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README.md +87 -196
config.json +90 -0
model.safetensors +3 -0
training_args.bin +3 -0

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 ---
-library_name: transformers
-tags: []
 ---
-# 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.
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-## Uses
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-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.
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-## Training Details
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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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 ---
+license: other
+base_model: sayeed99/segformer-b3-fashion
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+model-index:
+- name: segformer-b3-fashion-finetuned-polo-segments-v1.5
+  results: []
 ---
+<!-- 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-b3-fashion-finetuned-polo-segments-v1.5
+This model is a fine-tuned version of [sayeed99/segformer-b3-fashion](https://huggingface.co/sayeed99/segformer-b3-fashion) on the sshk/polo-badges-segmentation dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1007
+- Mean Iou: 0.8404
+- Mean Accuracy: 0.9136
+- Overall Accuracy: 0.9704
+- Accuracy Unlabeled: nan
+- Accuracy Collar: 0.8876
+- Accuracy Polo: 0.9746
+- Accuracy Lines-cuff: 0.7358
+- Accuracy Lines-chest: 0.9360
+- Accuracy Human: 0.9631
+- Accuracy Background: 0.9848
+- Accuracy Tape: nan
+- Iou Unlabeled: nan
+- Iou Collar: 0.7360
+- Iou Polo: 0.9428
+- Iou Lines-cuff: 0.6178
+- Iou Lines-chest: 0.8353
+- Iou Human: 0.9386
+- Iou Background: 0.9718
+- Iou Tape: 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 30
+### Training results
+| Training Loss | Epoch   | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Collar | Accuracy Polo | Accuracy Lines-cuff | Accuracy Lines-chest | Accuracy Human | Accuracy Background | Accuracy Tape | Iou Unlabeled | Iou Collar | Iou Polo | Iou Lines-cuff | Iou Lines-chest | Iou Human | Iou Background | Iou Tape |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:---------------:|:-------------:|:-------------------:|:--------------------:|:--------------:|:-------------------:|:-------------:|:-------------:|:----------:|:--------:|:--------------:|:---------------:|:---------:|:--------------:|:--------:|
+| 0.1679        | 2.2222  | 20   | 0.2031          | 0.5532   | 0.5985        | 0.9492           | nan                | 0.6856          | 0.9707        | 0.0                 | 0.0022               | 0.9482         | 0.9843              | nan           | nan           | 0.5491     | 0.8882   | 0.0            | 0.0022          | 0.9192    | 0.9604         | nan      |
+| 0.0921        | 4.4444  | 40   | 0.1359          | 0.7103   | 0.7618        | 0.9631           | nan                | 0.8586          | 0.9739        | 0.1373              | 0.6598               | 0.9577         | 0.9835              | nan           | nan           | 0.6786     | 0.9244   | 0.1373         | 0.6217          | 0.9305    | 0.9691         | nan      |
+| 0.0603        | 6.6667  | 60   | 0.1166          | 0.8147   | 0.8651        | 0.9672           | nan                | 0.8436          | 0.9795        | 0.6385              | 0.7867               | 0.9586         | 0.9837              | nan           | nan           | 0.7114     | 0.9315   | 0.5955         | 0.7446          | 0.9352    | 0.9700         | nan      |
+| 0.0581        | 8.8889  | 80   | 0.1121          | 0.8185   | 0.8809        | 0.9677           | nan                | 0.8363          | 0.9767        | 0.6995              | 0.8279               | 0.9594         | 0.9857              | nan           | nan           | 0.7091     | 0.9336   | 0.6009         | 0.7611          | 0.9357    | 0.9709         | nan      |
+| 0.0445        | 11.1111 | 100  | 0.1047          | 0.8317   | 0.9033        | 0.9699           | nan                | 0.8719          | 0.9687        | 0.7198              | 0.9070               | 0.9686         | 0.9836              | nan           | nan           | 0.7263     | 0.9403   | 0.6081         | 0.8045          | 0.9390    | 0.9721         | nan      |
