Upload folder using huggingface_hub

Dockerfile

@@ -16,4 +16,4 @@ COPY --chown=user train.sh pytorch-image-models
 RUN chmod +x pytorch-image-models/train.sh
 
 COPY --chown=user . /app
-CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+CMD ["python", "app.py"]

app.py

@@ -1,6 +1,6 @@
 import os
-from fastapi import FastAPI
-import subprocess
+import gradio as gr
+
 import wandb
 from huggingface_hub import HfApi
 
@@ -9,13 +9,11 @@ API = HfApi(token=TOKEN)
 wandb_api_key = os.environ.get('wandb_api_key')
 wandb.login(key=wandb_api_key)
 
-random_num = 80.0
+random_num = '80.0'
 subset = 'frac-1over64'
 experiment_name = f"ImageNetTraining{random_num}-{subset}"
 experiment_repo = f"datacomp/{experiment_name}"
-app = FastAPI()
 
-@app.get("/")
 def start_train():
     os.system("echo '#### pwd'")
     os.system("pwd")
@@ -33,11 +31,21 @@ def start_train():
     # Handles CUDA OOM errors.
     os.system(f"export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True")
     os.system("echo 'Okay, trying training.'")
-    os.system(f"cd pytorch-image-models; ./train.sh 4 --dataset hfds/datacomp/imagenet-1k-random-{random_num}-{subset} --log-wandb --experiment ImageNetTraining{random_num}-{subset} --model seresnet34 --sched cosine --epochs 150 --warmup-epochs 5 --lr 0.4 --reprob 0.5 --remode pixel --batch-size 256 --amp -j 4")
+    os.system(f"cd pytorch-image-models; ./train.sh 4 --dataset hfds/datacomp/imagenet-1k-random-{random_num}-{subset} --log-wandb --wandb-project {experiment_name} --experiment ImageNetTraining{random_num}-{subset} --model seresnet34 --sched cosine --epochs 150 --warmup-epochs 5 --lr 0.4 --reprob 0.5 --remode pixel --batch-size 256 --amp -j 4")
     os.system("echo 'Done'.")
     os.system("ls")
     # Upload output to repository
     os.system("echo 'trying to upload...'")
     API.upload_folder(folder_path="/app", repo_id=f"{experiment_repo}", repo_type="dataset",)
     API.pause_space(experiment_repo)
-    return {"Completed": "!"}
+
+def run():
+    with gr.Blocks() as app:
+        gr.Markdown(f"Randomization: {random_num}")
+        gr.Markdown(f"Subset: {subset}")
+        start = gr.Button("Start")
+        start.click(start_train)
+    app.launch(server_name="0.0.0.0", server_port=7860)
+    
+if __name__ == '__main__':
+    run()

pytorch-image-models/hfdocs/source/models.mdx

@@ -33,7 +33,7 @@ A more exciting view (with pretty pictures) of the models within `timm` can be f
 ## DLA
 
 * Implementation: [dla.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/dla.py)
-* Paper: https://arxiv.org/abs/1707.06484
+* Paper: `Deep Layer Aggregation` - https://arxiv.org/abs/1707.06484
 * Code: https://github.com/ucbdrive/dla
 
 ## Dual-Path Networks
@@ -78,14 +78,14 @@ A more exciting view (with pretty pictures) of the models within `timm` can be f
 ## NASNet-A
 
 * Implementation: [nasnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/nasnet.py)
-* Papers: `Learning Transferable Architectures for Scalable Image Recognition` - https://arxiv.org/abs/1707.07012
+* Paper: `Learning Transferable Architectures for Scalable Image Recognition` - https://arxiv.org/abs/1707.07012
 * Code: https://github.com/Cadene/pretrained-models.pytorch
 * Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet
 
 ## PNasNet-5
 
 * Implementation: [pnasnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/pnasnet.py)
-* Papers: `Progressive Neural Architecture Search` - https://arxiv.org/abs/1712.00559
+* Paper: `Progressive Neural Architecture Search` - https://arxiv.org/abs/1712.00559
 * Code: https://github.com/Cadene/pretrained-models.pytorch
 * Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet
 

pytorch-image-models/hfdocs/source/models/adversarial-inception-v3.mdx

@@ -77,7 +77,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/advprop.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/big-transfer.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/csp-darknet.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/csp-resnet.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/csp-resnext.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/densenet.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/dla.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/dpn.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/ecaresnet.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/efficientnet-pruned.mdx

@@ -1,6 +1,6 @@
 # EfficientNet (Knapsack Pruned)
 
-**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales  these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use \\( 2^N \\) times more computational resources, then we can simply increase the network depth by \\( \alpha ^ N \\),  width by \\( \beta ^ N \\), and image size by \\( \gamma ^ N \\), where \\( \alpha, \beta, \gamma \\) are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient \\( \phi \\) to uniformly scales network width, depth, and resolution in a principled way.
+**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use \\( 2^N \\) times more computational resources, then we can simply increase the network depth by \\( \alpha ^ N \\), width by \\( \beta ^ N \\), and image size by \\( \gamma ^ N \\), where \\( \alpha, \beta, \gamma \\) are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient \\( \phi \\) to uniformly scale network width, depth, and resolution in a principled way.
 
 The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image.
 
@@ -79,7 +79,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/efficientnet.mdx

@@ -1,6 +1,6 @@
 # EfficientNet
 
-**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales  these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use \\( 2^N \\) times more computational resources, then we can simply increase the network depth by \\( \alpha ^ N \\),  width by \\( \beta ^ N \\), and image size by \\( \gamma ^ N \\), where \\( \alpha, \beta, \gamma \\) are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient \\( \phi \\) to uniformly scales network width, depth, and resolution in a  principled way.
+**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use \\( 2^N \\) times more computational resources, then we can simply increase the network depth by \\( \alpha ^ N \\), width by \\( \beta ^ N \\), and image size by \\( \gamma ^ N \\), where \\( \alpha, \beta, \gamma \\) are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient \\( \phi \\) to uniformly scale network width, depth, and resolution in a principled way.
 
