0
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+model-index:
+- name: segformer-b0-finetuned-batch1-30nov
+  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]
-- **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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### 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]
+<!-- 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-batch1-30nov
+
+This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the PushkarA07/batch1-tiles dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0966
+- Mean Iou: 0.5771
+- Mean Accuracy: 0.6248
+- Overall Accuracy: 0.9912
+- Accuracy Abnormality: 0.2533
+- Iou Abnormality: 0.1629
+
+## 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: 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: 10
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Abnormality | Iou Abnormality |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------:|:---------------:|
+| 0.5472        | 0.4167 | 10   | 0.6708          | 0.4927   | 0.8372        | 0.9235           | 0.7497               | 0.0623          |
+| 0.4938        | 0.8333 | 20   | 0.6279          | 0.5549   | 0.7553        | 0.9766           | 0.5310               | 0.1333          |
+| 0.4212        | 1.25   | 30   | 0.5862          | 0.5301   | 0.7733        | 0.9634           | 0.5807               | 0.0970          |
+| 0.3701        | 1.6667 | 40   | 0.4789          | 0.5446   | 0.6614        | 0.9811           | 0.3373               | 0.1080          |
+| 0.2942        | 2.0833 | 50   | 0.4493          | 0.5046   | 0.7100        | 0.9499           | 0.4667               | 0.0594          |
+| 0.3006        | 2.5    | 60   | 0.2510          | 0.5474   | 0.6485        | 0.9833           | 0.3091               | 0.1116          |
+| 0.2309        | 2.9167 | 70   | 0.3163          | 0.5131   | 0.6837        | 0.9610           | 0.4027               | 0.0654          |
+| 0.2249        | 3.3333 | 80   | 0.3296          | 0.5296   | 0.7012        | 0.9703           | 0.4284               | 0.0891          |
+| 0.2643        | 3.75   | 90   | 0.3136          | 0.5328   | 0.7081        | 0.9713           | 0.4413               | 0.0943          |
+| 0.2062        | 4.1667 | 100  | 0.2595          | 0.5395   | 0.6919        | 0.9761           | 0.4039               | 0.1029          |
+| 0.1932        | 4.5833 | 110  | 0.2060          | 0.5752   | 0.6311        | 0.9905           | 0.2667               | 0.1599          |
+| 0.1693        | 5.0    | 120  | 0.2871          | 0.5652   | 0.6707        | 0.9859           | 0.3512               | 0.1445          |
+| 0.1555        | 5.4167 | 130  | 0.1601          | 0.5623   | 0.6411        | 0.9876           | 0.2898               | 0.1370          |
+| 0.1344        | 5.8333 | 140  | 0.1512          | 0.5732   | 0.6543        | 0.9886           | 0.3155               | 0.1578          |
+| 0.1531        | 6.25   | 150  | 0.1447          | 0.5706   | 0.6309        | 0.9899           | 0.2669               | 0.1514          |
+| 0.149         | 6.6667 | 160  | 0.1763          | 0.5708   | 0.6415        | 0.9891           | 0.2891               | 0.1526          |
+| 0.2171        | 7.0833 | 170  | 0.1883          | 0.5695   | 0.6407        | 0.9890           | 0.2876               | 0.1500          |
+| 0.1207        | 7.5    | 180  | 0.1804          | 0.5725   | 0.6363        | 0.9897           | 0.2780               | 0.1552          |
+| 0.106         | 7.9167 | 190  | 0.1413          | 0.5712   | 0.6299        | 0.9900           | 0.2648               | 0.1524          |
+| 0.1234        | 8.3333 | 200  | 0.1407          | 0.5650   | 0.6124        | 0.9904           | 0.2292               | 0.1396          |
+| 0.2295        | 8.75   | 210  | 0.0943          | 0.5734   | 0.6150        | 0.9914           | 0.2334               | 0.1555          |
+| 0.1301        | 9.1667 | 220  | 0.0870          | 0.5746   | 0.6135        | 0.9917           | 0.2302               | 0.1575          |
+| 0.1581        | 9.5833 | 230  | 0.0895          | 0.5747   | 0.6129        | 0.9917           | 0.2289               | 0.1577          |
+| 0.1062        | 10.0   | 240  | 0.0966          | 0.5771   | 0.6248        | 0.9912           | 0.2533               | 0.1629          |
+
+
+### Framework versions
+
+- Transformers 4.46.2
+- Pytorch 2.5.1+cu121
+- Datasets 3.1.0
+- Tokenizers 0.20.3

config.json

@@ -0,0 +1,78 @@
+{
+  "_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": "normal",
+    "1": "abnormality"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "abnormality": 1,
+    "normal": 0
+  },
+  "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": "4.46.2"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:302823a3cae91000deb27473c5c101f0c3e996d084b5f7f15b78a894b61ce05f
+size 14884776

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6f3ceab58763566777d568bed05ad4569465b2b18c49246f4d9f6f3eb211fb6d
+size 5304