+| 0.0456        | 13.3333 | 120  | 0.1055          | 0.8342   | 0.9151        | 0.9694           | nan                | 0.8931          | 0.9687        | 0.7391              | 0.9402               | 0.9614         | 0.9878              | nan           | nan           | 0.7285     | 0.9405   | 0.6102         | 0.8184          | 0.9371    | 0.9708         | nan      |
+| 0.0443        | 15.5556 | 140  | 0.1034          | 0.8349   | 0.9039        | 0.9700           | nan                | 0.8740          | 0.9742        | 0.7208              | 0.9059               | 0.9636         | 0.9851              | nan           | nan           | 0.7324     | 0.9411   | 0.6091         | 0.8166          | 0.9384    | 0.9717         | nan      |
+| 0.0475        | 17.7778 | 160  | 0.1032          | 0.8384   | 0.9139        | 0.9699           | nan                | 0.8885          | 0.9738        | 0.7383              | 0.9356               | 0.9604         | 0.9868              | nan           | nan           | 0.7341     | 0.9409   | 0.6160         | 0.8300          | 0.9377    | 0.9717         | nan      |
+| 0.0411        | 20.0    | 180  | 0.1018          | 0.8403   | 0.9150        | 0.9702           | nan                | 0.8911          | 0.9770        | 0.7389              | 0.9378               | 0.9592         | 0.9862              | nan           | nan           | 0.7362     | 0.9417   | 0.6194         | 0.8346          | 0.9383    | 0.9716         | nan      |
+| 0.0345        | 22.2222 | 200  | 0.1003          | 0.8397   | 0.9112        | 0.9704           | nan                | 0.8885          | 0.9768        | 0.7359              | 0.9201               | 0.9625         | 0.9836              | nan           | nan           | 0.7355     | 0.9423   | 0.6157         | 0.8345          | 0.9387    | 0.9716         | nan      |
+| 0.0403        | 24.4444 | 220  | 0.1007          | 0.8397   | 0.9140        | 0.9705           | nan                | 0.8826          | 0.9745        | 0.7393              | 0.9392               | 0.9633         | 0.9851              | nan           | nan           | 0.7353     | 0.9434   | 0.6172         | 0.8319          | 0.9388    | 0.9716         | nan      |
+| 0.0563        | 26.6667 | 240  | 0.1009          | 0.8406   | 0.9140        | 0.9704           | nan                | 0.8914          | 0.9765        | 0.7306              | 0.9391               | 0.9603         | 0.9859              | nan           | nan           | 0.7360     | 0.9427   | 0.6202         | 0.8344          | 0.9383    | 0.9718         | nan      |
+| 0.0464        | 28.8889 | 260  | 0.1007          | 0.8404   | 0.9136        | 0.9704           | nan                | 0.8876          | 0.9746        | 0.7358              | 0.9360               | 0.9631         | 0.9848              | nan           | nan           | 0.7360     | 0.9428   | 0.6178         | 0.8353          | 0.9386    | 0.9718         | nan      |
+### Framework versions
+- Transformers 4.44.0
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

config.json ADDED Viewed

	@@ -0,0 +1,90 @@

+{
+  "_name_or_path": "sayeed99/segformer-b3-fashion",
+  "architectures": [
+    "SegformerForSemanticSegmentation"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "classifier_dropout_prob": 0.1,
+  "decoder_hidden_size": 768,
+  "depths": [
+    3,
+    4,
+    18,
+    3
+  ],
+  "downsampling_rates": [
+    1,
+    4,
+    8,
+    16
+  ],
+  "drop_path_rate": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_sizes": [
+    64,
+    128,
+    320,
+    512
+  ],
+  "id2label": {
+    "0": "unlabeled",
+    "1": "collar",
+    "2": "polo",
+    "3": "lines-cuff",
+    "4": "lines-chest",
+    "5": "human",
+    "6": "background",
+    "7": "tape"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "background": 6,
+    "collar": 1,
+    "human": 5,
+    "lines-chest": 4,
+    "lines-cuff": 3,
+    "polo": 2,
+    "tape": 7,
+    "unlabeled": 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": "4.44.0"
+}

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