 The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image.
 
@@ -77,7 +77,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/ensemble-adversarial.mdx

@@ -77,7 +77,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/ese-vovnet.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/fbnet.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/gloun-inception-v3.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/gloun-resnet.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/gloun-resnext.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/gloun-senet.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/gloun-seresnext.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/gloun-xception.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/hrnet.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/ig-resnext.mdx

@@ -77,7 +77,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/inception-resnet-v2.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/inception-v3.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/inception-v4.mdx

@@ -72,7 +72,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/legacy-se-resnet.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/legacy-se-resnext.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/legacy-senet.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/mixnet.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/mnasnet.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/mobilenet-v2.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/mobilenet-v3.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/nasnet.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/noisy-student.mdx

@@ -82,7 +82,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/pnasnet.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/regnetx.mdx

@@ -1,10 +1,10 @@
 # RegNetX
 
-**RegNetX** is a convolutional network design space with simple, regular models with parameters: depth \\( d \\), initial width \\( w\_{0} > 0 \\), and slope \\( w\_{a} > 0 \\), and generates a different block width \\( u\_{j} \\) for each block \\( j < d \\). The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure):
+**RegNetX** is a convolutional network design space with simple, regular models with parameters: depth \\( d \\), initial width \\( w_{0} > 0 \\), and slope \\( w_{a} > 0 \\), and generates a different block width \\( u_{j} \\) for each block \\( j < d \\). The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure):
 
-\\( \\) u\_{j} = w\_{0} + w\_{a}\cdot{j} \\( \\)
+\\( u_{j} = w_{0} + w_{a}\cdot{j} \\)
 
-For **RegNetX** we have additional restrictions: we set \\( b = 1 \\) (the bottleneck ratio), \\( 12 \leq d \leq 28 \\), and \\( w\_{m} \geq 2 \\) (the width multiplier).
+For **RegNetX** we have additional restrictions: we set \\( b = 1 \\) (the bottleneck ratio), \\( 12 \leq d \leq 28 \\), and \\( w_{m} \geq 2 \\) (the width multiplier).
 
 ## How do I use this model on an image?
 
@@ -77,7 +77,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/regnety.mdx

@@ -1,10 +1,10 @@
 # RegNetY
 
-**RegNetY** is a convolutional network design space with simple, regular models with parameters: depth \\( d \\), initial width \\( w\_{0} > 0 \\), and slope \\( w\_{a} > 0 \\), and generates a different block width \\( u\_{j} \\) for each block \\( j < d \\). The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure):
+**RegNetY** is a convolutional network design space with simple, regular models with parameters: depth \\( d \\), initial width \\( w_{0} > 0 \\), and slope \\( w_{a} > 0 \\), and generates a different block width \\( u_{j} \\) for each block \\( j < d \\). The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure):
 
-\\( \\) u\_{j} = w\_{0} + w\_{a}\cdot{j} \\( \\)
+\\( u_{j} = w_{0} + w_{a}\cdot{j} \\)
 
-For **RegNetX** authors have additional restrictions: we set \\( b = 1 \\) (the bottleneck ratio), \\( 12 \leq d \leq 28 \\), and \\( w\_{m} \geq 2 \\) (the width multiplier).
+For **RegNetX** authors have additional restrictions: we set \\( b = 1 \\) (the bottleneck ratio), \\( 12 \leq d \leq 28 \\), and \\( w_{m} \geq 2 \\) (the width multiplier).
 
 For **RegNetY** authors make one change, which is to include [Squeeze-and-Excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block).
 
@@ -79,7 +79,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/res2net.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/res2next.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/resnest.mdx

@@ -1,6 +1,6 @@
 # ResNeSt
 
-A **ResNeSt** is a variant on a [ResNet](https://paperswithcode.com/method/resnet), which instead stacks [Split-Attention blocks](https://paperswithcode.com/method/split-attention). The cardinal group representations are then concatenated along the channel dimension: \\( V = \text{Concat} \\){\\( V^{1},V^{2},\cdots{V}^{K} \\)}. As in standard residual blocks, the final output \\( Y \\) of otheur Split-Attention block is produced using a shortcut connection: \\( Y=V+X \\), if the input and output feature-map share the same shape.  For blocks with a stride, an appropriate transformation \\( \mathcal{T} \\) is applied to the shortcut connection to align the output shapes:  \\( Y=V+\mathcal{T}(X) \\). For example, \\( \mathcal{T} \\) can be strided convolution or combined convolution-with-pooling.
+A **ResNeSt** is a variant on a [ResNet](https://paperswithcode.com/method/resnet), which instead stacks [Split-Attention blocks](https://paperswithcode.com/method/split-attention). The cardinal group representations are then concatenated along the channel dimension: \\( V = \text{Concat} \{ V^{1},V^{2},\cdots,{V}^{K} \} \\). As in standard residual blocks, the final output \\( Y \\) of otheur Split-Attention block is produced using a shortcut connection: \\( Y=V+X \\), if the input and output feature-map share the same shape.  For blocks with a stride, an appropriate transformation \\( \mathcal{T} \\) is applied to the shortcut connection to align the output shapes:  \\( Y=V+\mathcal{T}(X) \\). For example, \\( \mathcal{T} \\) can be strided convolution or combined convolution-with-pooling.
 
 ## How do I use this model on an image?
 
@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/resnet-d.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/resnet.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/resnext.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/rexnet.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/se-resnet.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/selecsls.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation