Update README.md

README.md

@@ -3,8 +3,11 @@ library_name: transformers
 tags:
 - vision
 - image-segmentation
+- ecology
 datasets:
 - coralscapes
+metrics:
+- mean_iou
 ---
 
 # Model Card for Model ID
@@ -21,287 +24,151 @@ using the AdamW optimizer with an initial learning rate of 6e-5, weight decay of
 During training, images are randomly scaled within a range of 1 and 2, flipped horizontally with a 0.5 probability and randomly cropped to 1024×1024 pixels. 
 Input images are normalized using the ImageNet mean and standard deviation. For evaluation, a non-overlapping sliding window strategy is employed, 
 using a window size of 1024x1024. 
-<!-- TODO - We used a stride of 1024 but in the demo it is variable. Should we move this entire section to training below? -->
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
+
+- **Model type:** SegFormer
 - **License:** [More Information Needed]
 - **Finetuned from model:** [SegFormer (b2-sized) encoder pre-trained-only (`nvidia/mit-b2`)](https://huggingface.co/nvidia/mit-b2)
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
+### Model Sources 
 
 - **Repository:** [coralscapesScripts](https://github.com/eceo-epfl/coralscapesScripts/)
 - **Paper [optional]:** [More Information Needed]
 - **Demo** [Hugging Face Spaces](https://huggingface.co/spaces/EPFL-ECEO/coralscapes_demo):
 
-## 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
 
 The simplest way to use this model to segment an image of the Coralscapes dataset is as follows:
 
 ```python
-from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation
-from PIL import Image
-from datasets import load_dataset
-
-# Load an image from the coralscapes dataset or load your own image 
-dataset = load_dataset("EPFL-ECEO/coralscapes") 
-image = dataset["test"][42]["image"]
-
-preprocessor = SegformerImageProcessor.from_pretrained("EPFL-ECEO/segformer-b2-finetuned-coralscapes-1024-1024")
-model = SegformerForSemanticSegmentation.from_pretrained("EPFL-ECEO/segformer-b2-finetuned-coralscapes-1024-1024")
-
-inputs = preprocessor(image, return_tensors = "pt")
-outputs = model(**inputs)
-outputs = preprocessor.post_process_semantic_segmentation(outputs, target_sizes=[(image.size[1], image.size[0])])
-label_pred = outputs[0].cpu().numpy()
+  from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation
+  from PIL import Image
+  from datasets import load_dataset
+  
+  # Load an image from the coralscapes dataset or load your own image 
+  dataset = load_dataset("EPFL-ECEO/coralscapes") 
+  image = dataset["test"][42]["image"]
+  
+  preprocessor = SegformerImageProcessor.from_pretrained("EPFL-ECEO/segformer-b2-finetuned-coralscapes-1024-1024")
+  model = SegformerForSemanticSegmentation.from_pretrained("EPFL-ECEO/segformer-b2-finetuned-coralscapes-1024-1024")
+  
+  inputs = preprocessor(image, return_tensors = "pt")
+  outputs = model(**inputs)
+  outputs = preprocessor.post_process_semantic_segmentation(outputs, target_sizes=[(image.size[1], image.size[0])])
+  label_pred = outputs[0].cpu().numpy()
 ```
 
