Selling 1 VIP wristband for SAT/SUN. The VIP experience is totally worth it. EDC website has no more VIP Saturday tickets for sale, only Sunday for $500+ for one. Willing to let them go for $800 OBO. I'm a local so can meet anywhere.

All items are + shipping and OBO, and I do mean OBO. I will not be offended by a low offer, you just might not get it if someone offers more.
Shipping only to lower 48 and usually for <$20. Local pickup near 53224.
All items are used with no known issues unless noted. I do my best to test out devices, but there are no guarantees. Any issues with devices after purchase, I'll do my best to make it right, but again no guarantees.
Image Link: https://imgur.com/a/exOHStH
Mini Desktops:
$150 - 1x HP Z2 Mini G3 Workstation: Xeon E3-1225v6 4/4 core CPU, 16GB 2400MHz DDR4 RAM, 512GB Samsung M.2 (3200/1700 MBps), NVidia Quadro M620 2GB dGPU, Fresh Windows 10 activated. Super small workstation, 4x DP outputs, 4x USB SS ports, and 2x USB C ports. Incredibly easy to clean and swap internals as well with one switch on the back. Comes with OEM 200w PSU.
$70 - 1x HP Elite Slice: Win 10 Pro, i5-6500T 4/4 core CPU, 8GB DDR4 RAM (1 DIMM), 256GB SSD, OEM 60w PSU included.
HDDs (prices are per drive):
$90 - 6x 10TB 3.5" SATA: Western Digital WD101PURZ, 7200RPM, PoH ~41,600 $80 - 4x 10TB 3.5" SATA: Western Digital WD100PURZ, 5400RPM, PoH ~56,200 $30 - 4x 4TB 3.5" SATA: Seagate 1H4168-505, 5900RPM, 64MB Cache $60 - 1x 8TB 3.5" SATA: Western Digital WD80PUZX, 5400RPM, PoH ~16,000, 128MB Cache $90 - 7x 5TB 2.5" SATA: Seagate Barracuda 2AN170-500, 5400RPM, PoH ~15,800
RAM (prices are for all DIMMs):
ECC: $16 - 4x 4GB DDR3 ECC: PC3-10600R-9-10-J0, HP 500203-061 $24 - 6x 4GB DDR3 ECC: PC3-8500, 'LifeTime Memory Prodcuts' $64 - 4x 16GB DDR4 ECC: PC4-2400T-RC1-11-MC0, Cisco UCS-MR-1X161RV-A
Non-ECC: $10 - 2x 4GB DDR3: PC3-10666, G.Skill Ripsaws, CL9-9-9-24 1.5v $10 - 2x 4GB DDR3: PC3-10666, G.Skill Ripsaws X, CL9-9-9-24 1.5v $16 - 2x 8GB DDR3: 1600MHz, Corsair Vengeance, CL9-9-9-24 (1 DIMM) CL10-10-10-27 (1 DIMM) 1.5v
Label Printer:
$140 Panduit MP300 Kit: Brand new label printer. Flagship model capable of printing labels up to 1.5", but unfortunately did not fit my specific needs. Comes with an almost-full cartridge of cable labels.

Recent DRS share count additions via reddit posts: * ~1% of DRS holders have posted their share count on reddit in the past 6 months * ~12% of DRS holders have posted their share count on reddit at least once

Date range	DRS share count additions	Number of reddit posts
4/6/2024 to 4/12/2024	32,982	66
4/13/2024 to 4/19/2024	16,528	62
4/20/2024 to 4/26/2024	20,022	64
4/27/2024 to 5/3/2024	22,258	54
5/4/2024 to 5/9/2024	8,734	43
5/10/2024 to 5/16/2024	94,940	71

5/16/2024 Top 69 DRS holders from recent reddit post or comment activity: * From a sample size of 1,335 holders (3,800,710 DRS'd shares) * Top 69 holders are 2,598,112 of the 3,800,710 DRS'd shares * There were 194,270 record holders on 3/20/2024 (Form 10-K/A)

Position	Number of shares	Pos	# of shares	Pos	# of shares
1	1,390,972	24	19,151	47	9,583
2	73,000	25	17,012	48	9,544
3	70,800	26	16,109	49	9,500
4	58,769	27	16,074	50	9,472
5	47,915	28	16,004	51	9,025
6	45,063	29	14,404	52	8,311
7	41,003	30	14,220	53	8,204
8	40,121	31	14,000	54	8,022
9	34,401	32	13,575	55	8,000
10	31,150	33	13,000	56	8,000
11	30,000	34	11,900	57	7,700
12	30,000	35	11,064	58	7,600
13	29,369	36	11,000	59	7,536
14	25,086	37	10,900	60	7,000
15	22,400	38	10,500	61	6,695
16	21,568	39	10,104	62	6,695
17	21,500	40	10,102	63	6,586
18	20,900	41	10,101	64	6,433
19	20,267	42	10,085	65	6,347
20	20,180	43	10,005	66	6,243
21	20,163	44	10,000	67	6,026
22	20,000	45	10,000	68	6,000
23	20,000	46	9,653	69	6,000

4/21/2023 List of stockholders top 25 holders (Credit: drs gme org contributors):

Position	Who	Number of shares
1	Cede & Co	228,451,023.9
2		1,200,009
3		203,868.0026
4		160,000
5		146,896
6		128,000
7		109,812
8		108,000
9		108,000
10	GameStop Omnibus Account	78,012
11		72,000
12		66,000
13		65,329
14		61,536
15		60,140
16		60,000
17		60,000
18		58,000
19		57,884
20		57,065.16734
21		55,840
22		55,479.32866
23		53,000.09312
24		51,600
25		50,810.5893

Insider share count via SEC filings (as of 5/16/2024):

Who	Number of shares	Last filing
Ryan Cohen	36,847,842	6/9/2023
Alain Attal	562,464	9/8/2023
Lawrence Cheng	73,860	4/8/2024
Mark Robinson	50,623	5/6/2024
James Grube	23,864	6/15/2023
Xu Yang	19,860	6/15/2023
Daniel Moore	13,606	4/25/2024

Recent former insiders share count via 4/19/2024 proxy statement:

Who	Number of shares	Last filing
Patel Nir Vinay - nearly there 4/4/2024	242,995	4/19/2024
Diana Saadeh-Jajeh - 8/11/2023	25,438	4/19/2024
Matthew Furlong - traversed 6/7/2023	51,800	4/19/2024

Number of Computershare accounts (Credit: drs gme org and many other contributors):

Date	Number of Computershare accounts	Change
8/2021	5,450	--
9/2021	37,750	+32,300
10/2021	73,950	+36,200
11/2021	89,450	+15,500
12/2021	108,950	+19,500
1/2022	120,450	+11,500
2/2022	127,450	+7,000
3/2022	137,450	+10,000
4/2022	150,450	+13,000
5/2022	159,750	+9,300
6/2022	165,450	+5,700
7/2022	175,450	+10,000 (odd increases near splividend?)
8/2022	193,250	+17,800 (odd increases near splividend?)
9/2022	196,800	+3,550
10/2022	199,900	+3,100
11/2022	203,200	+3,300
12/2022	205,800	+2,600
1/2023	208,000	+2,200
2/2023	208,600	+600
3/2023	211,000	+2,400
4/2023	211,900	+900
5/8/2023	213,4xx	+1,500
8/2023 or 9/2023	213,500	+100
10/24/2023	216,3xx	+2,800
11/21/2023	217,xxx	+700
12/10/2023	218,xxx	+1,000
1/4/2024	218,4xx	+400
4/18/2024	220,xxx	+1,600

Number of record holders:

Date	Source	Number of record holders
3/6/2013	Form 10-K	1,535
3/20/2014	Form 10-K	1,549
3/19/2015	Form 10-K	1,514
3/17/2016	Form 10-K	1,448
3/16/2017	Form 10-K	1,429
3/22/2018	Form 10-K	1,397
3/21/2019	Form 10-K	1,383
3/20/2020	Form 10-K	1,425
3/17/2021	Form 10-K	1,683
3/11/2022	Form 10-K	125,543
4/8/2022	List of stockholders	139,602
3/22/2023	Form 10-K	197,058
4/21/2023	List of stockholders	197,954
3/20/2024	Form 10-K/A	194,270
4/19/2024	List of stockholders	Available May/June 2024

From 4/21/2023's List of stockholders data (Credit: drs gme org contributors): * 1,717 accounts with less than 1 share * 6,268 accounts with 1 or less shares * 27,552 accounts with 5 or less shares
DRS count timeline (to 3/20/2024):

Date	DRS count	Source
10/30/2021	20,800,000	Form 10-Q
1/29/2022	35,600,000	Form 10-K
4/8/2022	47,016,408	List of stockholders
4/30/2022	50,800,000	Form 10-Q
7/30/2022	71,300,000	Form 10-Q
8/15/2022	72,500,012	Whale #1 DRS'd 1,200,012 shares
10/29/2022	71,800,000	Form 10-Q
3/22/2023	76,000,000	Form 10-K
4/21/2023	76,265,982	List of stockholders
6/1/2023	76,600,000	Form 10-Q
6/20/2023	75,329,434	Mainstar rugpull -1,270,566 shares
8/31/2023	75,400,000	Form 10-Q
11/30/2023	75,400,000	Form 10-Q
3/20/2024	75,300,000	Form 10-K
4/19/2024	Available May/June 2024	List of stockholders

This year's List of stockholders DRS count should be interesting. Just jotting down to circle back in May/June:

Year	Stock price was around	Time between SEC filing and List of stockholders	DRS count change
2022	$25	1/29/22 to 4/8/22 (10 weeks)	47,016,408 - 35,600,000 = +11,416,408
2023	$17	3/22/23 to 4/21/23 (4 weeks)	76,265,982 - 76,000,000 = +265,982
2024	$11	3/20/24 to 4/19/24 (4 weeks)	?? - 75,300,000 = ??

GME Volume last 2 years:

Year	Volume
2022	2,825,293,700
2023	1,167,878,465

20 lowest volume days (as of 5/16/2024):

Rank	Date	Volume	Closing price
1	8/3/2023	1,318,700	$20.93
2	8/8/2023	1,431,300	$20.74
3	8/10/2023	1,432,400	$20.19
4	8/7/2023	1,441,300	$21.07
5	7/26/2023	1,489,200	$22.70
6	8/11/2023	1,500,200	$20.19
7	5/9/2023	1,522,700	$20.24
8	5/11/2023	1,652,500	$20.88
9	1/30/2024	1,652,600	$14.55
10	7/25/2023	1,672,000	$22.85
11	9/27/2023	1,681,300	$17.15
12	11/8/2023	1,705,600	$13.28
13	2/21/2024	1,731,300	$13.41
14	7/12/2023	1,742,200	$23.44
15	4/13/2023	1,803,900	$22.55
16	7/3/2023	1,806,600	$24.91
17	2/22/2024	1,817,800	$13.36
18	10/13/2023	1,820,565	$14.90
19	2/8/2024	1,838,300	$14.35
20	8/30/2023	1,884,500	$18.37

As Reddit doesn't allow posts to exceed 40,000 characters, Eastwood's edition had to be split into two parts because his whole career cannot be ignored. The first part was posted yesterday.

Million Dollar Baby (2004)¨

"Beyond his silence, there is a past. Beyond her dreams, there is a feeling. Beyond hope, there is a memory. Beyond their journey, there is a love."
His 25th film. Based on stories from the 2000 collection Rope Burns: Stories from the Corner by F.X. Toole, it stars Eastwood, Hilary Swank and Morgan Freeman. The film follows Margaret "Maggie" Fitzgerald, an underdog amateur boxer who is helped by an underappreciated boxing trainer to achieve her dream of becoming a professional.
Paul Haggis wrote the script on spec, and it took four years to sell it. The film was stuck in development hell for years before it was shot. Several studios rejected the project even when Eastwood signed on as actor and director. Even Warner Bros., Eastwood's longtime home base, would not agree to a $30 million budget. Eastwood persuaded Lakeshore Entertainment's Tom Rosenberg to put up half the budget (as well as handle foreign distribution), with Warner Bros. contributing the rest.
The film had an incredible run in limited release, breaking many records for Eastwood's career. It eventually earned a fantastic $216 million worldwide, becoming his highest grossing film ever. It received critical acclaim, and it was named as one of his greatest films. It won four Oscars: Best Picture, Best Director, Best Actress (for Swank), and Best Supporting Actor (for Freeman). Eastwood became one of the very few directors to make two films to win both Best Picture and Best Director.

• Budget: $30,000,000.
  
• Domestic gross: $100,492,203. ($166.8 million adjusted)
  
• Worldwide gross: $216,763,646.
  

Flags of Our Fathers (2006)

"A single shot can end the war."
His 26th film. Based on the book written by James Bradley and Ron Powers, it stars Ryan Phillippe, Jesse Bradford, Adam Beach, John Benjamin Hickey, John Slattery, Paul Walker, Jamie Bell, Barry Pepper, Robert Patrick and Neal McDonough. The film follows the 1945 Battle of Iwo Jima, the five Marines and one Navy corpsman who were involved in raising the flag on Iwo Jima, and the after effects of that event on their lives.
The film received positive reviews, but it bombed at the box office with just $65 million against its huge $90 million budget.

• Budget: $90,000,000.
  
• Domestic gross: $33,602,376. ($52.2 million adjusted)
  
• Worldwide gross: $65,900,249.
  

Letters from Iwo Jima (2006)

"The completion of the Iwo Jima saga."
His 27th film. Based on Picture Letters from Commander in Chief by Tadamichi Kuribayashi, it stars Ken Watanabe, Kazunari Ninomiya, Tsuyoshi Ihara, Ryō Kase and Shidō Nakamura. It's a companion film to Flags of Our Fathers, and portrays the Battle of Iwo Jima from the perspective of the Japanese soldiers.
In the process of reading about the Japanese perspective of the war for Flags of Our Fathers, in particular General Tadamichi Kuribayashi, Eastwood decided to film a companion piece with this film, which was shot entirely in Japanese. The film was shot back-to-back, starting filming just one month after Flags of Our Fathers wrapped filming.
Despite being seen as the least accessible of both films, this film was much more successful at the box office than the previous film (including a colossal $42 million in Japan alone). It also received critical acclaim, particularly for how it handed the depiction of good and evil from both sides. It received 4 Oscar nominations, including Best Picture and Best Director.

• Budget: $19,000,000.
  
• Domestic gross: $13,756,082. ($21.3 million adjusted)
  
• Worldwide gross: $68,673,228.
  

Changeling (2008)

"To find her son, she did what no one else dared."
His 28th film. It stars Angelina Jolie and John Malkovich, and is based on real-life events, specifically the 1928 Wineville Chicken Coop murders in Mira Loma, California. It follows a woman united with a boy who she realizes is not her missing son. When she tries to demonstrate that to the police and city authorities, she is vilified as delusional, labeled as an unfit mother and confined to a psychiatric ward.
The film earned $113 million worldwide, barely breaking even at the box office. The film received mixed reviews, but Jolie received praise for her performance. She was nominated for the Oscar for Best Actress.

• Budget: $55,000,000.
  
• Domestic gross: $35,739,802. ($52 million adjusted)
  
• Worldwide gross: $113,398,237.
  