 While using the above approach should still work for images of different sizes and scales, for images that are not close to the training size of the model (1024x1024), 
 we recommend using the following approach using a sliding window to achieve better results:
 
 ```python
-import torch 
-import torch.nn.functional as F
-from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation
-from PIL import Image
-from datasets import load_dataset
-import numpy as np
-
-def resize_image(image, target_size=1024):
-    """
-    Used to resize the image such that the smaller side equals 1024
-    """
-    h_img, w_img = image.size
-    if h_img < w_img:
-        new_h, new_w = target_size, int(w_img * (target_size / h_img))
-    else:
-        new_h, new_w  = int(h_img * (target_size / w_img)), target_size
-    resized_img = image.resize((new_h, new_w))
-    return resized_img
-
-def segment_image(image, preprocessor, model, crop_size = (1024, 1024), num_classes = 40, transform=None):
-    """
-    Finds an optimal stride based on the image size and aspect ratio to create
-    overlapping sliding windows of size 1024x1024 which are then fed into the model.  
-    """ 
-    h_crop, w_crop = crop_size
-    
-    img = torch.Tensor(np.array(resize_image(image, target_size=1024)).transpose(2, 0, 1)).unsqueeze(0)
-    batch_size, _, h_img, w_img = img.size()
-    
-    if transform:
-        img = torch.Tensor(transform(image = img.numpy())["image"]).to(device)    
-        
-    h_grids = int(np.round(3/2*h_img/h_crop)) if h_img > h_crop else 1
-    w_grids = int(np.round(3/2*w_img/w_crop)) if w_img > w_crop else 1
-    
-    h_stride = int((h_img - h_crop + h_grids -1)/(h_grids -1)) if h_grids > 1 else h_crop
-    w_stride = int((w_img - w_crop + w_grids -1)/(w_grids -1)) if w_grids > 1 else w_crop
-    
-    preds = img.new_zeros((batch_size, num_classes, h_img, w_img))
-    count_mat = img.new_zeros((batch_size, 1, h_img, w_img))
-    
-    for h_idx in range(h_grids):
-        for w_idx in range(w_grids):
-          y1 = h_idx * h_stride
-          x1 = w_idx * w_stride
-          y2 = min(y1 + h_crop, h_img)
-          x2 = min(x1 + w_crop, w_img)
-          y1 = max(y2 - h_crop, 0)
-          x1 = max(x2 - w_crop, 0)
-          crop_img = img[:, :, y1:y2, x1:x2]
-          with torch.no_grad():
-            if(preprocessor):
-                inputs = preprocessor(crop_img, return_tensors = "pt")
-                inputs["pixel_values"] = inputs["pixel_values"].to(device)
-            else:
-                inputs = crop_img.to(device)
-            outputs = model(**inputs)
-
-          resized_logits = F.interpolate(
-              outputs.logits[0].unsqueeze(dim=0), size=crop_img.shape[-2:], mode="bilinear", align_corners=False
-          )
-          preds += F.pad(resized_logits,
-                          (int(x1), int(preds.shape[3] - x2), int(y1),
-                          int(preds.shape[2] - y2)))
-          count_mat[:, :, y1:y2, x1:x2] += 1
-    
-    assert (count_mat == 0).sum() == 0
-    preds = preds / count_mat
-    preds = preds.argmax(dim=1)
-    preds = F.interpolate(preds.unsqueeze(0).type(torch.uint8), size=image.size[::-1], mode='nearest')
-    label_pred = preds.squeeze().cpu().numpy()
-    return label_pred
-
-# Load an image from the coralscapes dataset or load your own image 
-dataset = load_dataset("EPFL-ECEO/coralscapes") 
-image = dataset["test"][42]["image"]
-
-preprocessor = SegformerImageProcessor.from_pretrained("EPFL-ECEO/segformer-b2-finetuned-coralscapes-1024-1024")
-model = SegformerForSemanticSegmentation.from_pretrained("EPFL-ECEO/segformer-b2-finetuned-coralscapes-1024-1024")
-
-label_pred = segment_image(image, preprocessor, model)
+  import torch 
+  import torch.nn.functional as F
+  from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation
+  from PIL import Image
+  import numpy as np
+  from datasets import load_dataset
+  device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+  def resize_image(image, target_size=1024):
+      """
+      Used to resize the image such that the smaller side equals 1024
+      """
+      h_img, w_img = image.size
+      if h_img < w_img:
+          new_h, new_w = target_size, int(w_img * (target_size / h_img))
+      else:
+          new_h, new_w  = int(h_img * (target_size / w_img)), target_size
+      resized_img = image.resize((new_h, new_w))
+      return resized_img
+
+  def segment_image(image, preprocessor, model, crop_size = (1024, 1024), num_classes = 40, transform=None):
+      """
+      Finds an optimal stride based on the image size and aspect ratio to create
+      overlapping sliding windows of size 1024x1024 which are then fed into the model.  
+      """ 
+      h_crop, w_crop = crop_size
+      
+      img = torch.Tensor(np.array(resize_image(image, target_size=1024)).transpose(2, 0, 1)).unsqueeze(0)
+      batch_size, _, h_img, w_img = img.size()
+      
+      if transform:
+          img = torch.Tensor(transform(image = img.numpy())["image"]).to(device)    
+          
+      h_grids = int(np.round(3/2*h_img/h_crop)) if h_img > h_crop else 1
+      w_grids = int(np.round(3/2*w_img/w_crop)) if w_img > w_crop else 1
+      
+      h_stride = int((h_img - h_crop + h_grids -1)/(h_grids -1)) if h_grids > 1 else h_crop
+      w_stride = int((w_img - w_crop + w_grids -1)/(w_grids -1)) if w_grids > 1 else w_crop
+      
+      preds = img.new_zeros((batch_size, num_classes, h_img, w_img))
+      count_mat = img.new_zeros((batch_size, 1, h_img, w_img))
+      
+      for h_idx in range(h_grids):
+          for w_idx in range(w_grids):
+              y1 = h_idx * h_stride
+              x1 = w_idx * w_stride
+              y2 = min(y1 + h_crop, h_img)
+              x2 = min(x1 + w_crop, w_img)
+              y1 = max(y2 - h_crop, 0)
+              x1 = max(x2 - w_crop, 0)
+              crop_img = img[:, :, y1:y2, x1:x2]
+              with torch.no_grad():
+                  if(preprocessor):
+                      inputs = preprocessor(crop_img, return_tensors = "pt")
+                      inputs["pixel_values"] = inputs["pixel_values"].to(device)
+                  else:
+                      inputs = crop_img.to(device)
+                  outputs = model(**inputs)
+
+              resized_logits = F.interpolate(
+                  outputs.logits[0].unsqueeze(dim=0), size=crop_img.shape[-2:], mode="bilinear", align_corners=False
+              )
+              preds += F.pad(resized_logits,
+                              (int(x1), int(preds.shape[3] - x2), int(y1),
+                              int(preds.shape[2] - y2))).cpu()
+              count_mat[:, :, y1:y2, x1:x2] += 1
+          
+      assert (count_mat == 0).sum() == 0
+      preds = preds / count_mat
+      preds = preds.argmax(dim=1)
+      preds = F.interpolate(preds.unsqueeze(0).type(torch.uint8), size=image.size[::-1], mode='nearest')
+      label_pred = preds.squeeze().cpu().numpy()
+      return label_pred
+
+  # Load an image from the coralscapes dataset or load your own image 
+  dataset = load_dataset("EPFL-ECEO/coralscapes") 
+  image = dataset["test"][42]["image"]
+
+  preprocessor = SegformerImageProcessor.from_pretrained("EPFL-ECEO/segformer-b2-finetuned-coralscapes-1024-1024")
+  model = SegformerForSemanticSegmentation.from_pretrained("EPFL-ECEO/segformer-b2-finetuned-coralscapes-1024-1024")
+
+  label_pred = segment_image(image, preprocessor, model)
 ```
 
-## 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 & Evaluation Details
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+### Data
 
-#### Speeds, Sizes, Times [optional]
+The model is trained and evaluated on the [Coralscapes dataset](https://huggingface.co/datasets/EPFL-ECEO/coralscapes) which is a general-purpose dense semantic segmentation dataset for coral reefs. 
 
-<!-- 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]
+### Procedure
 
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+Training is conducted following the Segformer original [implementation](https://proceedings.neurips.cc/paper_files/paper/2021/file/64f1f27bf1b4ec22924fd0acb550c235-Paper.pdf), using a batch size of 8 for 265 epochs, 
+using the AdamW optimizer with an initial learning rate of 6e-5, weight decay of 1e-2 and polynomial learning rate scheduler with a power of 1. 
+During training, images are randomly scaled within a range of 1 and 2, flipped horizontally with a 0.5 probability and randomly cropped to 1024×1024 pixels. 
+Input images are normalized using the ImageNet mean and standard deviation. For evaluation, a non-overlapping sliding window strategy is employed, 
+using a window size of 1024x1024. 
 
-[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
+mIoU - 54.682
+Accuracy - 80.904
 
-[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. -->
+## Citation 
 
 **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]
-
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-
-## Model Card Contact
-
-[More Information Needed]