Gran Torino (2008)

"Ever come across somebody you shouldn't have messed with?"
His 29th film. It stars Eastwood, and follows Walt Kowalski, a recently widowed Korean War veteran alienated from his family and angry at the world, whose young neighbor, Thao Vang Lor, is pressured by his cousin into stealing Walt's prized Ford Torino for his initiation into a gang. Walt thwarts the theft and subsequently develops a relationship with the boy and his family.
The film received great reviews, as well as praise from the Hmong community. It ended up becoming a sleeper hit, and it earned $270 million worldwide, becoming his highest grossing film.

• Budget: $25,000,000.
  
• Domestic gross: $148,095,302. ($215.6 million adjusted)
  
• Worldwide gross: $269,958,228.
  

Invictus (2009)

"His people needed a leader. He gave them a champion."
His 30th film. It stars Morgan Freeman and Matt Damon. Following the aftermath of the apartheid, President Nelson Mandela decides to unite his people by supporting a rugby team in their bid to win the 1995 Rugby World Cup.
The film earned $122 million worldwide, barely breaking even. It received positive reviews, and Freeman and Damon received Oscar nominations for their performances.

• Budget: $50,000,000.
  
• Domestic gross: $37,491,364. ($54.7 million adjusted)
  
• Worldwide gross: $122,426,792.
  

Hereafter (2010)

"Touched by death. Changed by life."
His 31st film. It stars Matt Damon, Cécile de France, Bryce Dallas Howard, Lyndsey Marshal, Jay Mohr and Thierry Neuvic. An American with a special connection to the afterlife, a woman with a near-death experience and a young English boy, who lost his loved ones, cross paths in an effort to find closure in their lives.
Despite mixed reviews, it managed to earn $107 million, turning a small profit.

• Budget: $50,000,000.
  
• Domestic gross: $32,746,941. ($47 million adjusted)
  
• Worldwide gross: $106,956,330.
  

J. Edgar (2011)

"The most powerful man in the world."
His 32nd film. The film stars Leonardo DiCaprio, Armie Hammer, Naomi Watts, Josh Lucas, and Judi Dench, and follows the career of FBI director J. Edgar Hoover, focusing on Hoover's life from the 1919 Palmer Raids onward.
The film received mixed reviews; while DiCaprio received praise, the technical aspects of the film were criticized. It earned $84 million, making it a box office success, but far below what DiCaprio usually makes at the box office.

• Budget: $35,000,000.
  
• Domestic gross: $37,306,030. ($52 million adjusted)
  
• Worldwide gross: $84,920,539.
  

Jersey Boys (2014)

"Everybody remembers it how they need to."
His 33rd film. Base on the 2004 jukebox musical, it stars John Lloyd Young, Erich Bergen, Michael Lomenda, Vincent Piazza and Christopher Walken, and tells the story of the musical group The Four Seasons.
It received mixed reviews, with praise for the musical numbers but criticism for the narrative and runtime, and failed at the box office.

• Budget: $40,000,000.
  
• Domestic gross: $47,047,013. ($62.3 million adjusted)
  
• Worldwide gross: $67,647,013.
  

American Sniper (2014)

"The most lethal sniper in U.S. history."
His 34th film. It is based on the memoir by Chris Kyle, Scott McEwen and Jim DeFelice, and stars Bradley Cooper and Sienna Miller. The film follows the life of Kyle, who became the deadliest marksman in U.S. military history with 255 kills from four tours in the Iraq War, 160 of which were officially confirmed by the Department of Defense. While Kyle was celebrated for his military successes, his tours of duty took a heavy toll on his personal and family life.
In 2012, Cooper and Warner Bros. bought the rights to the memoir. Cooper wanted Chris Pratt to star as Kyle, but WB told him they would only greenlight the film if he stars in it. After Kyle's murder in 2013, Steven Spielberg signed to direct. Spielberg had read Kyle's book, though he desired to have a more psychological conflict present in the screenplay so an "enemy sniper" character could serve as the insurgent sharpshooter who was trying to track down and kill Kyle. Spielberg's ideas contributed to the development of a lengthy screenplay approaching 160 pages. Due to Warner Bros.' budget constraints, Spielberg felt he could not bring his vision of the story to the screen. So Eastwood was brought in to direct.
The film attained a solid, but not extraordinary response from critics. It also attracted some controversy over its portrayal of both the Iraq War and Kyle himself.
The box office though?
To say that the film had a fantastic run would be selling it short.
It opened on Christmas Day in 4 theaters, and it earned a huge $633,456 ($158,364 PTA). But the following weekend, it actually increased despite playing at the same amount of theaters, adding $676,909. That translated to a $169,227 PTA, becoming the highest second weekend PTA in history for a live-action film. And on its third weekend, it earned $579,518 ($144,879 PTA), becoming the first film to have three weekends above $100,000 PTA. In the 22 days it played in just 4 theaters, it earned $3,424,778.
On its first wide weekend, the film shook the industry by opening with a colossal $89 million. That was almost as much as the other 2014 blockbusters, and given that the film didn't have 3D pricing, it's very likely it sold far more tickets than them. It broke the January opening weekend record by twice as much, and the second biggest for an R-rated title. With insane word of mouth ("A+" on CinemaScore), this film had the legs. In less than one week, it became Eastwood's highest grossing film domestically. On its second weekend, it dropped just 28% and made $64 million, which was the biggest second weekend for an R-rated film (a record it still maintains) and crossed $200 million domestically. And by March, the film overtook The Hunger Games: Mockingjay – Part 1 ($334 million) as the highest grossing 2014 film in North America.
After an insane run in theaters, it closed with a gigantic $350 million domestically, which made it the second highest grossing R-rated film in North America. Overseas, it was also very strong, and it made a huge $547 million worldwide. It was easily Eastwood's highest grossing film, even adjusted for inflation. One of the greatest box office runs in recent memory. It received six Oscar nominations, including Best Picture, Best Adapted Screenplay, and Best Actor for Cooper, ultimately winning one for Best Sound Editing.
The biggest surprise of the 2010s? Perhaps. Cause let's face it, when 2014, did any of you had this as the top film of the year? Or even in the Top 20? Please.

• Budget: $59,000,000.
  
• Domestic gross: $350,159,020. ($463.7 million adjusted)
  
• Worldwide gross: $547,659,020.
  

Sully (2016)

"The untold story behind the miracle on the Hudson."
His 35th film. Based on the autobiography Highest Duty by Chesley "Sully" Sullenberger and Jeffrey Skiles, it stars Tom Hanks, Aaron Eckhart, Laura Linney, Anna Gunn, Autumn Reeser, Holt McCallany, and Jamey Sheridan. The film follows Sullenberger's 2009 emergency landing of US Airways Flight 1549 on the Hudson River, in which all 155 passengers and crew survived and the subsequent publicity and investigation.
The film received strong reviews, and earned over $240 million worldwide, becoming one of his highest grossing films.

• Budget: $60,000,000.
  
• Domestic gross: $125,070,033. ($163.3 million adjusted)
  
• Worldwide gross: $243,870,033.
  

The 15:17 to Paris (2018)

"The real heroes."
His 36th film. Based on the autobiography by Jeffrey E. Stern, Spencer Stone, Anthony Sadler, and Alek Skarlatos, it stars Stone, Sadler, and Skarlatos as themselves and follows the trio through life leading up to and including their stopping of the 2015 Thalys train attack.
Despite choosing Kyle Gallner, Jeremie Harris and Alexander Ludwig as the leads, Eastwood decided to cast the heroes to play themselves, which was met with confusion as they lacked acting experience. And that was reflected on the final film; it received negative reviews for its acting, and it bombed at the box office.

• Budget: $30,000,000.
  
• Domestic gross: $36,276,286. ($45.2 million adjusted)
  
• Worldwide gross: $57,176,286.
  

The Mule (2018)

"Nobody runs forever."
His 37th film. Based on the 2014 The New York Times article The Sinaloa Cartel's 90-Year-Old Drug Mule by Sam Dolnick, it stars Eastwood, Bradley Cooper, Laurence Fishburne, Michael Peña, Dianne Wiest, and Andy García. Due to financial issues, horticulturist Earl Stone becomes a courier for a drug cartel. Slowly, he grows closer to his estranged family, but his illegal activities threaten much more than his life.
It received good reviews (although some questioned its story and tone), and earned over $173 million worldwide.

• Budget: $50,000,000.
  
• Domestic gross: $103,804,407. ($129.6 million adjusted)
  
• Worldwide gross: $174,804,407.
  

Richard Jewell (2019)

"The world will know his name and the truth."
His 38th film. The film stars Paul Walter Hauser, Sam Rockwell, Kathy Bates, Jon Hamm, and Olivia Wilde. The film depicts the July 27 Centennial Olympic Park bombing and its aftermath, as security guard Richard Jewell finds a bomb during the 1996 Summer Olympics in Atlanta, Georgia, and alerts authorities to evacuate, only to later be wrongly accused of having placed the device himself.
The film received positive reviews, but several journalists criticized the critical portrayal of the reporter that first accused Jewell: Kathy Scruggs (specifically for trading sex for stories). The film marked another commercial failure for Eastwood.

• Budget: $45,000,000.
  
• Domestic gross: $22,345,542. ($27.4 million adjusted)
  
• Worldwide gross: $44,645,542.
  

Cry Macho (2021)

"A story of being lost and found."
His 39th film. Based on the novel by N. Richard Nash, it stars Eastwood and Dwight Yoakam. Set in 1979, it follows a former rodeo star hired to reunite a young boy in Mexico with his father in the United States.
Nash tried to get this film made all the way since 1970s, but no studio was willing to pick it up. He restructured his films as a novel, was successful and studios were now interested. There were a few candidates for the leading role; Robert Mitchum, Roy Scheider, Arnold Schwarzenegger and Eastwood himself. Arnie was willing to star in the film back in 2003, but put it on hold when he was elected Governor. He was set to star after leaving office, but the project was scrapped after his affair scandal was made known. In 2020, Eastwood signed to return.
The film received mixed reviews, particularly for its writing and acting. It was also a huge flop at the box office, and marked Eastwood's least attended film as leading man. David Zaslav criticized the studio's decision to finance the film. Warner executives allegedly said that although they knew the film was unlikely to turn a profit, they felt indebted to Eastwood for his decades-long relationship with the studio and his consistent ability to deliver films under budget and on time.

• Budget: $33,000,000.
  
• Domestic gross: $10,310,734. ($11.9 million adjusted)
  
• Worldwide gross: $16,510,734.
  

The Future

He recently wrapped post-production on his 40th film, Juror No. 2. It stars Nicholas Hoult, Toni Collette, Zoey Deutch, Leslie Bibb, Chris Messina, J. K. Simmons and Kiefer Sutherland, and follows a juror serving on a murder trial who realizes he may be at fault for the victim's death.

MOVIES (FROM HIGHEST GROSSING TO LEAST GROSSING)

No.	Movie	Year	Studio	Domestic Total	Overseas Total	Worldwide Total	Budget
1	American Sniper	2014	Warner Bros.	$350,159,020	$197,500,000	$547,659,020	$59M
2	Gran Torino	2008	Warner Bros.	$148,095,302	$121,862,926	$269,958,228	$25M
3	Sully	2016	Warner Bros.	$125,070,033	$118,800,000	$243,870,033	$60M
4	Million Dollar Baby	2004	Warner Bros.	$100,492,203	$116,271,443	$216,763,646	$30M
5	The Bridges of Madison County	1995	Warner Bros.	$71,516,617	$110,500,000	$182,016,617	$22M
6	The Mule	2018	Warner Bros.	$103,804,407	$71,000,000	$174,804,407	$50M
7	Unforgiven	1992	Warner Bros.	$101,167,799	$58,000,000	$159,167,799	$14.4M
8	Mystic River	2003	Warner Bros.	$90,135,191	$66,460,000	$156,595,191	$25M
9	Sudden Impact	1983	Warner Bros.	$67,642,693	$83,000,000	$150,642,693	$22M
10	A Perfect World	1993	Warner Bros.	$31,130,999	$104,000,000	$135,130,999	$30M
11	Space Cowboys	2000	Warner Bros.	$90,464,773	$38,419,359	$128,884,132	$60M
12	Invictus	2009	Warner Bros.	$37,491,364	$84,935,428	$122,426,792	$55M
13	Heartbreak Ridge	1986	Warner Bros.	$42,724,017	$78,975,983	$121,700,000	$15M
14	Changeling	2008	Universal	$35,739,802	$77,658,435	$113,398,237	$55M
15	Hereafter	2010	Warner Bros.	$32,746,941	$74,209,389	$106,956,330	$50M
16	Absolute Power	1997	Sony	$50,068,310	$42,700,000	$92,768,310	$50M
17	J. Edgar	2011	Warner Bros.	$37,306,030	$47,614,509	$84,920,539	$35M
18	Letters from Iwo Jima	2006	Warner Bros.	$13,756,082	$54,917,146	$68,673,228	$19M
19	Jersey Boys	2014	Warner Bros.	$47,047,013	$20,600,000	$67,647,013	$40M
20	Flags of Our Fathers	2006	Warner Bros.	$33,602,376	$32,297,873	$65,900,249	$90M
21	The 15:17 to Paris	2018	Warner Bros.	$36,276,286	$20,900,000	$57,176,286	$30M
22	Firefox	1982	Warner Bros.	$46,708,276	$0	$46,708,276	$21M
23	Richard Jewell	2019	Warner Bros.	$22,345,542	$22,300,000	$44,645,542	$45M
24	Pale Rider	1985	Warner Bros.	$41,410,568	$0	$41,410,568	$6.9M
25	The Gauntlet	1977	Warner Bros.	$35,400,000	$0	$35,400,000	$5.5M
26	The Outlaw Josey Wales	1976	Warner Bros.	$31,800,000	$0	$31,800,000	$3.7M
27	Blood Work	2002	Warner Bros.	$26,235,081	$5,559,637	$31,794,718	$50M
28	Midnight in the Garden of Good and Evil	1997	Warner Bros.	$25,105,255	$0	$25,105,255	$30M
29	Bronco Billy	1980	Warner Bros.	$24,265,659	$0	$24,265,659	$6.5M
30	The Rookie	1990	Warner Bros.	$21,633,874	$0	$21,633,874	$30M
31	True Crime	1999	Warner Bros.	$16,649,768	$0	$16,649,768	$55M
32	Cry Macho	2021	Warner Bros.	$10,310,734	$6,200,000	$16,510,734	$33M
33	High Plains Drifter	1973	Universal	$15,700,000	$0	$15,700,000	$5.5M
34	The Eiger Sanction	1975	Universal	$14,200,000	$0	$14,200,000	$9M
35	Play Misty for Me	1971	Universal	$10,600,000	$0	$10,600,000	$950K
36	Honkytonk Man	1982	Warner Bros.	$4,484,991	$0	$4,484,991	$2M
37	White Hunter Black Heart	1990	Warner Bros.	$2,319,124	$0	$2,319,124	$24M
38	Bird	1988	Warner Bros.	$2,181,286	$0	$2,181,286	$14M
39	Breezy	1973	Universal	$200,000	$17,753	$217,753	$750K

Across those 39 films, he has made $3,536,687,297 worldwide. That's $90,684,289 per film.

The Verdict

Insanely profitable.
Even the bombs do not taint this kind of reputation. Eastwood has made all these films under budget and never past its deadline. That's something that has to be treasured for studios, no wonder he's been staying with Warner Bros. since 1976. His ability to get films ready in short notice is impressive; Richard Jewell started filming in June and it was on theaters in December. One of the most impressive actors who transitioned into directors. You can tell that Sergio Leone and Don Siegel taught him well.
Now of course, his method of directing can also have its setbacks: he's often known for not asking for multiple takes and he skips rehearsals. So that means the performances of his actors aren't always the best they could've done. Which is why, despite making some masterpieces or fantastic films, he's also made a few films with weak technical aspects: poor lighting (J. Edgar), questionable logic (Cry Macho), and some bad acting (Gran Torino and The 15:17 to Paris). At the same time, it's clear he can also get extraordinary performances through these methods; Gene Hackman, Sean Penn, Tim Robbins, Hilary Swank and Morgan Freeman won Oscars for starring in his films.
He also proved old age doesn't prevent you from continuing to work. He's turning 94 in a few weeks, and he's still directing films. Manoel de Oliveira directed films until he was 104, so perhaps we still have a few more years with Eastwood behind the camera.
P.S. Ever since I started this series, there's been suggestions that I should do "Actors at the Box Office" multiple times. While the idea is intriguing, that doesn't seem feasible for me. I'd have to categorize whether the actor is leading, supporting, original IP, adaptation, remakes, etc. Besides, with the continuing decline of star power, it's tough to decide what actor is truly moving the needle at the box office. That's why I'm making solely "Directors at the Box Office", because the director is responsible for the production. If the film succeeds, the director will get credit. And if the film flops, the director will be blamed. So this is the closest you'll get to "Actors at the Box Office".
Hope you liked this edition. You can find this and more in the wiki for this section.
The next director will be Robert Zemeckis. One of the biggest falls from grace.
I asked you to choose who else should be in the run and the comment with the most upvotes would be chosen. It had to be a controversial filmmaker. Well, we'll later talk about... Zack Snyder. Oh, BoxOffice chose fuego 🔥
This is the schedule for the following four:

Week	Director	Reasoning
May 20-26	Robert Zemeckis	Can we get old Zemeckis back?
May 27-June 2	Richard Donner	An influential figure of the 70s and 80s.
June 3-9	Ang Lee	What happened to Lee?
June 10-16	Zack Snyder	RIP Inbox.

Who should be next after Snyder? That's up to you.

More accounts in my spreadsheet
Old references(8 years selling history) : 1 2 3 4 5 6 7

• For any additional questions - dm or Discord: structur;
  
• No Paypal. Price can be discussed;
  
• Digital Steam Gift Cards;
  3 steps to purchase an account with steam gift:
  
  • You buy steam gift using your Paypal or credit/debit card.
  • You write me code from gift card.
  • I give you transfer code and password.
• Bitcoin/Etherium/USDT/USDC;
  3 steps to purchase an account with crypto:
  
  • Registration and verification on any platform.
  • Deposit and purchase a cryptocurrency USDT/USDC/BTC/ETH.
  • Transfer money to the specified address.
    US residents can buy cryptocurrency in the PayPal application in the finance section and send it to any crypto address
    
• • Yoomoney; Bank transfer* for CIS;
• CS2 / DOTA2 Skins;
  

SHOWCASE
Account 2 557,341,453 154SQ Castoria+Merlin+Waver+Muramasa+Gilgamesh+Ishtar(archer)+Space Ishtar+Summer Kama+Osacabe-hime+Xuanzang+Ganesha+Arjuna alter+FREE SSR
Account 3 862,067,424 272SQ/9T Castoria+Merlin+Tamamo caster+Muramasa+Summer Kiara+Jalter(avenger)+Odysseus+Osacabe-hime+Artemis+Gilgamesh+FREE SSR
Account 4 159,435,242 200SQ Castoria+Merlin+Gilgamesh+Fae Lancelot+Da Vinci rider+Da Vinci caster+Arjuna Alter+Dioscuri+Ozymandias+Odysseus+FREE SSR
Account 7 615,327,536 170SQ/6T Castoria+Merlin+Skadi+Waver+Gilgamesh+Jalter(avenger)+Saber OG+Vritra+Arjuna alter+FREE SSR
Account 8 547,488,804 468SQ Castoria+Merlin+Muramasa+Kama+Summer Kama+Saber OG+Altera+Ozymandias+Skadi+Napoleon+FREE SSR
Account 9 649,800,345 180SQ/17T Castoria+Waver+Muramasa+Gilgamesh+Ishtar(archer)+Space Ishtar+Summer Kiara+Summer Kama+Napoleon+FREE SSR
Account 11 762,053,510 72SQ Castoria+Merlin+Waver+Muramasa+Space Ishtar+Hokusai+Da Vinci rider+Da Vinci Caster+Taigong Wang+FREE SSR
Account 12 584,496,101 180SQ/9T Castoria+Merlin+Gilgamesh+Ishtar(archer)+Ereshkigal+Nero Summer+Arjuna Alter+Enkidu+FREE SSR
Account 13 404,580,102 279SQ/16T Castoria+Merlin+Muramasa+Hokusai+Summer Kama+Vritra+Odysseus+FREE SSR
Account 14 388,367,536 56SQ Merlin+Waver+Musashi saber+Gilgamesh+Raikou+Koyanskaya light+Koyanskaya dark+Odysseus+FREE SSR
Account 15 755,118,723 139SQ Castoria+Merlin+Waver+Ibuki-douji+Koyanskaya dark+Achilles+Odysseus+Nemo+FREE SSR
Account 17 978,190,425 449SQ/31T Castoria+Waver+Musashi saber+Musashi summer+Achilles+Karna+FREE SSR
Account 18 005,711,424 137SQ/9T Castoria+Merlin+Waver+Gilgamesh+Jalter(avenger)+Bradamante(np2)+Arjuna alter+FREE SSR
Account 20 214,822,666 107SQ/10T Castoria+Merlin+Muramasa+Anastasia+Vlad III+Arjuna alter+FREE SSR
Account 21 856,912,090 105SQ/9T Castoria+Merlin+Muramasa+Nightingale+Artemis+Nemo+FREE SSR
Account 22 152,560,004 212SQ/12T Castoria+Merlin+Muramasa+Space Ishtar+Summer Kiara+FREE SSR
Account 23 419,078,938 34SQ Castoria+Merlin+Waver+Arjuna Alter+Drake+Achilles+FREE SSR
Account 24 779,260,491 97SQ/9T Castoria+Merlin+Waver+Skadi+Bradamante+FREE SSR
Account25 189,523,531 181SQ Castoria+Waver+Muramasa+Fae Lancelot+Da Vinci Rider+Da Vinci Caster+Summer Kiara+Kiara OG+Space Ishtar+Summer Kama+Anastasia+Xian Yu+Shuten-Douji+FREE SSR
Account26 980,749,674 429SQ Castoria+Muramasa+Nero bride+Da Vinci Caster+Da Vinci Rider+Summer Kiara+Kiara OG+Space Ishtar+Summer Kama+FREE SSR
Account27 646,307,211 475SQ/18T Castoria+Waver+Muramasa+Fae Lancelot+Da Vinci Rider+Space Ishtar+Summer Kama+Summer Kiara+Kiara OG+FREE SSR
Account28 966,605,866 638SQ Castoria+Waver+Muramasa+Fae Lancelot+Da Vinci Rider+Space Ishtar+Summer Kama+Summer Kiara+Kiara OG+FREE SSR
Account29 599,663,333 581SQ/8T Castoria+Waver+Muramasa+Sakamoto Ryoma+Space Ishtar+Summer Kama+Xuanzang+FREE SSR
Account30 807,991,176 455SQ Castoria+Waver+Muramasa(np2)+Space Ishtar+Summer Kama+Vritra+Ganesha+FREE SSR
Account31 103,027,533 527SQ/18T Castoria+Muramasa+Hokusai+Summer Musashi+Summer Kama+Napoleon+Nero Bride+FREE SSR
Account32 899,993,043 618SQ/18T Castoria+Muramasa+Summer Kama+Space Ishtar+Kiara OG+FREE SSR
Account33 446,620,259 637SQ/18T Castoria+Waver+Muramasa+Space Ishtar+Summer Kama+FREE SSR
Account34 821,520,647 508SQ/26T Castoria+Muramasa+Summer Kama+Bradamante+Ozymandias+FREE SSR
Account35 066,541,942 648SQ/18T Castoria+Muramasa+Space Ishtar+Summer Kama+FREE SSR
Account36 848,971,029 645SQ/18T Castoria+Muramasa+Summer Kama+FREE SSR
Account37 552,886,579 526SQ/36T Castoria+Waver+Summer Kama+FREE SSR

I just did some math to calculate how much crystals so i theoretically earn in swgoh per year an the number is mindblowing. Marh is not 100% accurate but generally realistic.
I am in Kyber 2 and hitting every day fleet Arena rank 1-3:
Assumption i win 50% of my GAC: 9 fights in total per month: (800+200)/2*9 = 4500 Additionaly 3 weekly rewards lets say 1500 in total End reward about 2000 GAC in total: 8500 per month, 102k per year
TB currently 37 stars twice per month: about 2000 per month = 24k per year
Daylies: 240 arena + 300 fleet (always between rank 1-3) + 120 for daylie activities = 660 * 365 = 240k
Total: GAC + TB + Daylie = 102k + 24k + 240k = 366k!
But why i always have like between 5-10k 😂😂

Player K/D’s:

1. Cellium (FaZe) 1,990/1,600 1.24 K/D
   
2. Scrap (Ultra) 1,970/1,674 1.18 K/D
   
3. Simp (FaZe) 2,254/1,950 1.16 K/D
   
4. HyDra (Subliners) 2,076/1,840 1.13 K/D
   
5. Attach (Legion) 1,694/1,532 1.11 K/D
   
6. Pred (OpTic) 2,278/2,062 1.10 K/D
   
7. Skyz (Subliners) 1,719/1,565 1.10 K/D
   
8. Drazah (FaZe) 2,066/1,912 1.08 K/D
   
9. Gwinn (Ravens) 1,841/1,705 1.08 K/D
   
10. CleanX (Ultra) 1,858/1,757 1.06 K/D
    
11. Nero (Legion) 1,727/1,636 1.06 K/D
    
12. aBeZy (FaZe) 2,072/1,965 1.05 K/D
    
13. Dashy (OpTic) 2,025/1,931 1.05 K/D
    
14. Gio (Legion) 1,093/1,044 1.05 K/D
    
15. Beans (Breach) 385/369 1.04 K/D
    
16. iLLeY (Surge) 761/731 1.04 K/D
    
17. Shotzzy (OpTic) 2,247/2,181 1.03 K/D
    
18. Sib (Subliners) 1,808/1,763 1.03 K/D
    
19. Kenny (OpTic) 2,197/2,144 1.02 K/D
    
20. Ghosty (Thieves) 1,697/1,661 1.02 K/D
    
21. Flames (Guerrillas) 249/244 1.02 K/D
    
22. Insight (Ultra) 1,583/1,556 1.02 K/D
    
23. ReeaL (Ravens/Heretics) 707/696 1.02 K/D
    
24. Lyynnz (Røkkr) 1,876/1,848 1.02 K/D
    
25. Snoopy (Breach) 1,715/1,691 1.01 K/D
    
26. Lucky (Heretics) 1,483/1,468 1.01 K/D
    
27. SlasheR (Breach) 1,014/1,015 0.99 K/D
    
28. Diamondcon (Guerrillas) 1,742/1,757 0.99 K/D
    
29. Priestahh (Breach) 1,510/1,536 0.98 K/D
    
30. Gunless (Røkkr) 443/453 0.98 K/D
    
31. Capsidal (Breach) 564/579 0.97 K/D
    
32. Fame (Guerrillas) 1,751/1,800 0.97 K/D
    
33. KiSMET (Subliners) 1,820/1,878 0.97 K/D
    
34. Estreal (Guerrillas) 1,765/1,824 0.97 K/D
    
35. Vikul (Heretics) 1,514/1,573 0.96 K/D
    
36. Envoy (Ultra) 1,696/1,766 0.96 K/D
    
37. Abuzah (Surge) 1,608/1,682 0.96 K/D
    
38. TJHaLy (Ravens) 1,522/1,593 0.96 K/D
    
39. MettalZ (Heretics) 1,529/1,602 0.95 K/D
    
40. Nastie (Thieves) 1,117/1,173 0.95 K/D
    
41. JoeDeceives (Thieves) 1,043/1,102 0.95 K/D
    
42. Standy (Legion/Røkkr) 1,047/1,108 0.94 K/D
    
43. Kremp (Thieves) 1,126/1,202 0.94 K/D
    
44. Johnny (Legion) 71/76 0.93 K/D
    
45. Assault (Guerrillas) 1,312/1,408 0.93 K/D
    
46. Arcitys (Surge) 1,037/1,113 0.93 K/D
    
47. Vivid (Røkkr) 1,384/1,505 0.92 K/D
    
48. Huke (Surge) 1,660/1,809 0.92 K/D
    
49. Pentagrxm (Breach) 490/536 0.91 K/D
    
50. Clayster (Ravens) 1,607/1,759 0.91 K/D
    
51. 04 (Surge) 505/556 0.91 K/D
    
52. JurNii (Heretics) 520/576 0.90 K/D
    
53. Accuracy (Røkkr) 1,695/1,895 0.89 K/D
    
54. Afro (Thieves) 1,058/1,191 0.89 K/D
    
55. Owakening (Røkkr) 1,198/1,350 0.89 K/D
    
56. FelonY (Ravens) 1,283/1,447 0.89 K/D
    
57. Purj (Legion) 1,211/1,372 0.88 K/D
    
58. Cammy (Thieves) 480/548 0.88 K/D
    
59. Breszy (Surge) 880/1,007 0.87 K/D
    
60. Seany (Breach) 140/163 0.86 K/D
    
61. Asim (Breach/Legion) 725/846 0.86 K/D
    
62. EriKBooM (Heretics) 478/576 0.83 K/D
    
63. GodRx (Ravens) 204/264 0.77 K/D
    

Team K/D’s:

1. Atlanta FaZe 8,382/7,427 1.13 K/D
   
2. NY Subliners 7,423/7,046 1.05 K/D
   
3. Toronto Ultra 7,107/6,753 1.05 K/D
   
4. OpTic Texas 8,747/8,318 1.05 K/D
   
5. LV Legion 6,542/6,504 1.01 K/D
   
6. Boston Breach 6,379/6,501 0.98 K/D
   
7. LA Guerrillas 6,819/7,033 0.97 K/D
   
8. Miami Heretics 6,044/6,274 0.96 K/D
   
9. Carolina Royal Ravens 6,644/6,985 0.95 K/D
   
10. LA Thieves 6,521/6,877 0.95 K/D
    
11. Seattle Surge 6,454/6,898 0.94 K/D
    
12. Minnesota Røkkr 7,061/7,552 0.93 K/D
    

I have been searching around my area, and after exhausting the usual suspects, I found a company that deals with electronics from all of the companies that are going out of business around my area. I just got this email, and it is a bit overwhelming. Does anything jump out to you? I have no problem with doing my own upgrades if needed and was planning on 3d printing a small rack for a patch panel and network switch
Refurbished Dell and Lenovo Tiny PC’s
Dell Optiplex 7060 Micro/Mini, 40+
i5-8500T, 16GB, 256GB m.2, Win11 Pro, WIFI $179/each, qty=10 $169
Upgrade to new M.2 512GB SSD $19
Upgrade to 32GB DDR4 $24
Lenovo Tiny M715Q 2-DP, 1-VGA
AMD Pro A6-8570E R5 8GB/128GB-SSD/Win10 Pro (no wifi) $66
Lenovo Tiny M900, 2-DP/1-VGA
i5-6500T/16GB/256GB-SSD M.2/Win10Pro $104
Add internal WIFI w/external antenna $9
Upgrade To 32GB Ram $31
Upgrade to new 2.5" 512GB SSD $19
Upgrade to new m.2 512GB SSD $19
Upgrade to new m.2 1TB SSD $49
Add Lenovo DVDRW w/bracket $19
Lenovo Tiny M720q, 10+ in stock
I5-8500T/16GB/New 256GB-SSD/Win11Pro $189
I7-8700T/16GB/New 256GB-SSD/Win11Pro $279
Add Internal WIFI w/antenna $10
Lenovo ThinkCentre M70Q Gen 2 Tiny, 20+ in stock
i5-10400T/16GB/New 256GB-SSD M.2/Win11 Pro $351/each
i5-10400T/32GB/New 1TB-SSD M.2/Win11 Pro $441/each
HP EliteDesk 800 G2 Desktop Mini (tiny)
i7-6700t 16gb, 256GB m.2 SSD Win10 Pro/WIFI $159
i7-6700t 32gb, 256GB m.2 SSD Win10 Pro/WIFI $179
Upgrade to new 512GB m.2 SSD $19
HP ProDesk 400 G4 Desktop Mini (tiny)
I5-8500t 16gb, 512GB SSD Win11 Pro/WIFI $139/each, qty=10 $134/each
Dell Optiplex 9020 USFF
i5-4570 8GB/128GB-SSD/DVDRW/Win10 Pro $64, qty=10+ $56
Dell Optiplex 5040 SFF
i5-6500 16GB/240GB-SSD/DVDROM/Win10 Pro $129
Dell Optiplex 3060/5060 SFF
i5-8500 16GB/256GB-SSD/DVDROM/Win11 Pro $229
Upgrade to NEW 512GB SSD $19
Upgrade to 16GB Memory $19
Refurbished Laptops, starting at $159
HP and Lenovo business models, i5-6th gen and higher, email for specs
HP EliteBook 850 G6,
I5-8265U 16GB/256GB-SSD/15.6” /Win11 Pro (ref) $199, 5 in stock
HP Elitebook 850 G7 i7-10510U/16GB/256GB-SSD/15.6” FHD/Win11 Pro $409, 2 in stock
HP Elitebook 850 G8 i7-11165G7/16GB/256GB-SSD/15.6” FHD/Win11 Pro $509, 1 in stock
NEW Lenovo Thinkcentre Tiny TIO 27”, 3-year lenovo warranty
NEW Lenovo ThinkCentre Tiny-in-one 27” HDMI/displayport 11JHRAR1US $338

Introduction:
Hi, everyone! I'm currently working on a medical image detection task based on chest X-ray images as my undergraduate final project, in which the dataset is composed of Pneumonia with 4273 images, Normal class with 1583 images, COVID-19 with 576 images, and I used ViT(replicate by myself from scratch) as the model architecture in this project. Above is the basic information of this project. But as the title said, I found that my model cannot learn anything from the dataset through these metrics (confusion matrix, precision, and so on), and then I tried my method from our forum to deal with this problem, some results are shown as the following:
Experiment 1: custom ViT, batch size: 64, learning rate:3e-4, epochs:30.

 32 Learning Rate: 0.000300 33 Epoch: 3 Train Loss: 0.5388, Train Acc: 0.8243, Test Loss: 1.5342, Test Acc: 0.6638, Learning Rate: 0.000300 34 Confusion Matrix: 35 COVID Normal Pneumonia 36 COVID 0 0 116 37 Normal 0 0 317 38 Pneumonia 0 0 855 39 Classification Report: 40 precision recall f1-score support 41 0 0.000000 0.000000 0.000000 116.00000 42 1 0.000000 0.000000 0.000000 317.00000 43 2 0.663820 1.000000 0.797947 855.00000 44 accuracy 0.663820 0.663820 0.663820 0.66382 45 macro avg 0.221273 0.333333 0.265982 1288.00000 46 weighted avg 0.440657 0.663820 0.529693 1288.00000 47 Accuracy Score: 0.6638 48 Learning Rate: 0.000300 49 Epoch: 4 Train Loss: 0.5498, Train Acc: 0.7905, Test Loss: 1.4384, Test Acc: 0.6638, Learning Rate: 0.000300 50 Confusion Matrix: 51 COVID Normal Pneumonia 52 COVID 0 0 116 53 Normal 0 0 317 54 Pneumonia 0 0 855 55 Classification Report: 56 precision recall f1-score support 57 0 0.000000 0.000000 0.000000 116.00000 58 1 0.000000 0.000000 0.000000 317.00000 59 2 0.663820 1.000000 0.797947 855.00000 60 accuracy 0.663820 0.663820 0.663820 0.66382 61 macro avg 0.221273 0.333333 0.265982 1288.00000 62 weighted avg 0.440657 0.663820 0.529693 1288.00000 63 Accuracy Score: 0.6638 64 Learning Rate: 0.000300 

this experiment is an initial experiment without any techniques to deal with the class imbalance issue, my model seems that learned nothing from the dataset. and then after a search, some solutions are: Learning rate, "Model complexity" "Class imbalance": before expt 1, I didn't note my dataset was imbalanced "Optimizer and loss function choice": I used the Adam and CrossEntropy as the same as the original paper, so I didn't pay attention to this part.
and then I continued to other experiments, firstly, I used the lr_scheduler.ReduceLROnPlateau to decrease my lr util 3e-8, the confusion matrix was the same as experiment 1.
Experiment 2: lr_rate:3e-3, others is the same as last experiment, but in this expt, two different kinds of stuff are:

1. I used WeightedRandomSampler in train_dataloader, after this I checked the distribution of the dataloader, the proportion of COVID(minority class)was almost equal to others.for idx, (_, label) in enumerate(train_dataloader): if idx > 5: break ncovid = (label == 0).sum() n = label.sum() print(ncovid/n)tensor(0.3226) tensor(0.3134) tensor(0.3077) tensor(0.3651) tensor(0.2133) tensor(0.3710)Output
2. In the stage of splitting the dataset, I used stratified sampling, which resulted in a good distribution. (no code in this after, I manually calculated the proportion of each class in train_dataset), in this case, i think this part may be okay. 3. but the result is not good as the last experiment

​

 99 COVID Normal Pneumonia 100 COVID 170 0 0 101 Normal 470 0 0 102 Pneumonia 1289 0 0 103 Classification Report: 104 precision recall f1-score support 105 0 0.088129 1.000000 0.161982 170.000000 106 1 0.000000 0.000000 0.000000 470.000000 107 2 0.000000 0.000000 0.000000 1289.000000 108 accuracy 0.088129 0.088129 0.088129 0.088129 109 macro avg 0.029376 0.333333 0.053994 1929.000000 110 weighted avg 0.007767 0.088129 0.014275 1929.000000 111 Accuracy Score: 0.0881 112 Learning Rate: 0.000300 113 Epoch 00008: reducing learning rate of group 0 to 3.0000e-05. 114 Epoch: 8 Train Loss: 0.8391, Train Acc: 0.3374, Test Loss: 1.5712, Test Acc: 0.0881, Learning Rate: 0.000030 115 Confusion Matrix: 116 COVID Normal Pneumonia 117 COVID 170 0 0 118 Normal 470 0 0 119 Pneumonia 1289 0 0 120 Classification Report: 121 precision recall f1-score support 122 0 0.088129 1.000000 0.161982 170.000000 123 1 0.000000 0.000000 0.000000 470.000000 124 2 0.000000 0.000000 0.000000 1289.000000 125 accuracy 0.088129 0.088129 0.088129 0.088129 126 macro avg 0.029376 0.333333 0.053994 1929.000000 127 weighted avg 0.007767 0.088129 0.014275 1929.000000 128 Accuracy Score: 0.0881 129 Learning Rate: 0.000030 

Code:

dataset = torchvision.datasets.ImageFolder(data_path, transform=common_transform) train_dataset, test_dataset = torch.utils.data.random_split(dataset,[0.7, 0.3]) # Deal with Imbalanced Dataset class_weights={} for root, sudir, files in os.walk(data_path): if files: class_weights[dataset.class_to_idx[os.path.basename(root)]] = len(files) print(class_weights) # output:{1: 1583, 2: 4273, 0: 576} sample_weights = [0] * len(train_dataset) for idx, (data, label) in enumerate(train_dataset): class_weight = class_weights[label] sample_weights[idx] = 1/class_weight sampler = WeightedRandomSampler(sample_weights, num_samples=len(sample_weights), replacement=True) train_dataloader = DataLoader(train_dataset, batch_size=batch_size, sampler=sampler) test_dataloader = DataLoader(test_dataset, batch_size=batch_size) 

Note:

1. class_to_idx = (COVID:0, NORMAL:1, PNEUMONIA:2}
2. After this expt, I thought the reason of the output was still is the same as the expt without weighted sampling may due to the order of sample_weights being incorrect, but eventually no error on that.
3. there are differences in this case, as you can see, the model always overrepresented a specific class in each epoch. I have no idea why this situation appeared after the two techniques I mentioned above were used in this experiment, and I checked the experiment simultaneously. And then I tried changing the lr using lr_scheduler as well, but nothing new. someone mentioned that combining the weightedSampling and specifying the weights for the loss function is a recommended method, so I continued to the next experiment.

experiment 4: Parameters:

config:{batch_size:64 Dataset:"dataset with weighted Sampling and class weight in loss function" epochs:50 image_size:224 initial_learning_rate:0.0003 mlp_dropout:0.1 model_name:"ViT" transformer_layers:12} 

Result:

 1 Epoch: 1 Train Loss: 0.9572, Train Acc: 0.3302, Test Loss: 1.4187, Test Acc: 0.0876, Learning Rate: 0.000300 2 Confusion Matrix: 3 COVID Normal Pneumonia 4 COVID 169 1 0 5 Normal 470 0 0 6 Pneumonia 1286 3 0 7 Classification Report: 8 precision recall f1-score support 9 0 0.087792 0.994118 0.161337 170.00000 10 1 0.000000 0.000000 0.000000 470.00000 11 2 0.000000 0.000000 0.000000 1289.00000 12 accuracy 0.087610 0.087610 0.087610 0.08761 13 macro avg 0.029264 0.331373 0.053779 1929.00000 14 weighted avg 0.007737 0.087610 0.014218 1929.00000 15 Accuracy Score: 0.0876 16 Learning Rate: 0.000300 17 Epoch: 2 Train Loss: 0.8471, Train Acc: 0.3275, Test Loss: 1.3836, Test Acc: 0.0881, Learning Rate: 0.000300 18 Confusion Matrix: 19 COVID Normal Pneumonia 20 COVID 170 0 0 21 Normal 470 0 0 22 Pneumonia 1289 0 0 23 Classification Report: 24 precision recall f1-score support 25 0 0.088129 1.000000 0.161982 170.000000 26 1 0.000000 0.000000 0.000000 470.000000 27 2 0.000000 0.000000 0.000000 1289.000000 28 accuracy 0.088129 0.088129 0.088129 0.088129 29 macro avg 0.029376 0.333333 0.053994 1929.000000 30 weighted avg 0.007767 0.088129 0.014275 1929.000000 31 Accuracy Score: 0.0881 32 Learning Rate: 0.000300 33 Epoch: 3 Train Loss: 0.8325, Train Acc: 0.3444, Test Loss: 1.4703, Test Acc: 0.0881, Learning Rate: 0.000300 34 Confusion Matrix: 35 COVID Normal Pneumonia 36 COVID 170 0 0 37 Normal 470 0 0 38 Pneumonia 1289 0 0 39 Classification Report: 40 precision recall f1-score support 41 0 0.088129 1.000000 0.161982 170.000000 42 1 0.000000 0.000000 0.000000 470.000000 ..............(same as above) 

Code:

# Calculating weights: inverse of class frequencies total_samples = 576 + 1583 + 4273 weights = torch.tensor([total_samples / 576, total_samples / 1583, total_samples / 4273], dtype=torch.float32) loss_function = torch.nn.CrossEntropyLoss(weights).to(device) optimizer = torch.optim.Adam(params=vit_model_own.parameters(), lr=learning_rate, betas=(0.9, 0.999), weight_decay=weight_decay_optimizer) scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,patience=5,threshold=0.1,verbose="True") 

Model Summary:

========================================================================================== Layer (type:depth-idx) Param # ========================================================================================== ViT 152,064 ├─Dropout: 1-1 -- ├─PatchEmbedding: 1-2 -- │ └─Conv2d: 2-1 590,592 │ └─Flatten: 2-2 -- ├─Sequential: 1-3 -- │ └─TransformerEncoderBlock: 2-3 -- │ │ └─MultiheadSelfAttentionBlock: 3-1 2,363,904 │ │ └─MLPBlock: 3-2 4,723,968 │ └─TransformerEncoderBlock: 2-4 -- │ │ └─MultiheadSelfAttentionBlock: 3-3 2,363,904 │ │ └─MLPBlock: 3-4 4,723,968 │ └─TransformerEncoderBlock: 2-5 -- │ │ └─MultiheadSelfAttentionBlock: 3-5 2,363,904 │ │ └─MLPBlock: 3-6 4,723,968 │ └─TransformerEncoderBlock: 2-6 -- │ │ └─MultiheadSelfAttentionBlock: 3-7 2,363,904 │ │ └─MLPBlock: 3-8 4,723,968 │ └─TransformerEncoderBlock: 2-7 -- │ │ └─MultiheadSelfAttentionBlock: 3-9 2,363,904 │ │ └─MLPBlock: 3-10 4,723,968 │ └─TransformerEncoderBlock: 2-8 -- │ │ └─MultiheadSelfAttentionBlock: 3-11 2,363,904 │ │ └─MLPBlock: 3-12 4,723,968 │ └─TransformerEncoderBlock: 2-9 -- │ │ └─MultiheadSelfAttentionBlock: 3-13 2,363,904 │ │ └─MLPBlock: 3-14 4,723,968 │ └─TransformerEncoderBlock: 2-10 -- │ │ └─MultiheadSelfAttentionBlock: 3-15 2,363,904 │ │ └─MLPBlock: 3-16 4,723,968 │ └─TransformerEncoderBlock: 2-11 -- │ │ └─MultiheadSelfAttentionBlock: 3-17 2,363,904 │ │ └─MLPBlock: 3-18 4,723,968 │ └─TransformerEncoderBlock: 2-12 -- │ │ └─MultiheadSelfAttentionBlock: 3-19 2,363,904 │ │ └─MLPBlock: 3-20 4,723,968 │ └─TransformerEncoderBlock: 2-13 -- │ │ └─MultiheadSelfAttentionBlock: 3-21 2,363,904 │ │ └─MLPBlock: 3-22 4,723,968 │ └─TransformerEncoderBlock: 2-14 -- │ │ └─MultiheadSelfAttentionBlock: 3-23 2,363,904 │ │ └─MLPBlock: 3-24 4,723,968 ├─Sequential: 1-4 -- │ └─LayerNorm: 2-15 1,536 │ └─Linear: 2-16 2,307 ========================================================================================== Total params: 85,800,963 Trainable params: 85,800,963 Non-trainable params: 0 ========================================================================================== 

Summary: After experiment 3, I tried to use TinyVGG to classify it as well, the result is the same. I felt I was cooked, and too many factors will lead to failure because I'm so new to this area, even though there are some other methods such as custom loss function train on F-score, data argument, oversamplling, and so on, the point is that I thought I may not understand what was going on even from the beginning, more and more experiment might make the thing more complex.
I'm not sure whether I provided all the necessary information, leave a comment please if not.
Looking forward to your help!!!